Methanogenic Potential of Sewer Microbiomes and Its Implications for Methane Emission.

The sewer system, despite being a significant source of methane emissions, has often been overlooked in current greenhouse gas inventories due to the limited availability of quantitative data. Direct monitoring in sewers can be expensive or biased due to access limitations and internal heterogeneity of sewer networks. Fortunately, since methane is almost exclusively biogenic in sewers, we demonstrate in this study that the methanogenic potential can be estimated using known sewer microbiome data. By combining data mining techniques and bioinformatics databases, we developed the first data-driven method to analyze methanogenic potentials using a data set containing 633 observations of 53 variables obtained from literature mining. The methanogenic potential in the sewer sediment was around 250-870% higher than that in the wet biofilm on the pipe and sewage water. Additionally, k-means clustering and principal component analysis linked higher methane emission rates (9.72 ± 51.3 kgCO2 eq m-3 d-1) with smaller pipe size, higher water level, and higher potentials of sulfate reduction in the wetted pipe biofilm. These findings exhibit the possibility of connecting microbiome data with biogenic greenhouse gases, further offering insights into new approaches for understanding greenhouse gas emissions from understudied sources.

The convergence of lactic acid microbiomes and metabolites in long-term electrofermentation.

Regulating electron transfer in predominantly fermentative microbiomes has broad implications in environmental, chemical, food, and medical fields. Here we demonstrate electrochemical control in fermenting food waste, digestate, and wastewater to improve lactic acid production. We hypothesize that applying anodic potential will expedite and direct fermentation towards lactic acid. Continued operation that introduced epi/endophytic communities (Lactococcus, Lactobacillus, Weissella) to pure culture Lactiplantibacillus plantarum reactors with static electrodes was associated with the loss of anode-induced process intensification despite 80% L. plantarum retention. Employing fluidized electrodes discouraged biofilm formation and extended electrode influence to planktonic gram-positive fermenters using mediated extracellular electron transfer. While short-term experiments differentially enriched Lactococcus and Klebsiella spp., longer-term operations indicated convergent microbiomes and product spectra. These results highlight a functional resilience of environmental fermentative microbiomes to perturbations in redox potential, underscoring the need to better understand electrode induced polymicrobial interactions and physiological impacts to engineer tunable open-culture or synthetic consortia.

Membrane electrolysis distillation for volatile fatty acids extraction from pH-neutral fermented wastewater.

Volatile fatty acids (VFAs) serve as building blocks for a wide range of chemicals, but it is difficult to extract VFAs from pH-neutral wastewater using evaporation methods because of the ionized form. This study presents a new membrane electrolysis distillation (MED) process that extracts VFAs from such fermentation solutions. MED uniquely integrates pH regulation and joule heating to facilitate the efficient evaporation of VFAs. This integration occurs alongside a hydrophobic membrane that ensures effective gas-liquid phase separation. Operating solely on electricity, MED achieved an acid flux rate of 12.03 g/m2/h at 6V. In contrast, the control results without the joule heating or pH swing only obtained a 0.23 g/m2/h and 0.32 g/m2/h flux, respectively. In addition, a physicochemical model was developed to assess the impacts of temperature on membrane surface pH. This system enhances resource recovery from waste streams and helps achieve a circular carbon economy.

Localized surface plasmon energy dissipation in bimetallic core-shell nanostructures.

Exploring the plasmon energy dissipation mechanism of bimetallic nanostructures after photoexcitation is of great significance for controlling energy transfer in plasmonic applications. The absorption, scattering, and extinction spectra of Ag@Cu, Ag@Pt, and Ag@Co core-shell nanostructures are calculated by finite element method, and the energy dissipation process is visualized by using particle trajectory and the absorbed power density distribution. The absorption/scattering ratio of the core-shell nanostructures, the shell absorptivity, the time-domain electric field as well as the extra-core electron arrangements of Ag, Cu, Pt, and Co atoms are analyzed for figuring out the energy dissipation mechanism. The results show that when a non-plasmonic metal is coated on the surface of a plasmonic metal, the plasmon energy dissipates preferentially in the shell, and the degree of dissipation depends on the imaginary part of the dielectric constant of the shell and the core. A larger dielectric constant of the shell can cause more energy to be transferred from the plasmonic metal to the shell region. This study provides the fundamental physical framework and design principles for plasmonic nanostructures.

Renewable biomass reinvigorates sustainable water-energy nexus.

The water-energy nexus has garnered worldwide interest. Current dual-functional research aimed at co-producing freshwater and electricity faces significant challenges, including sub-optimal capacities ("1 + 1 < 2"), poor inter-functional coordination, high carbon footprints, and large costs. Mainstream water-to-electricity conversions are often compromised owing to functionality separation and erratic gradients. Herein, we present a sustainable strategy based on renewable biomass that addresses these issues by jointly achieving competitive solar-evaporative desalination and robust clean electricity generation. Using hydrothermally activated basswood, our solar desalination exceeded the 100% efficiency bottleneck even under reduced solar illumination. Through simple size-tuning, we achieved a high evaporation rate of 3.56 kg h-1 m-2 and an efficiency of 149.1%, representing 128%-251% of recent values without sophisticated surface engineering. By incorporating an electron-ion nexus with interfacial Faradaic electron circulation and co-ion-predominated micro-tunnel hydrodynamic flow, we leveraged free energy from evaporation to generate long-term electricity (0.38 W m-3 for over 14d), approximately 322% of peer performance levels. This inter-functional nexus strengthened dual functionalities and validated general engineering practices. Our presented strategy holds significant promise for global human-society-environment sustainability.

De-nitrosylation Coordinates Appressorium Function for Infection of the Rice Blast Fungus.

As a signaling molecule, nitric oxide (NO) regulates the development and stress response in different organisms. The major biological activity of NO is protein S-nitrosylation, whose function in fungi remains largely unclear. Here, it is found in the rice blast fungus Magnaporthe oryzae, de-nitrosylation process is essential for functional appressorium formation during infection. Nitrosative stress caused by excessive accumulation of NO is harmful for fungal infection. While the S-nitrosoglutathione reductase GSNOR-mediated de-nitrosylation removes excess NO toxicity during appressorium formation to promote infection. Through an indoTMT switch labeling proteomics technique, 741 S-nitrosylation sites in 483 proteins are identified. Key appressorial proteins, such as Mgb1, MagB, Sps1, Cdc42, and septins, are activated by GSNOR through de-nitrosylation. Removing S-nitrosylation sites of above proteins is essential for proper protein structure and appressorial function. Therefore, GSNOR-mediated de-nitrosylation is an essential regulator for appressorium formation. It is also shown that breaking NO homeostasis by NO donors, NO scavengers, as well as chemical inhibitor of GSNOR, shall be effective methods for fungal disease control.

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.

Electrochemically coupled CH4 and CO2 consumption driven by microbial processes.

The chemical transformations of methane (CH4) and carbon dioxide (CO2) greenhouse gases typically have high energy barriers. Here we present an approach of strategic coupling of CH4 oxidation and CO2 reduction in a switched microbial process governed by redox cycling of iron minerals under temperate conditions. The presence of iron minerals leads to an obvious enhancement of carbon fixation, with the minerals acting as the electron acceptor for CH4 oxidation and the electron donor for CO2 reduction, facilitated by changes in the mineral structure. The electron flow between the two functionally active microbial consortia is tracked through electrochemistry, and the energy metabolism in these consortia is predicted at the genetic level. This study offers a promising strategy for the removal of CH4 and CO2 in the natural environment and proposes an engineering technique for the utilization of major greenhouse gases.

Coupling Curvature and Hydrophobicity: A Counterintuitive Strategy for Efficient Electroreduction of Nitrate into Ammonia.

The electrochemical upcycling of nitrate (NO3-) to ammonia (NH3) holds promise for synergizing both wastewater treatment and NH3 synthesis. Efficient stripping of gaseous products (NH3, H2, and N2) from electrocatalysts is crucial for continuous and stable electrochemical reactions. This study evaluated a layered electrocatalyst structure using copper (Cu) dendrites to enable a high curvature and hydrophobicity and achieve a stratified liquid contact at the gas-liquid interface of the electrocatalyst layer. As such, gaseous product desorption or displacement from electrocatalysts was enhanced due to the separation of a wetted reaction zone and a nonwetted zone for gas transfer. Consequently, this electrocatalyst structure yielded a 2.9-fold boost in per-active-site activity compared with that with a low curvature and high hydrophilic counterpart. Moreover, a NH3 Faradaic efficiency of 90.9 ± 2.3% was achieved with nearly 100% NO3- conversion. This high-curvature hydrophobic Cu dendrite was further integrated with a gas-extraction membrane, which demonstrated a comparable NH3 yield from the real reverse osmosis retentate brine.

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.

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.

Tough and Recyclable Phase-Separated Supramolecular Gels via a Dehydration-Hydration Cycle.

Hydrogels are compelling materials for emerging applications including soft robotics and autonomous sensing. Mechanical stability over an extensive range of environmental conditions and considerations of sustainability, both environmentally benign processing and end-of-life use, are enduring challenges. To make progress on these challenges, we designed a dehydration-hydration approach to transform soft and weak hydrogels into tough and recyclable supramolecular phase-separated gels (PSGs) using water as the only solvent. The dehydration-hydration approach led to phase separation and the formation of domains consisting of strong polymer-polymer interactions that are critical for forming PSGs. The phase-separated segments acted as robust, physical cross-links to strengthen PSGs, which exhibited enhanced toughness and stretchability in its fully swollen state. PSGs are not prone to overswelling or severe shrinkage in wet conditions and show environmental tolerance in harsh conditions, e.g., solutions with pH between 1 and 14. Finally, we demonstrate the use of PSGs as strain sensors in air and aqueous environments.

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.

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.

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.

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.

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.