Incidence and risk factors for potential drug-drug interactions in outpatients receiving opioid analgesics.

BACKGROUND: Opioids are the most frequently used drugs to treat pain in cancer patients. However opioid analgesics can cause adverse effects and potential drug-drug interaction. RESEARCH DESIGN AND METHODS: This cross-sectional retrospective study analyzed pDDI in 1839 patients with opioid analgesics in a large comprehensive hospital in China from January 1 to 31 December 2022. Three drug interaction databases were used to screen for pDDI including Drugs (U.S.A.), Medscape (U.S.A.), and Drug Assistant of Dingxiangyuan (China). RESULTS: The prevalence of pDDIs among 1839 patients was around 41.27% of 759 patients, and 564 patients (74.31%) with pDDIs were diagnosed with tumor. Further, the total of 275 various pDDIs combinations were identified. The combination of oxycodone with morphine had the most frequent occurrence of 229 times, and its adverse effects mainly related to exacerbate central respiratory depression. While, gender, tumor, number of diagnoses, and the variety of opioid analgesics used were independent risk factors for pDDIs. CONCLUSIONS: Outpatients taking opioid analgesics had a higher incidence of pDDIs. As consequently, optimized monitoring and management of patients taking opioid analgesics is recommended in order to ensure patient medication safety.

Response mechanism of a highly efficient partial nitritation-anammox (PN/A) process under antibiotic stress: Extracellular polymers, microbial community, and functional genes.

The Partial nitritation-Anammox (PN/A) process can be restricted when treating high ammonia nitrogen wastewater containing antibiotics. This study aims to explore the response mechanism of the PN/A process under antibiotic stress. Results showed the PN/A process achieved a nitrogen removal rate higher than 1.01 ± 0.03 kg N/m3/d under long-term sulfamethazine stress. The increase of extracellular polymers from 22.52 to 43.96 mg/g VSS was conducive to resisting antibiotic inhibitory. The increase of Denitratisoma and SM1A02 abundance as well as functional genes nirS and nirK indicated denitrifiers should play an important role in the stability of the PN/A system under sulfamethazine stress. In addition, antibiotic-resistant genes (ARGs) sul1 and intI1 significantly increased by 8.78 and 5.12 times of the initial values to maintain the resistance of PN/A process to sulfamethazine stress. This study uncovers the response mechanism of the PN/A process under antibiotic stress, offering a scientific basis and guidance for further application in the future.

Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design.

Metabolic modeling and machine learning (ML) are crucial components of the evolving next-generation tools in systems and synthetic biology, aiming to unravel the intricate relationship between genotype, phenotype, and the environment. Nonetheless, the comprehensive exploration of integrating these two frameworks, and fully harnessing the potential of fluxomic data, remains an unexplored territory. In this study, we present, rigorously evaluate, and compare ML-based techniques for data integration. The hybrid model revealed that the overexpression of six target genes and the knockout of seven target genes contribute to enhanced ethanol production. Specifically, we investigated the influence of succinate dehydrogenase (SDH) on ethanol biosynthesis in Saccharomyces cerevisiae through shake flask experiments. The findings indicate a noticeable increase in ethanol yield, ranging from 6 % to 10 %, in SDH subunit gene knockout strains compared to the wild-type strain. Moreover, in pursuit of a high-yielding strain for ethanol production, dual-gene deletion experiments were conducted targeting glycerol-3-phosphate dehydrogenase (GPD) and SDH. The results unequivocally demonstrate significant enhancements in ethanol production for the engineered strains Δsdh4Δgpd1, Δsdh5Δgpd1, Δsdh6Δgpd1, Δsdh4Δgpd2, Δsdh5Δgpd2, and Δsdh6Δgpd2, with improvements of 21.6 %, 27.9 %, and 22.7 %, respectively. Overall, the results highlighted that integrating mechanistic flux features substantially improves the prediction of gene knockout strains not accounted for in metabolic reconstructions. In addition, the finding in this study delivers valuable tools for comprehending and manipulating intricate phenotypes, thereby enhancing prediction accuracy and facilitating deeper insights into mechanistic aspects within the field of synthetic biology.

Linking PAHs concentration, risk to PAHs source shift in soil and water in epikarst spring systems, Southwest China.

A systematic assessment of the variations in the ecological risk of PAHs and the key emission sources controlling the variations is of great importance to human health and aquatic organisms. PAH concentrations, composition, source, and ecological risk in soils and water in two different periods (2010-2011 and 2019) of three typical epikarst springs in Southwest China were investigated. Results showed that PAH concentrations in soil and water have an overall downward trend (a reduction of 57 % and 93 %, respectively) in the past 10 years, which is consistent with the downward trend in the relative contribution rate of raw coal production (a 66 % reduction). In terms of composition, the proportion of low-ring PAHs decreased, medium-ring and high-ring PAHs increased in the soil profile. The proportion of low-ring PAHs did not change obviously, the proportion of medium-ring PAHs increased, and the proportion of high-ring PAHs decreased in epikarst springs. The source of PAHs changed remarkably over time, the relative contribution of coal combustion to PAHs decreased from 38 % to 20 %, and the vehicle contribution of PAHs increased from 31 % to 44 % in soils. The relative contribution rate of unburned oil and coke oven and biomass combustion change is less. Furthermore, the ecological risk of PAHs in the soils was reduced from moderate risk 2 to moderate risk 1, the risk in epikarst spring was reduced from high risk to moderate risk 2 after 10 years. This study demonstrates that substituting petroleum and coal with green energies can reduce PAH concentrations and risk.

Thiamethoxam adsorption by ZnCl2 modified cow manure biochar: Mechanism and quantitative prediction.

The overuse of thiamethoxam (THM) has threatened the survival of living organisms and it is necessary to find an environmentally friendly material to remove THM frequently detected in water. Biochar prepared from cow manure modified with ZnCl2 (Zn-CBC) was used to remove THM. Compared to the unmodified cow manure biochar (CBC), the removal ratio of THM by Zn-CBC was enhanced 35 times. In the mechanistic analysis, SEM and BET showed that Zn-CBC had a good pore structure and its specific surface area (166.502 m2 g-1) increased to 17 times that of CBC, indicating that Zn-CBC had good pore adsorption properties. The adsorption kinetic and isotherm implied that the main mechanism was chemisorption including π-π interaction and H-bonding. Furthermore, the stable graphitized structure of Zn-CBC allowed for efficient adsorption and reusability. In addition, this study constructed an intelligent prediction model using batch experiment data, and the high R2 (0.978) and low RMSE (0.057) implied that the model could accurately and quantitatively predict the adsorption efficiency. This paper provides a novel perspective to simultaneously remove the neonicotinoid insecticides and realize the resource utilization of cow manure.

Ammonium sulfate supplementation enhances erythromycin biosynthesis by augmenting intracellular metabolism and precursor supply in Saccharopolyspora erythraea.

In this study, the cellular metabolic mechanisms regarding ammonium sulfate supplementation on erythromycin production were investigated by employing targeted metabolomics and metabolic flux analysis. The results suggested that the addition of ammonium sulfate stimulates erythromycin biosynthesis. Targeted metabolomics analysis uncovered that the addition of ammonium sulfate during the late stage of fermentation resulted in an augmented intracellular amino acid metabolism pool, guaranteeing an ample supply of precursors for organic acids and coenzyme A-related compounds. Therefore, adequate precursors facilitated cellular maintenance and erythromycin biosynthesis. Subsequently, an optimal supplementation rate of 0.02 g/L/h was determined. The results exhibited that erythromycin titer (1311.1 µg/mL) and specific production rate (0.008 mmol/gDCW/h) were 101.3% and 41.0% higher than those of the process without ammonium sulfate supplementation, respectively. Moreover, the erythromycin A component proportion increased from 83.2% to 99.5%. Metabolic flux analysis revealed increased metabolic fluxes with the supplementation of three ammonium sulfate rates.

Free nitrous acid prediction in ANAMMOX process using hybrid deep neural network model.

Free nitrous acid (FNA) is a critical metric for stabilization of ANAMMOX but can not be directly and immediately measured by sensors or chemical measurement method, which hinders the effective management and operation for ANAMMOX. This study focuses on FNA prediction using hybrid model based on temporal convolutional network (TCN) combined with attention mechanism (AM) optimized by multiobjective tree-structured parzen estimator (MOTPE), called MOTPE-TCNA. A case study in an ANAMMOX reactor is carried out. Results show that nitrogen removal rate (NRR) is highly correlated with FNA concentration, indicating that it can forecast the operational status by predicting FNA. Then, MOTPE successfully optimizes the hyperparameters of TCN, helping TCN achieve a high prediction accuracy, and AM furtherly improves model accuracy. MOTPE-TCNA obtains the highest prediction accuracy, whose R2 value gets 0.992, increasing 1.71-11.80% compared to other models. As a deep neural network model, MOTPE-TCNA has more advantages than traditional machine learning methods in FNA prediction, which is beneficial to maintain the stable operation and easy control for ANAMMOX process.

Accurate multi-objective prediction of CO2 emission performance indexes and industrial structure optimization using multihead attention-based convolutional neural network.

The establishment of specific targets for the global carbon peaking and neutrality raises urgent requirements for prediction of CO2 emission performance indexes (CEPIs) and industrial structure optimization. However, accurate multi-objective prediction of CEPIs is still a knotty problem. In the present study, multihead attention-based convolutional neural network (MHA-CNN) model was proposed for accurate prediction of 4 CEPIs and further provided the rational suggestions for further industrial structure optimization. The proposed MHA-CNN model introduces deep learning mechanism with efficient resolution strategies for training model overfitting, feature extraction, and self-supervised learning to acquire the adaptability for CEPIs. Multihead attention (MHA) mechanism plays important roles in influence weight interpretation of variables to facilitate the prediction performance of CNN on CEPIs. The MHA-CNN model presented its overwhelmingly superior performance to CNN model and long short-term memory (LSTM) model, two frequently-used models, in multi-objective prediction of CEPIs using 8 influence variables, which highlighted advantages of MHA module in multi-dimensional feature extraction. Additionally, contributions of influence variables to CEPIs based on MHA analyses presented relatively high consistency with the geographical distribution analyses, indicating the excellent capacity of the MHA module in variable weights identification and contribution dissection. Based on the more accurate prediction results by MHA-CNN than those by CNN and LSTM model, the increase in the tertiary industry and the decreases in the first and secondary industries are conducive to improvement of total-factor carbon emission efficiency and further enhancement of effective energy utilization in regions with inefficient carbon emissions. This study provides insights towards the critical roles of the proposed MHA-CNN model in accurate multi-objective prediction of CEPIs and further industrial structure optimization for improvement of total-factor carbon emission efficiency.

Novel hydrophilic straw biochar for the adsorption of neonicotinoids: kinetics, thermodynamics, influencing factors, and reuse performance.

Nitenpyram (NIT) is the most water-soluble neonicotinoid (NEO). It has been shown to pose a serious threat to human health and the environment but was always ignored due to its limited market share. There were few experts who studied NIT's transport behavior on biochar. In this study, two types of biochar were co-activated separately using zinc chloride combined with phosphoric acid and potassium hydroxide combined with acetic acid, marked as ZBC and KBC. Characterizations suggested that hydrophilic ZBC and KBC had more surface functional groups than unmodified biochar (BC), and specific surface areas of ZBC (456.406 m2·g-1) and KBC (750.588 m2·g-1) were significantly higher than of BC (67.181 m2·g-1). The pore structures of KBC and ZBC were hierarchical porous structures with different pore sizes and typical microporous structure, respectively. The adsorption performance of either NIT or IMI on KBC was better than that on ZBC. Only 0.4 g·L-1 of KBC can absorb 89.62% of NIT in just 5 min. The equilibrium adsorption amounts of NIT on ZBC and KBC were 17.995 mg·g-1 and 82.910 mg·g-1. Elovich and Langmuir models were used to evaluate the whole adsorption process, which was attributed to the chemisorption mechanism. In addition, removal rates of NIT were negatively correlated to NIT's initial concentration and positively correlated to the dose of biochar. pH had almost no effect on adsorption, but the presence of salt ions can inhibit the removal of NIT. Long-term stabilities of biochars were also acceptable. These findings will promote the development in the preparation of biochar fields and provide a positive reference value for NEO removal.

Enhanced biodegradation of thiamethoxam with a novel polyvinyl alcohol (PVA)/sodium alginate (SA)/biochar immobilized Chryseobacterium sp H5.

Long-term and extensive usage of thiamethoxam, the second-generation neonicotinoid insecticide, has caused a serious threat to non-target organisms and ecological security. Efficient immobilized microorganism techniques are a sustainable solution for bioremediation of thiamethoxam contamination. A Gram-negative aerobic bacterium Chryseobacterium sp H5 with high thiamethoxam-degrading efficiencies was isolated from activated sludge. Then we developed a novel polyvinyl alcohol (PVA)/sodium alginate (SA)/biochar bead with this functional microbe immobilization to enhance the biodegradation and removal of thiamethoxam. Results indicated that the total removal and biodegradation rate of thiamethoxam with PVA/SA/biochar (0.7 %) beads with Chryseobacterium sp H5 immobilization at 30 °C and pH of 7.0 within 7 d reached about 90.47 % and 68.03 %, respectively, much higher than that using PVA/SA immobilized microbes (75.06 %, 56.05 %) and free microbes (61.72 %). Moreover, the PVA/SA/biochar (0.7 %) immobilized microbes showed increased tolerance to extreme conditions. Biodegradation metabolites of thiamethoxam were identified and two intermediates were first reported. Based on the identified biodegradation intermediates, cleavage of C-N between the 2-chlorothiazole ring and oxadiazine, dichlorination, nitrate reduction and condensation reaction would be the major biodegradation routes of thiamethoxam. Results of this work suggested the novel PVA/SA/biochar beads with Chryseobacterium sp H5 immobilization would be helpful for the effective bioremediation of thiamethoxam contamination.

Exosomes from artesunate-treated bone marrow-derived mesenchymal stem cells transferring SNHG7 to promote osteogenesis via TAF15-RUNX2 pathway.

Aim: Effect of artesunate (ART)-treated bone marrow-derived mesenchymal stem cells-derived exosomes (BMSC-Exos) on osteogenesis and its underlying mechanisms were investigated. Materials & methods: Proliferation, alkaline phosphatase activity and calcified nodule formation of osteoblasts were determined. A mouse model of osteoporosis was established by ovariectomy. Results: SNHG7 was upregulated in BMSC-Exos by twofold, which was further enhanced in ART-BMSC-Exos by about twofold. ART intensified BMSC-Exos-induced proliferation, alkaline phosphatase activity by about fourfold, calcified nodule formation by about threefold and upregulation of osteogenesis related molecules RUNX2 (by 50%), BMP2 (by 30%) and ATF4 (by 40%) via delivering SNHG7. Mechanistically, SNHG7 recruited TAF15 to facilitate RUNX2 stability. Conclusion: ART-BMSC-Exos facilitated osteogenesis via delivering SNHG7 by modulating TAF15/RUNX2 axis.

Osteoporosis is the most common and complex skeletal disorder worldwide. Exosomes derived from bone marrow-derived mesenchymal stem cells (BMSC-Exos) have been recognized as an ideal seed source for bone tissue regeneration. We aimed to explore the effect of artesunate (ART)-BMSC-Exos on osteogenesis and its underlying mechanisms. The results showed that ART-BMSC derived exosomal SNHG7 facilitated osteoblast activity and attenuated osteogenesis in mice by modulating TAF15/RUNX2 pathway. Our findings contribute to a better understanding of the therapeutic mechanisms of ART-BMSCs-Exos for osteoporosis and suggest ART-BMSC-Exos as a novel therapeutic option for osteoporosis.

The neural stem cell properties of Pkd2l1+ cerebrospinal fluid-contacting neurons in vivo.

The neural stem cells (NSCs) in the ventricular-subventricular zone of the adult mammalian spinal cord may be of great benefit for repairing spinal cord injuries. However, the sources of NSCs remain unclear. Previously, we have confirmed that cerebrospinal fluid-contacting neurons (CSF-cNs) have NSC potential in vitro. In this study, we verified the NSC properties of CSF-cNs in vivo. In mouse spinal cords, Pkd2l1+ CSF-cNs localized around the central canal express NSC markers. In vitro, Pkd2l1+ CSF-cNs form a neurosphere and express NSC markers. Activation and proliferation of CSF-cNs can be induced by injection of the neurotrophic factors basic fibroblast growth factor (bFGF) and vascular endothelial growth factor (VEGF) into the lateral ventricle. Spinal cord injury (SCI) also induces NSC activation and proliferation of CSF-cNs. Collectively, our results demonstrate that Pkd2l1+ CSF-cNs have NSC properties in vivo and may be involved in SCI recovery.

The Shift of Soil Bacterial Community After Afforestation Influence Soil Organic Carbon and Aggregate Stability in Karst Region.

Soil microbes regulate the carbon cycle and affect the formation and stabilization of soil aggregates. However, the interactions between the soil microbial community and soil organic carbon (SOC) fractions, organic carbon (OC) content in aggregates, and soil aggregate stability after afforestation are remain poorly understood. In our study, we investigated SOC fractions in bulk soil, aggregate-associated OC content, soil aggregate stability, and soil bacterial community with high-throughput 16S rRNA sequencing at sites representing natural secondary forest (NF) and managed forest (MF), with cropland (CL) as reference in a degraded karst region of Southwest China. Our results showed that afforestation remarkably increased the SOC fraction and OC content in aggregates, the mean weight diameter (MWD), and the mean geometric diameter (GMD). The most dominant bacterial phyla detected were Acidobacteriota, Actinobacteriota, Proteobacteria, and Chloroflexi across all soils. Afforestation remarkably altered the relative abundances of most of the dominant soil bacteria at the phylum, class, and order levels. Interestingly, such changes in the abundance of soil bacteria taxa had significantly effects on SOC fraction, aggregate-associated OC content, MWD, and MGD. The abundance of dominant bacterial taxa such as Methylomirabilota, Latescibacterota, Methylomirabilia, MB-A2-108, norank_Latescibacterota; Dehalococcoidia, Rokubacteriales, Gaiellales, Microtrichales, norank_c__MB-A2-108, norank_c__norank_p__Latescibacterota, Rhizobiales, and S085 not only remarkably increased but also had significant positive effects on SOC fractions and aggregate-associated OC content after afforestation. Moreover, MWD and MGD were positively correlated with the relative abundance of Methylomirabilota, Methylomirabilia, Rokubacteriales, Latescibacterota, and Rhizobiales. Results indicated the importance of certain soil bacteria for regulating SOC storage and soil aggregate stability. We concluded that afforestation on cropland could alter the abundance of soil bacteria, and these changes modulate the stability of soil aggregates and SOC fractions.

Engineering of succinyl-CoA metabolism in view of succinylation regulation to improve the erythromycin production.

As a novel protein post-translational modification (PTM), lysine succinylation is widely involved in metabolism regulation by altering the activity of catalytic enzymes. Inactivating succinyl-CoA synthetase in Saccharopolyspora erythraea HL3168 E3 was proved significantly inducing the global protein hypersuccinylation. To investigate the effects, succinylome of the mutant strain E3ΔsucC was identified by using a high-resolution mass spectrometry-based proteomics approach. PTMomics analyses suggested the important roles of succinylation on protein biosynthesis, carbon metabolism, and antibiotics biosynthesis in S. erythraea. Enzymatic experiments in vivo and in vitro were further conducted to determine the succinylation regulation in the TCA cycle. We found out that the activity of aconitase (SACE_3811) was significantly inhibited by succinylation in E3ΔsucC, which probably led to the extracellular accumulation of pyruvate and citrate during the fermentation. Enzyme structural analyses indicated that the succinylation of K278 and K373, conservative lysine residues locating around the protein binding pocket, possibly affects the activity of aconitase. To alleviate the metabolism changes caused by succinyl-CoA synthetase inactivation and protein hypersuccinylation, CRISPR interference (CRISPRi) was applied to mildly downregulate the transcription level of gene sucC in E3. The erythromycin titer of the CRISPRi mutant E3-sucC-sg1 was increased by 54.7% compared with E3, which was 1200.5 mg/L. Taken together, this work not only expands our knowledge of succinylation regulation in the TCA cycle, but also validates that CRISPRi is an efficient strategy on the metabolic engineering of S. erythraea. KEY POINTS: • We reported the first systematic profiling of the S. erythraea succinylome. • We found that the succinylation regulation on the activity of aconitase. • We enhanced the production of erythromycin by using CRISPRi to regulate the transcription of gene sucC.

Reconstruction of the Genome-Scale Metabolic Model of Saccharopolyspora erythraea and Its Application in the Overproduction of Erythromycin.

Saccharopolyspora erythraea is considered to be an effective host for erythromycin. However, little is known about the regulation in terms of its metabolism. To develop an accurate model-driven strategy for the efficient production of erythromycin, a genome-scale metabolic model (iJL1426) was reconstructed for the industrial strain. The final model included 1426 genes, 1858 reactions, and 1687 metabolites. The accurate rates of the growth predictions for the 27 carbon and 31 nitrogen sources available were 92.6% and 100%, respectively. Moreover, the simulation results were consistent with the physiological observation and 13C metabolic flux analysis obtained from the experimental data. Furthermore, by comparing the single knockout targets with earlier published results, four genes coincided within the range of successful knockouts. Finally, iJL1426 was used to guide the optimal addition strategy of n-propanol during industrial erythromycin fermentation to demonstrate its ability. The experimental results showed that the highest erythromycin titer was 1442.8 µg/mL at an n-propanol supplementation rate of 0.05 g/L/h, which was 45.0% higher than that without n-propanol supplementation, and the erythromycin-specific synthesis rate was also increased by 30.3%. Therefore, iJL1426 will lead to a better understanding of the metabolic capabilities and, thus, is helpful in a systematic metabolic engineering approach.

Impact of biosurfactant and iron nanoparticles on biodegradation of polyaromatic hydrocarbons (PAHs).

Polycyclic aromatic hydrocarbons (PAHs) are hazardous toxic contaminants and considered as primary pollutants due to their persistent nature and most of them are carcinogenic and mutagenic. The key challenge in PAHs degradation is their hydrophobic nature, which makes them one of the most complex materials and inaccessible by a broad range of microorganisms. This bioavailability can be increased by using a biosurfactant. In the present study mixed PAHs were degraded using the biosurfactant producing bacterial strains. In addition, iron nanoparticles were synthesized and the impact of iron nanoparticles on the growth of the mixed bacterial strains (Pseudomonas stutzeri NA3 and Acinetobacter baumannii MN3) was optimized. The mixed PAHs (anthracene, pyrene, and benzo(a)pyrene) degradation was enhanced by addition of biosurfactant (produced by Bacillus subtilis A1) and iron nanoparticles, resulting in 85% of degradation efficiency. The addition of the biosurfactant increased the bioavailability of the PAHs in the aqueous environment, which might help bacterial cells for the initial settlement and development. The addition of iron nanoparticles increased both bacterial biomass and PAHs adsorption over their surface. These overall interactions assisted in the utilization of PAHs by the mixed bacterial consortia. This study illustrates that this integrated approach can be elaborated for the removal of the complex PAHs pollutants from soil and aqueous environments.

Detection of Neonicotinoids in agriculture soil and degradation of thiacloprid through photo degradation, biodegradation and photo-biodegradation.

The social and ecological influence of Neonicotinoids (NEOs) usage in agriculture sector is progressively higher. There are seven NEOs insecticides widely used for the insects control. Among the NEOs, thiacloprid (THD) was extensively used for insect control during crop cultivation. This study targets to analyse the contamination levels of NEOs in agricultural soil and identify photo-biodegradation of THD degradation using pure isolates and mixed consortium. The photo degradation (PD), biodegradation (BD) and photo-biodegradation (PBD) of THD were compared. The corn field agricultural soils were polluted by four NEOs, among them THD had greater contamination level (surface soil: 3901.2 ± 0.04 µg/g) and (sub-surface soil: 3988.6 ± 0.05 µg/g). Three soil free enriched bacterial strains following Bacillus atrophaeus (PB-2), Priestia megaterium (PB-3) (formerly known as Bacillus megaterium), and Peribacillus simplex (PB-4) (formerly known as Bacillus simplex) were identified by microbiological and molecular 16s rRNA gene sequencing. The PD, BD and PBD of THD were conducted and degradation rate was detected by instrument UPLC-MS-MS. The PBD process with blue-LEDs showed better THD degradation efficiency than PD and BD, where the specific THD degradation rate was 85 ± 0.2%, 87 ± 0.5%, and 89 ± 0.3%, respectively for PB-2, PB-3 and PB-4. Then, the photo-biodegradation performance is greater at 150, 175, 200 rpm, pH 7.0-9.0, and temperature 30-35 °C. After the PBD system deliver four intermediate metabolites, the THD degradation process maybe through nitro reduction, hydroxylation and oxidative cleavage pathway.

Inference of emission history of neonicotinoid pesticides from marine sediment cores impacted by riverine runoff of a developed agricultural region: The Pearl River Basin, China.

Neonicotinoids (NEOs), as the most-consumed pesticides on a global scale, have posed a serious threat to human health and ecological environment. Information regarding the emission history of NEOs is of great importance to improve the prediction of their environmental loading and biological risk potential. In the present study, contamination levels and compositions of 12 NEOs were identified in 8 sediment cores from the Lingdingyang Estuary, which was impacted by agricultural emissions in riverine runoff of the Pearl River Basin for centuries. The total concentration of 12 target NEOs (∑12NEOs) ranged from 0.02 to 69.5 ng/g dw along the sediment core profile, with a mean of 12.9 ± 15.9 ng/g dw. Net deposition fluxes and concentrations of 5 parent NEOs experienced a remarkable exponential increase in the vertical profile of sediment cores, except for imidacloprid (IMI). Despite the similar exponential growth before 2012, subsequent decreased levels of IMI in historical sediment indicated its gradual replacement by other NEOs. IMI was the NEO with the highest frequency of 80.3% and the highest mean concentration of 7.66 ± 8.76 ng/g dw. The ecological risk assessment of NEOs suggests that 65.1% of sediment samples exceeded the chronic threshold for aqueous organisms using equilibrium partitioning approach. Since downward diffusion of NEOs in the Lingdingyang Estuary was rectified by their rapid desorption, the sedimentary record probably provided an accurate illustration of agricultural NEO emissions in the Pearl River Basin, China. The recent NEO inventory in the adjacent waters of core sites was estimated with a mean of 76.8 tons/yr. This study provides insights into the role of agricultural emission in riverine runoff in the environmental loads of NEOs in the historical sediment.

Designing NAZO@BC electrodes for enhanced elimination of hydrophilic organic pollutants in heterogeneous electro-Fenton system: Insights into the detoxification mediated by 1O2 and •OH.

Hydrophilic organic pollutants (HLOPs) in effluents of wastewater treatment plants are more prevalent than hydrophobic counterparts, therefore development of upstream processes that can effectively enhance the removal of HLOPs can substantially enhance overall treatment performance. To bridge this gap, 3D electrodes made of biochar-supported Al-ZnO nanoparticles (NAZO@BC) applied in heterogeneous electro-Fenton (EF) system, abbreviated as NBE-EF system, is rationally designed for enhanced elimination of HLOPs in wastewater. Our analysis indicates the NBE-EF system results in an efficient THM elimination, 42.4 times greater than that of conventional EF system. MoS2 as an efficient cocatalyst plays an important role in the conversion from Fe(III) to Fe(II). Singlet oxygen (1O2) and hydroxyl radical (•OH) are identified as the primary reactive oxygen species (ROS) in the NBE-EF system. NAZO@BC electrodes could concentrate HLOPs on their surface and degrade it effectively, achieving also a self-cleaning effect. Effective elimination of four HLOPs, i.e., thiamethoxam (THM), dinotefuran (DIN), nitenpyram (NIT), and acetamiprid (ACE), demonstrated the high degradation performance of the NBE-EF system, even at neutral and alkaline conditions. This study provides a new approach for enhanced elimination of HLOPs in wastewater treatment and mechanical insights into degradation pathways and toxicity attenuation.

Water quality prediction model using Gaussian process regression based on deep learning for carbon neutrality in papermaking wastewater treatment system.

Wastewater recycling is the measure with enormous potentiality to achieve carbon neutrality in wastewater treatment plants. High-precision online monitoring can improve the stability of wastewater treatment system and help wastewater recycling. A new water quality prediction CSWLSTM-GPR model, which fused the spatial feature of convolutional neural network (CNN), the temporal feature of sharing-weight long short-term memory (SWLSTM) and the probabilistic reliability of Gaussian process regression (GPR), was applied for monitoring papermaking wastewater treatment system with high-precision point prediction and interval prediction. Compared with SWLSTM-GPR and CLSTM-GPR, RMSE of CSWLSTM-GPR reduced by more than 48.9% on effluent chemical oxygen demand (CODeff), MAE reduced by more than 49.3%, R2 increased by more than 25.14%, R increased by more than 7.07%. And for the effluent suspended solids (SSeff), CSWLSTM-GPR had better predictive results than SWLSTM-GPR and CSWLSTM-GPR. Compared with SWLSTM-GPR, RMSE, MAE, R, R2 of CSWLSTM-GPR on effluent suspended solids (SSeff) were improved by 4.8%, 6.1%, 29.01% and 31.15%, respectively. Simulation results showed convincing comprehensive forecasting ability were obtained and the true values frequently stayed within the water quality range obtained by CSWLSTM-GPR model, which provided important insights for online monitoring, wastewater recycling and carbon neutrality of papermaking industry.