Post-Translational Modification ß-Hydroxybutyrylation Regulates Ustilaginoidea virens Virulence.

Lysine ß-hydroxybutyrylation (Kbhb) is an evolutionarily conserved and widespread post-translational modification that is associated with active gene transcription and cellular proliferation. However, its role in phytopathogenic fungi remains unknown. Here, we characterized Kbhb in the rice false smut fungus Ustilaginoidea virens. We identified 2204 Kbhb sites in 852 proteins, which are involved in diverse biological processes. The mitogen-activated protein kinase UvSlt2 is a Kbhb protein, and a strain harboring a point mutation at K72, the Kbhb site of this protein, had decreased UvSlt2 activity and reduced fungal virulence. Molecular dynamic simulations revealed that K72bhb increases the hydrophobic solvent-accessible surface area of UvSlt2, thereby affecting its binding to its substrates. The mutation of K298bhb in the septin UvCdc10 resulted in reduced virulence and altered the subcellular localization of this protein. Moreover, we confirmed that the NAD+-dependent histone deacetylases UvSirt2 and UvSirt5 are the major enzymes that remove Kbhb in U. virens. Collectively, our findings identify regulatory elements of the Kbhb pathway and reveal important roles for Kbhb in regulating protein localization and enzymatic activity. These findings provide insight into the regulation of virulence in phytopathogenic fungi via post-translational modifications.

Sirt5-mediated lysine desuccinylation regulates oxidative stress adaptation in Magnaporthe oryzae during host intracellular infection.

Plant pathogenic fungi elaborate numerous detoxification strategies to suppress host reactive oxygen species (ROS), but their coordination is not well-understood. Here, we show that Sirt5-mediated protein desuccinylation in Magnaporthe oryzae is central to host ROS detoxification. SIRT5 encodes a desuccinylase important for virulence via adaptation to host oxidative stress. Quantitative proteomics analysis identified a large number of succinylated proteins targeted by Sirt5, most of which were mitochondrial proteins involved in oxidative phosphorylation, TCA cycle, and fatty acid oxidation. Deletion of SIRT5 resulted in hypersuccinylation of detoxification-related enzymes, and significant reduction in NADPH : NADP+ and GSH : GSSG ratios, disrupting redox balance and impeding invasive growth. Sirt5 desuccinylated thioredoxin Trx2 and glutathione peroxidase Hyr1 to activate their enzyme activity, likely by affecting proper folding. Altogether, this work demonstrates the importance of Sirt5-mediated desuccinylation in controlling fungal process required for detoxifying host ROS during M. oryzae infection.

Plasma levels of hydrogen sulfide and homocysteine correlate with the efficacy of antidepressant agents and serve as potential diagnostic and therapeutic markers.

OBJECTIVE: Hydrogen sulfide (H2S) is associated with depressive-like behavior in rodents. We undertook cross-sectional and longitudinal analyses of plasma levels of H2S and its substrate homocysteine (Hcy) in depression and assessed the association of both parameters with psychopathology and cognitive function. METHODS: Forty-one patients suffering from depression (PSDs) and 48 healthy volunteers were recruited. PSDs were treated for 8 weeks. Analyzable data were collected from all participants for assessment of their psychopathology and cognitive function. Plasma was collected for determination of levels of H2S and Hcy, and data were correlated to determine their potential as plasma biomarkers. RESULTS: Cross-sectional analyses revealed PSDs to have a low plasma H2S level and high Hcy level. Longitudinal analyses revealed that 8 weeks of treatment reversed the changes in plasma levels of H2S and Hcy in PSDs. Plasma levels of H2S and Hcy were associated with psychopathology and cognitive function in depression. The area under the receiver operating characteristic curve (AUC) for a combination of plasma levels of H2S and Hcy and expression of the TNF gene (i.e., H2S-Hcy-TNF) was 0.848 for diagnosing depression and 0.977 for predicting the efficacy of antidepressant agents. CONCLUSION: Plasma levels of H2S and Hcy reflect changes in psychopathology and cognitive function in depression and H2S-Hcy-TNF has the potential to diagnose depression and predict the efficacy of antidepressant medications.

Machine Learning for Polymer Design to Enhance Pervaporation-Based Organic Recovery.

Pervaporation (PV) is an effective membrane separation process for organic dehydration, recovery, and upgrading. However, it is crucial to improve membrane materials beyond the current permeability-selectivity trade-off. In this research, we introduce machine learning (ML) models to identify high-potential polymers, greatly improving the efficiency and reducing cost compared to conventional trial-and-error approach. We utilized the largest PV data set to date and incorporated polymer fingerprints and features, including membrane structure, operating conditions, and solute properties. Dimensionality reduction, missing data treatment, seed randomness, and data leakage management were employed to ensure model robustness. The optimized LightGBM models achieved RMSE of 0.447 and 0.360 for separation factor and total flux, respectively (logarithmic scale). Screening approximately 1 million hypothetical polymers with ML models resulted in identifying polymers with a predicted permeation separation index >30 and synthetic accessibility score <3.7 for acetic acid extraction. This study demonstrates the promise of ML to accelerate tailored membrane designs.

Highly Selective Electrochemical Nitrate to Ammonia Conversion by Dispersed Ru in a Multielement Alloy Catalyst.

Electrochemical reduction of nitrate to ammonia (NH3) converts an environmental pollutant to a critical nutrient. However, current electrochemical nitrate reduction operations based on monometallic and bimetallic catalysts are limited in NH3 selectivity and catalyst stability, especially in acidic environments. Meanwhile, catalysts with dispersed active sites generally exhibit a higher atomic utilization and distinct activity. Herein, we report a multielement alloy nanoparticle catalyst with dispersed Ru (Ru-MEA) with other synergistic components (Cu, Pd, Pt). Density functional theory elucidated the synergy effect of Ru-MEA than Ru, where a better reactivity (NH3 partial current density of -50.8 mA cm-2) and high NH3 faradaic efficiency (93.5%) is achieved in industrially relevant acidic wastewater. In addition, the Ru-MEA catalyst showed good stability (e.g., 19.0% decay in FENH3 in three hours). This work provides a potential systematic and efficient catalyst discovery process that integrates a data-guided catalyst design and novel catalyst synthesis for a range of applications.

Machine Learning in Environmental Research: Common Pitfalls and Best Practices.

Machine learning (ML) is increasingly used in environmental research to process large data sets and decipher complex relationships between system variables. However, due to the lack of familiarity and methodological rigor, inadequate ML studies may lead to spurious conclusions. In this study, we synthesized literature analysis with our own experience and provided a tutorial-like compilation of common pitfalls along with best practice guidelines for environmental ML research. We identified more than 30 key items and provided evidence-based data analysis based on 148 highly cited research articles to exhibit the misconceptions of terminologies, proper sample size and feature size, data enrichment and feature selection, randomness assessment, data leakage management, data splitting, method selection and comparison, model optimization and evaluation, and model explainability and causality. By analyzing good examples on supervised learning and reference modeling paradigms, we hope to help researchers adopt more rigorous data preprocessing and model development standards for more accurate, robust, and practicable model uses in environmental research and applications.

Ca-Based Layered Double Hydroxides for Environmentally Sustainable Carbon Capture.

The process of carbon dioxide capture typically requires a large amount of energy for the separation of carbon dioxide from other gases, which has been a major barrier to the widespread deployment of carbon capture technologies. Innovation of carbon dioxide adsorbents is herein vital for the attainment of a sustainable carbon capture process. In this study, we investigated the electrified synthesis and rejuvenation of calcium-based layered double hydroxides (Ca-based LDHs) as solid adsorbents for CO2. We discovered that the particle morphology and phase purity of the LDHs, along with the presence of secondary phases, can be controlled by tuning the current density during electrodeposition on a porous carbon substrate. The change in phase composition during carbonation and calcination was investigated to unveil the effect of different intercalated anions on the surface basicity and thermal stability of Ca-based LDHs. By decoupling the adsorption of water and CO2, we showed that the adsorbed water largely promoted CO2 adsorption, most likely through a sequential dissolution and reaction pathway. A carbon capture capacity of 4.3 ± 0.5 mmol/g was measured at 30 °C and relative humidity of 40% using 10 vol % CO2 in nitrogen as the feed stream. After CO2 capture occurred, the thermal regeneration step was carried out by directly passing an electric current through the conductive carbon substrate, known as the Joule-heating effect. CO2 was found to start desorbing from the Ca-based LDHs at a temperature as low as 220 °C as opposed to the temperature above 700 °C required for calcium carbonate that forms as part of the Ca-looping capture process. Finally, we evaluated the cumulative energy demand and environmental impact of the LDH-based capture process using a life cycle assessment. We identified the most environmentally concerning step in the process and concluded that the postcombustion CO2 capture using LDH could be advantageous compared with existing technologies.

Methane Emissions from Municipal Wastewater Collection and Treatment Systems.

Municipal wastewater collection and treatment systems are critical infrastructures, and they are also identified as major sources of anthropogenic CH4 emissions that contribute to climate change. The actual CH4 emissions at the plant- or regional level vary greatly due to site-specific conditions as well as high seasonal and diurnal variations. Here, we conducted the first quantitative analysis of CH4 emissions from different types of sewers and water resource recovery facilities (WRRFs). We examined variations in CH4 emissions associated with methods applied in different monitoring campaigns, and identified main CH4 sources and sinks to facilitate carbon emission reduction efforts in the wastewater sector. We found plant-wide CH4 emissions vary by orders of magnitude, from 0.01 to 110 g CH4/m3 with high emissions associated with plants equipped with anaerobic digestion or stabilization ponds. Rising mains show higher dissolved CH4 concentrations than gravity sewers when transporting similar raw sewage under similar environmental conditions, but the latter dominates most collection systems around the world. Using the updated data sets, we estimated annual CH4 emission from the U.S. centralized, municipal wastewater treatment to be approximately 10.9 ± 7.0 MMT CO2-eq/year, which is about twice as the IPCC (2019) Tier 2 estimates (4.3-6.1 MMT CO2-eq/year). Given CH4 emission control will play a crucial role in achieving net zero carbon goals by the midcentury, more studies are needed to profile and mitigate CH4 emissions from the wastewater sector.

Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management.

The extraction of acetic acid and other carboxylic acids from water is an emerging separation need as they are increasingly produced from waste organics and CO2 during carbon valorization. However, the traditional experimental approach can be slow and expensive, and machine learning (ML) may provide new insights and guidance in membrane development for organic acid extraction. In this study, we collected extensive literature data and developed the first ML models for predicting separation factors between acetic acid and water in pervaporation with polymers' properties, membrane morphology, fabrication parameters, and operating conditions. Importantly, we assessed seed randomness and data leakage problems during model development, which have been overlooked in ML studies but will result in over-optimistic results and misinterpreted variable importance. With proper data leakage management, we established a robust model and achieved a root-mean-square error of 0.515 using the CatBoost regression model. In addition, the prediction model was interpreted to elucidate the variables' importance, where the mass ratio was the topmost significant variable in predicting separation factors. In addition, polymers' concentration and membranes' effective area contributed to information leakage. These results demonstrate ML models' advances in membrane design and fabrication and the importance of vigorous model validation.

Membrane Distillation-Crystallization for Sustainable Carbon Utilization and Storage.

Anthropogenic greenhouse gas emissions from power plants can be limited using postcombustion carbon dioxide capture by amine-based solvents. However, sustainable strategies for the simultaneous utilization and storage of carbon dioxide are limited. In this study, membrane distillation-crystallization is used to facilitate the controllable production of carbonate minerals directly from carbon dioxide-loaded amine solutions and waste materials such as fly ash residues and waste brines from desalination. To identify the most suitable conditions for carbon mineralization, we vary the membrane type, operating conditions, and system configuration. Feed solutions with 30 wt % monoethanolamine are loaded with 5-15% CO2 and heated to 40-50 °C before being dosed with 0.18 M Ca2+ and Mg2+. Membranes with lower surface energy and greater roughness are found to more rapidly promote mineralization due to up to 20% greater vapor flux. Lower operating temperature improves membrane wetting tolerance by 96.2% but simultaneously reduces crystal growth rate by 48.3%. Sweeping gas membrane distillation demonstrates a 71.6% reduction in the mineralization rate and a marginal improvement (37.5%) on membrane wetting tolerance. Mineral identity and growth characteristics are presented, and the analysis is extended to explore the potential improvements for carbon mineralization as well as the feasibility of future implementation.

Underestimation of Sector-Wide Methane Emissions from United States Wastewater Treatment.

An increasing percentage of US waste methane (CH4) emissions come from wastewater treatment (10% in 1990 to 14% in 2019), although there are limited measurements across the sector, leading to large uncertainties in current inventories. We conducted the largest study of CH4 emissions from US wastewater treatment, measuring 63 plants with average daily flows ranging from 4.2 × 10-4 to 8.5 m3 s-1 (<0.1 to 193 MGD), totaling 2% of the 62.5 billion gallons treated, nationally. We employed Bayesian inference to quantify facility-integrated emission rates with a mobile laboratory approach (1165 cross-plume transects). The median plant-averaged emission rate was 1.1 g CH4 s-1 (0.1-21.6 g CH4 s-1; 10th/90th percentiles; mean 7.9 g CH4 s-1), and the median emission factor was 3.4 × 10-2 g CH4 (g influent 5 day biochemical oxygen demand; BOD5)-1 [0.6-9.9 × 10-2 g CH4 (g BOD5)-1; 10th/90th percentiles; mean 5.7 × 10-2 g CH4 (g BOD5)-1]. Using a Monte Carlo-based scaling of measured emission factors, emissions from US centrally treated domestic wastewater are 1.9 (95% CI: 1.5-2.4) times greater than the current US EPA inventory (bias of 5.4 MMT CO2-eq). With increasing urbanization and centralized treatment, efforts to identify and mitigate CH4 emissions are needed.

High-Efficiency Recovery of Acetic Acid from Water Using Electroactive Gas-Stripping Membranes.

Recovery of carbon-based resources from waste is a critical need for achieving carbon neutrality and reducing fossil carbon extraction. We demonstrate a new approach for extracting volatile fatty acids (VFAs) using a multifunctional direct heated and pH swing membrane contactor. The membrane is a multilayer laminate composed of a carbon fiber (CF) bound to a hydrophobic membrane and sealed with a layer of polydimethylsiloxane (PDMS); this CF is used as a resistive heater to provide a thermal driving force for PDMS that, while a highly hydrophobic material, is known for its ability to rapidly pass gases, including water vapor. The transport mechanism for gas transport involves the diffusion of molecules through the free volume of the polymer matrix. CF coated with polyaniline (PANI) is used as an anode to induce an acidic pH swing at the interface between the membrane and water, which can protonate the VFA molecule. The innovative multilayer membrane used in this study has successfully demonstrated a highly efficient recovery of VFAs by simultaneously combining pH swing and joule heating. This novel technique has revealed a new concept in the field of VFA recovery, offering promising prospects for further advancements in this area. The energy consumption was 3.37 kWh/kg for acetic acid (AA), and an excellent separation factor of AA/water of 51.55 ± 2.11 was obtained with high AA fluxes of 51.00 ± 0.82 g.m-2hr-1. The interfacial electrochemical reactions enable the extraction of VFAs without the need for bulk temperature and pH modification.

Simple and Low-Cost Electroactive Membranes for Ammonia Recovery.

Ammonia is considered a contaminant to be removed from wastewater. However, ammonia is a valuable commodity chemical used as the primary feedstock for fertilizer manufacturing. Here we describe a simple and low-cost ammonia gas stripping membrane capable of recovering ammonia from wastewater. The material is composed of an electrically conducting porous carbon cloth coupled to a porous hydrophobic polypropylene support, that together form an electrically conductive membrane (ECM). When a cathodic potential is applied to the ECM surface, hydroxide ions are produced at the water-ECM interface, which transforms ammonium ions into higher-volatility ammonia that is stripped across the hydrophobic membrane material using an acid-stripping solution. The simple structure, low cost, and easy fabrication process make the ECM an attractive material for ammonia recovery from dilute aqueous streams, such as wastewater. When paired with an anode and immersed into a reactor containing synthetic wastewater (with an acid-stripping solution providing the driving force for ammonia transport), the ECM achieved an ammonia flux of 141.3 ± 14.0 g.cm-2.day-1 at a current density of 6.25 mA.cm-2 (69.2 ± 5.3 kg(NH3-N)/kWh). It was found that the ammonia flux was sensitive to the current density and acid circulation rate.

Plasma exosomes lncRNA-miRNA-mRNA network construction and its diagnostic efficacy identification in first-episode schizophrenia.

BACKGROUND: The exosomal lncRNA-miRNA-mRNA networks in first episode schizophrenia (FOS) have not reported yet. This study examined the lncRNA, miRNA and mRNA expression level in exosome derived from first episode schizophrenia (FOS) patients, and explored the the potential of exosomes as biomarkers for schizophrenia. METHODS: We recruited 10 FOS patients and healthy controls (HCs) respectively, examined the lncRNA, miRNA and mRNA expression level of plasma exosome by high throughput sequencing, constructed lncRNA-miRNA-mRNA network, and performed correlation analysis, GO and KEGG pathway analysis, PPI network construction and ROC analysis. RESULTS: There were 746 differently expressed lncRNA, 22 differently expressed miRNA, and 2637 differently expressed mRNA in plasma exosome in FOS compared with HCs. Then we constructed ceRNA network consisting of 8 down-regulated lncRNA, 7 up-regulated miRNA and 65 down-regulated mRNA, and 1 up-regulated lncRNA, 1 down-regulated miRNA and 4 up-regulated mRNA. The expression level of 1 lncRNA and 7 mRNA in exosomal network were correlated with PANSS score. GO and KEGG pathway analysis showed that 4 up-regulated mRNAs were enriched in neuropsychiatric system function. Down-regulated mRNA EZH2 and SIRT1 were identified as hub gene. Finally, we detected the ROC curve of ENSG00000251562, miR-26a-5p, EZH2, miR-22-3p, SIRT1, ENSG00000251562-miR-26a-5p-EZH2, ENSG00000251562-miR-22-3p-SIRT1, and found that the AUC of ceRNA network was higher than lncRNA, miRNA and mRNA alone. CONCLUSION: We constructed the lncRNA-miRNA-mRNA network in exosome derived from FOS plasma, and found that lncRNA-miRNA-mRNA network has potential as biomarkers for FOS.

Quantitative proteomics analysis reveals important roles of N-glycosylation on ER quality control system for development and pathogenesis in Magnaporthe oryzae.

Genetic studies have shown essential functions of N-glycosylation during infection of the plant pathogenic fungi, however, systematic roles of N-glycosylation in fungi is still largely unknown. Biological analysis demonstrated N-glycosylated proteins were widely present at different development stages of Magnaporthe oryzae and especially increased in the appressorium and invasive hyphae. A large-scale quantitative proteomics analysis was then performed to explore the roles of N-glycosylation in M. oryzae. A total of 559 N-glycosites from 355 proteins were identified and quantified at different developmental stages. Functional classification to the N-glycosylated proteins revealed N-glycosylation can coordinate different cellular processes for mycelial growth, conidium formation, and appressorium formation. N-glycosylation can also modify key components in N-glycosylation, O-glycosylation and GPI anchor pathways, indicating intimate crosstalk between these pathways. Interestingly, we found nearly all key components of the endoplasmic reticulum quality control (ERQC) system were highly N-glycosylated in conidium and appressorium. Phenotypic analyses to the gene deletion mutants revealed four ERQC components, Gls1, Gls2, GTB1 and Cnx1, are important for mycelial growth, conidiation, and invasive hyphal growth in host cells. Subsequently, we identified the Gls1 N-glycosite N497 was important for invasive hyphal growth and partially required for conidiation, but didn't affect colony growth. Mutation of N497 resulted in reduction of Gls1 in protein level, and localization from ER into the vacuole, suggesting N497 is important for protein stability of Gls1. Our study showed a snapshot of the N-glycosylation landscape in plant pathogenic fungi, indicating functions of this modification in cellular processes, developments and pathogenesis.

MTA1-mediated RNA m6 A modification regulates autophagy and is required for infection of the rice blast fungus.

In eukaryotes, N6 -methyladenosine (m6 A) is abundant on mRNA, and plays key roles in the regulation of RNA function. However, the roles and regulatory mechanisms of m6 A in phytopathogenic fungi are still largely unknown. Combined with biochemical analysis, MeRIP-seq and RNA-seq methods, as well as biological analysis, we showed that Magnaporthe oryzae MTA1 gene is an orthologue of human METTL4, which is involved in m6 A modification and plays a critical role in autophagy for fungal infection. The Δmta1 mutant showed reduced virulence due to blockage of appressorial penetration and invasive growth. Moreover, the autophagy process was severely disordered in the mutant. MeRIP-seq identified 659 hypomethylated m6 A peaks covering 595 mRNAs in Δmta1 appressoria, 114 m6 A peaks was negatively related to mRNA abundance, including several ATG gene transcripts. Typically, the mRNA abundance of MoATG8 was also increased in the single m6 A site mutant ∆atg8/MoATG8A982C , leading to an autophagy disorder. Our findings reveal the functional importance of the m6 A methylation in infection of M. oryzae and provide novel insight into the regulatory mechanisms of plant pathogenic fungi.

Upscaling 3D Engineered Trees for Off-Grid Desalination.

More than 70% of the population without access to safe drinking water lives in remote and off-grid areas. Inspired by natural plant transpiration, we designed and tested in this study an array of scalable three-dimensional (3D) engineered trees made of natural wood for continuous water desalination to provide affordable and clean drinking water. The trees took advantage of capillary action in the wood xylems and lifted water more than 1 foot off the ground with or without solar irradiation. This process overcame some major challenges of popular solar-driven water evaporation and water harvesting, such as intermittent operation, low water production rate, and system scaling. The trade-off between energy transfer and system footprint was tackled by optimizing the interspacing between the trees. The scaled system has a ratio of surface area (vapor generation) to project area (water transport) up to 118, significantly higher than the prevailing flat-sheet design. The extensive surface area evaporated water at a temperature cooler than the surrounding air, drawing on multiple environmental energy sources including solar, wind, or ambient heat in the air and realized continuous operation. The total energy for evaporation reached over 300% of the one-sun irradiance, enabling a freshwater production rate of 4.8 L m-2 h-1 from an array of 16 trees in an enclosed room and 14 L m-2 h-1 under a 3 m/s airflow. Furthermore, we found that the ambient heat in the air contributed 60%-70% of the total latent heat of vaporization when energy sources were decoupled. During long-term desalination tests, the engineered trees demonstrated a self-cleaning mechanism with daily cycles of salt accumulation and dissolution. Combining the quantification from an evaporation model and meteorology data covering the globe, we also demonstrated that the 3D engineered trees can be of particular interest for sustainable desalination in the Middle East and North Africa (MENA) regions.

ES&T in the 21st Century: A Data-Driven Analysis of Research Topics, Interconnections, And Trends in the Past 20 Years.

Environmental Science & Technology (ES&T) has served a leadership role in reporting advanced and significant research findings for decades and accumulated tremendous amount of high-quality literature. In this study, we developed tailored text mining methods and analyzed 29 188 papers published in ES&T from 2000 to 2019, and we performed data-driven analyses to reveal some critical information and guidance on what has been published, what topical changes have evolved, and what are the areas that deserve additional attention. While top research keywords remained stable (water, sorption, soil, emiss, oxid, exposur), the trending up and emerging keywords showed clear shift over the years. Keywords related to nanobased materials (nanoparticl, nanomateri, carbon nanotub), climate and energy (climat, ch4, greenhouse gas emiss, mitig, energi), and health (exposur, health, ingest) demonstrated the strongest uptrend in the past 10 years, while plastics and PFAS were among clear emerging topics in the past 5 years. Co-occurrence analysis showed distinct associations between media (water, soil, air, sediment), chemicals (pcb, humic subst, particulate matt), processes (sorption, remov, degrad), and properties (kinet, mechan, speciat). Furthermore, a rule-based classification deciphered trends, distributions, and interconnections of articles based on either monodomains (air, soil, solid waste, water, and wastewater) or multidomains. It found water and wastewater cross-discipline articles tended to have higher citation values, while air domain tended to stand alone. Water and air monodomains consistently increased their shares in publications (together 56.3% in 2019), while shares of soil studies gradually declined. This study provides new data-driven methods on literature mining and offers unique insights on environmental research landscape and opportunities.

Photochemical Behavior of Microbial Extracellular Polymeric Substances in the Aquatic Environment.

Microbially derived extracellular polymeric substances (EPSs) occupy a large portion of dissolved organic matter (DOM) in surface waters, but the understanding of the photochemical behaviors of EPS is still very limited. In this study, the photochemical characteristics of EPS from different microbial sources (Shewanella oneidensis, Escherichia coli, and sewage sludge flocs) were investigated in terms of the production of reactive species (RS), such as triplet intermediates (3EPS*), hydroxyl radicals (•OH), and singlet oxygen (1O2). The steady-state concentrations of •OH, 3EPS*, and 1O2 varied in the ranges of 2.55-8.73 × 10-17, 3.01-4.56 × 10-15, and 2.08-2.66 × 10-13 M, respectively, which were within the range reported for DOM from other sources. The steady-state concentrations of RS varied among different EPS isolates due to the diversity of their composition. A strong photochemical degradation of the protein-like components in EPS isolates was identified by excitation emission matrix fluorescence with parallel factor analysis, but relatively, humic-like components remained stable. Fourier-transform ion cyclotron resonance mass spectrometry further revealed that the aliphatic portion of EPS was resistant to irradiation, while other portions with lower H/C ratios and higher O/C ratios were more susceptible to photolysis, leading to the phototransformation of EPS to higher saturation and lower aromaticity. With the phototransformation of EPS, the RS derived from EPS could effectively promote the degradation of antibiotic tetracycline. The findings of this study provide new insights into the photoinduced self-evolution of EPS and the interrelated photochemical fate of contaminants in the aquatic environment.

Machine Learning: New Ideas and Tools in Environmental Science and Engineering.

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.