Causal prior-embedded physics-informed neural networks and a case study on metformin transport in porous media.

This study introduces a novel approach to transport modelling by integrating experimentally derived causal priors into neural networks. We illustrate this paradigm using a case study of metformin, a ubiquitous pharmaceutical emerging pollutant, and its transport behaviour in sandy media. Specifically, data from metformin's sandy column transport experiment was used to estimate unobservable parameters through a physics-based model Hydrus-1D, followed by a data augmentation to produce a more comprehensive dataset. A causal graph incorporating key variables was constructed, aiding in identifying impactful variables and estimating their causal dynamics or "causal prior." The causal priors extracted from the augmented dataset included underexplored system parameters such as the type-1 sorption fraction F, first-order reaction rate coefficient α, and transport system scale. Their moderate impact on the transport process has been quantitatively evaluated (normalized causal effect 0.0423, -0.1447 and -0.0351, respectively) with adequate confounders considered for the first time. The prior was later embedded into multilayer neural networks via two methods: causal weight initialization and causal prior regularization. Based on the results from AutoML hyperparameter tuning experiments, using two embedding methods simultaneously emerged as a more advantageous practice since our proposed causal weight initialization technique can enhance model stability, particularly when used in conjunction with causal prior regularization. amongst those experiments utilizing both techniques, the R-squared values peaked at 0.881. This study demonstrates a balanced approach between expert knowledge and data-driven methods, providing enhanced interpretability in black-box models such as neural networks for environmental modelling.

A multi-criteria simulation-optimization coupling approach for effective emergency response in marine oil spill accidents.

Effective marine oil spill responses are vital to reduce environmental, societal, and economic damage. This study developed a Multi-Criteria Emergency Response System (MC-ERS) to comprehensively evaluate response efficiency, operational costs, and environmental losses. The proposed system integrates dynamic multiphase simulation of oil weathering and oil cleanup processes and further provides effective planning for multi-stage resource allocation through system optimization. The developed weight-sum model improved the performance of response operations by reducing the complexity of multi-criteria decision-making. Particle Swarm Optimization (PSO) was chosen as the foundational optimization algorithm due to its efficiency in rapid convergence and suitability for complex problems. From extensive comparisons of PSO variants across benchmark functions and inertia strategies, the C-PSO algorithm was developed, demonstrating enhanced optimization performance for MC-ERS. The developed modelling system performance was demonstrated and evaluated through a representative case study. The optimization plan coordinated resource allocation from onshore warehouses to harbors and spill sites, balancing oil recovery efficiency, costs, and ecological losses. Optimized results indicate an oil recovery of up to 76.50% in five days. Additionally, the system cuts costs by 3.45% and environmental losses by 15.75%. The findings enhance the efficiency of marine oil spill emergency response and provide support for such incidents.

Protein 4.1R regulates M1 macrophages polarization via glycolysis, alleviating sepsis-induced liver injury in mice.

Acute liver injury (ALI) is a common clinical disease caused by sepsis, metabolic syndrome, hepatitis virus. Macrophage plays an important role in the development of ALI, which is characterized by polarization and inflammatory regulation. The polarization process of macrophages is related to membrane binding proteins and adaptors. Protein 4.1R acts as an adaptor, linking membrane proteins to the cytoskeleton, and is involved in cell activation and cytokine secretion. However, whether protein 4.1R is involved in regulating macrophage polarization and inflammation-induced liver injury remains unknown. In this study, protein 4.1R is identified with the special effect on macrophage M1 polarization. And it is further demonstrated that protein 4.1R deficiency significantly enhance glycolytic metabolism. Mechanistically, the regulation of protein 4.1R on pyruvate kinase M2 (PKM2) plays a key role in glycolysis metabolism. In addition, we found that protein 4.1R directly interacts with toll-like receptor 4 (TLR4), inhibits the activation of the AKT/HIF-1α signaling pathway. In conclusion, protein 4.1R targets HIF-1α mediated glycolysis regulates M1 macrophage polarization, indicating that protein 4.1R is a candidate for regulating macrophage mediated inflammatory response. In conclusion, we have revealed a novel function of protein 4.1R in macrophage polarization and ALI, providing important insights into the metabolic reprogramming, which is important for ALI therapy. We have revealed a novel function of protein 4.1R in macrophage polarization and ALI, providing important insights into the metabolic reprogramming, which is important for ALI therapy.

Mechanism of nearshore sediment-facilitated oil transport: New insights from causal inference analysis.

A quantitative understanding of spilled oil transport in a nearshore environment is challenging due to the complex physicochemical processes in aqueous conditions. The physicochemical processes involved in oil sinking mainly include oil dispersion, sediment settling, and oil-sediment interaction. For the first time, this work attempts to address the sinking mechanism in petroleum contaminant transport using structural causal models based on observed data. The effects of nearshore salinity distribution from the estuary to the ocean on those three processes are examined. The causal inference reveals sediment settling is the crucial process for oil sinking. Salinity indirectly affects oil sinking by promoting sediment settling rather than directly affecting oil-sediment interaction. The increase of salinity from 0‰ to 35‰ provides a natural enhancement for sediment settling. Notably, unbiased causal effect estimates demonstrate the strongest positive causal effect on the settling efficiency of sediments is posed by increasing oil dispersion effectiveness, with a normalized value of 1.023. The highest strength of the causal relationship between oil dispersion and sediment settling highlights the importance of the dispersing characteristics of spilled oil to sediment-facilitated oil transport. The employed logic, a data-driven method, will shed light on adopting advanced causal inference tools to unravel the complicated contaminants' transport.

Can the Maillard reaction affect the characteristic aroma of Sesame aroma baijiu - A research on the methional during the stacking fermentation stage of jiupei.

In this study, we focused on methional, a characteristic flavor compound of Sesame aroma baijiu, and investigated its production during the stacking fermentation of baijiu jiupei. It has been speculated that the Maillard reaction occurs during the stacking fermentation, which results in the production of methional. This research showed that methional increased during the stacking process, reaching 0.45 mg/kg in the later stage of stacking fermentation. To simulate the stacking fermentation, a Maillard reaction model was established for the first time with conditions determined based on the measured stacking parameters (pH, temperature, moisture, reducing sugars, etc.). Through the analysis of the reaction products, we found that it is highly possible that the Maillard reaction occurs during the stacking fermentation, and a potential formation route of methional during the process was elucidated. These findings provide insights for the study of relevant volatile compounds in baijiu.

Transport of Microplastic and Dispersed Oil Co-contaminants in the Marine Environment.

Microplastics (MPs) and oil pollution are major concerns in oceans. Although their coexistence in oceans and the associated MP-oil-dispersant agglomerates (MODAs) have been reported, limited attention is given to the behavior of the co-contaminants. This study investigated MODA transport in a simulated ocean system and explored related mechanisms under various oil types, salinities, and mineral concentrations. We found that more than 90% of the heavy oil-formed MODAs stayed at the seawater surface, while the light oil-formed MODAs were widely distributed throughout the seawater column. The increased salinity promoted MODAs formed by 7 and 90 µm MPs to transport from the seawater surface to the column. This was elucidated by the Derjaguin-Landau-Verwey-Overbeek theory as more MODAs formed under higher salinities and dispersants kept them stable in the seawater column. Minerals facilitated the sinking of large MP-formed MODAs (e.g., 40 µm) as minerals were adsorbed on the MODA surface, but their impact on small MP-formed MODAs (e.g., 7 µm) was negligible. A MODA-mineral system was proposed to explain their interaction. Rubey's equation was recommended to predict the sinking velocity of MODAs. This study is the first attempt to reveal MODA transport. Findings will contribute to the model development to facilitate their environmental risk evaluation in oceans.

Insights into toxicity of polychlorinated naphthalenes to multiple human endocrine receptors: Mechanism and health risk analysis.

This study explored the combined disruption mechanism of polychlorinated naphthalenes (PCNs) on the three key receptors (estrogen receptor, thyroid receptor, and adrenoceptor) of the human endocrine system. The intensity of PCN endocrine disruption on these receptors was first determined using a molecular docking method. A comprehensive index of PCN endocrine disruption to human was quantified by analytic hierarchy process and fuzzy analysis. The mode of action between PCNs and the receptors was further identified to screen the molecular characteristics influencing PCN endocrine disruption through molecular docking and fractional factorial design. Quantitative structure-activity relationship (QSAR) models were established to investigate the toxic mechanism due to PCN endocrine disruption. The results showed that the lowest occupied orbital energy (ELUMO) was the most important factor contributing to the toxicity of PCNs on the endocrine receptors, followed by the orbital energy difference (ΔE) and positive Millikan charge (q+). Furthermore, the strategies were formulated through adjusting the nutritious diet to reduce health risk for the workers in PCN contaminated sites and the effectiveness and feasibility were assessed by molecular dynamic simulation. The simulation results indicated that the human health risk caused by PCN endocrine disruption could be effectively decreased by nutritional supplementation. The binding ability between PCNs and endocrine receptors significantly declined (up to -16.45%) with the supplementation of vitamins (A, B2, B12, C, and E) and carotene. This study provided the new insights to reveal the toxic mechanism of PCNs on human endocrine systems and the recommendations on nutritional supplements for health risk reduction. The methodology and findings could serve as valuable references for screening of potential endocrine disruptors and developing appropriate strategies for PCN or other persistent organic pollution control and health risk management.

Dermal exposure to synthetic musks: Human health risk assessment, mechanism, and control strategy.

Synthetic musks (SMs) have been widely used as odor additives in personal care products (PCPs). Dermal exposure to SMs is the main pathway of the accumulation of these chemicals in human kerateins and poses potential health risks. In this study, in silico methods were established to reduce the human health risk of SMs from dermal exposure by investigating the risk mechanisms, designing lower bioaccumulation ability SMs and suggesting proper PCP ingredients using molecular docking, molecular dynamics simulation, and quantitative structure-activity relationship (QSAR) models. The binding energy, a parameter reflecting the binding ability of SMs and human keratin protein (4ZRY), was used as the indicator to assess the human health risk of SMs. According to the mechanism analysis, total energy was found as the most influential molecular structural feature influencing the bioaccumulation ability of a SM, and as one of the main factors influencing the function (i.e., odor sensitivity) of an SM. The 3D-QSAR models were constructed to control the human health risk of SMs by designing lower-risk SMs derivatives. The phantolide (PHAN)- 58 was determined to be the optimum SM derivative with lower bioaccumulation ability (reduced 17.25%) and improved odor sensitivity (increased 7.91%). A further reduction of bioaccumulation ability of PHAN-58 was found when adding proper body wash ingredients (i.e., alkyl ethoxylate sulfate (AES), dimethyloldimethyl (DMDM), EDTA-Na4, ethylene glycol distearate (EGDS), hydroxyethyl cellulose (HEC), lemon yellow and octyl glucose), leading to a significant reduction of the bioaccumulation ability (42.27%) compared with that of PHAN. Results demonstrated that the proposed theoretical mechanism and control strategies could effectively reduce the human health risk of SMs from dermal exposure.

Pulse electric field assisted process for extraction of Jiuzao glutelin extract and its physicochemical properties and biological activities investigation.

Jiuzao is the residue of baijiu distillation. In this study, pulse electric field (PEF) was used to improve the extraction efficiency of Jiuzao glutelin extract (JGE). The species, physicochemical properties, and biological activities of JGE were investigated to expand its utilization. The results showed that after treatment with PEF under optimal conditions (pulse times, 83 in total; strength, 3.26 kV/cm; Jiuzao/distilled water, 3:20), the JGE content increased by 13.81% compared with ultrasound auxiliary extraction. 59.16% of the JGE was identified to be from sorghum. JGE exhibited desirable foaming, foam stability, water and oil holding capacities, and in vitro antioxidant and angiotensin-converting enzyme inhibitory activities (the IC50 value was 0.61 mg/mL). In addition, JGE exhibited high cell compatibility at proper concentrations in Caco-2 and CCD 841 CON cells. Overall, PEF is a potential technique to extract high-quality JGE from Jiuzao due to its high yield, efficiency, and maintenance of JGE bioactivities.

A Modified Sequential Deposition Route for High-Performance Carbon-Based Perovskite Solar Cells under Atmosphere Condition.

Carbon-based hole transport material (HTM)-free perovskite solar cells have exhibited a promising commercialization prospect, attributed to their outstanding stability and low manufacturing cost. However, the serious charge recombination at the interface of the carbon counter electrode and titanium dioxide (TiO2) suppresses the improvement in the carbon-based perovskite solar cells' performance. Here, we propose a modified sequential deposition process in air, which introduces a mixed solvent to improve the morphology of lead iodide (PbI2) film. Combined with ethanol treatment, the preferred crystallization orientation of the PbI2 film is generated. This new deposition strategy can prepare a thick and compact methylammonium lead halide (MAPbI3) film under high-humidity conditions, which acts as a natural active layer that separates the carbon counter electrode and TiO2. Meanwhile, the modified sequential deposition method provides a simple way to facilitate the conversion of the ultrathick PbI2 capping layer to MAPbI3, as the light absorption layer. By adjusting the thickness of the MAPbI3 capping layer, we achieved a power conversation efficiency (PCE) of 12.5% for the carbon-based perovskite solar cells.

Machine learning-aided causal inference for unraveling chemical dispersant and salinity effects on crude oil biodegradation.

Chemical dispersants have been widely applied to tackle oil spills, but their effects on oil biodegradation in global aquatic systems with different salinities are not well understood. Here, both experiments and advanced machine learning-aided causal inference analysis were applied to evaluate related processes. A halotolerant oil-degrading and biosurfactant-producing species was selected and characterized within the salinity of 0-70 g/L NaCl. Notably, dispersant addition can relieve the biodegradation barriers caused by high salinities. To navigate the causal relationships behind the experimental data, a structural causal model to quantitatively estimate the strength of causal links among salinity, dispersant addition, cell abundance, biosurfactant productivity and oil biodegradation was built. The estimated causal effects were integrated into a weighted directed acyclic graph, which showed that overall positive effects of dispersant addition on oil biodegradation was mainly through the enrichment of cell abundance. These findings can benefit decision-making prior dispersant application under different saline environments.

Inhalation and ingestion of Synthetic musks in pregnant women: In silico spontaneous abortion risk evaluation and control.

Synthetic musks (SMs) are odor additives commonly used in the personal care products. Their wide existence in the environment and the recently reported adverse impact on the production and activity of progesterone and estrogen have raised pregnancy red flags and even lead to a pregnancy loss. Apart from the suggestion of limiting SM contact and exposure, effective abortion risk control measures for SMs remain to be blank. Facing the above challenges, this study tried to establish a new theoretical circumvention strategy to reduce the abortion risk of SMs to pregnant women by designing the supplementary diet plan and environmentally friendly SMs derivatives using molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) models. According to the supplementary diet plan, the diet combination of vitamin E, vitamin B2, niacin, vitamin A, and vitamin B6 were confirmed to not only provide essential nutrients for human health, but also reduce the abortion risk in pregnant women in daily life. The multi-activity (binding ability of SMs with progesterone-estrogen) 3D-QSAR model was constructed to screen SMs derivatives. The LibDock score, a parameter reflecting the binding ability between SMs' Derivative-24 with progesterone-estrogen, decreased as much as 137.67% compared with its precursor galaxolide (HHCB). The 3D-QSAR models assisted screening indicated that Derivative-24 had lower environmental impacts (i.e., bioconcentration and mobility) and improved functional properties (odor stability, musky scent, and odor intensity). The integration of the optimum candidate, Derivative-24, with optimum three supplementary diet plans exhibited a much lower abortion risk than HHCB, demonstrating the effectiveness of the proposed theoretical circumvention strategy as a comprehensive abortion risk control measure. It also shed light on the design of new pharmaceutical and personal care products using advanced computing tools.

Cell membrane skeletal protein 4.1R participates in entry of Zika virus into cells.

Zika virus (ZIKV) is a typical mosquito-borne flavivirus known to cause severe fetal microcephaly and adult Guillain-Barré syndrome. Currently, there are no specific drugs or licensed vaccines available for ZIKV infection, and further research is required to identify host cell proteins involved in the virus's life cycle. Viruses are known to use host cell membrane skeletal proteins, such as actin and spectrin, to complete cell entry, transportation, and release. Here, based on immunoprecipitation, the Axl and ZIKV envelope (E) protein were shown to interact with the cell membrane skeleton protein 4.1R. Furthermore, deletion of 4.1R significantly reduced virus titer and viral protein synthesis. Our study showed that 4.1R is an important host cell protein during ZIKV infection and may be involved in the process of viral entry into host cells.

Machine Learning-Aided Causal Inference Framework for Environmental Data Analysis: A COVID-19 Case Study.

Links between environmental conditions (e.g., meteorological factors and air quality) and COVID-19 severity have been reported worldwide. However, the existing frameworks of data analysis are insufficient or inefficient to investigate the potential causality behind the associations involving multidimensional factors and complicated interrelationships. Thus, a causal inference framework equipped with the structural causal model aided by machine learning methods was proposed and applied to examine the potential causal relationships between COVID-19 severity and 10 environmental factors (NO2, O3, PM2.5, PM10, SO2, CO, average air temperature, atmospheric pressure, relative humidity, and wind speed) in 166 Chinese cities. The cities were grouped into three clusters based on the socio-economic features. Time-series data from these cities in each cluster were analyzed in different pandemic phases. The robustness check refuted most potential causal relationships' estimations (89 out of 90). Only one potential relationship about air temperature passed the final test with a causal effect of 0.041 under a specific cluster-phase condition. The results indicate that the environmental factors are unlikely to cause noticeable aggravation of the COVID-19 pandemic. This study also demonstrated the high value and potential of the proposed method in investigating causal problems with observational data in environmental or other fields.

A data-driven binary-classification framework for oil fingerprinting analysis.

A marine oil spill is one of the most challenging environmental issues, resulting in severe long-term impacts on ecosystems and human society. Oil dispersants are widely applied as a treating agent in oil spill response operations. The usage of dispersants significantly changes the behaviors of dispersed oil and consequently challenges the oil fingerprinting analysis. In this study, machine learning was first introduced to analyze oil fingerprinting by developing a data-driven binary classification framework. The modeling integrated dimensionality reduction algorithms (e.g., principal component analysis, PCA) to distinguish. Five groups of biomarkers, including terpanes, steranes, triaromatic steranes (TA-steranes), monoaromatic steranes (MA-steranes), and diamantanes, were selected. Different feature spaces were created from the diagnostic index of biomarkers, and six ML algorithms were applied for comparative analysis and optimizing the modeling process, including k-nearest neighbor (KNN), support vector classifier (SVC), random forest classifier (RFC), decision tree classifier (DTC), logistic regression classifier (LRC), and ensemble vote classifier (EVC). Hyperparameter optimization and cross-validation through GridSearchCV were applied to prevent overfitting and increase the model accuracy. Model performance was evaluated by model score and F-score through confusion matrices. The results indicated that the RFC algorithm from the diamantanes dataset performed the best. It delivered the highest F-score (0.871) versus the lowest F-score (0.792) from the EVC algorithm from the TA-steranes dataset by PCA with a variance of 95%. Therefore, diamantanes were recommended as the most suitable biomarker for distinguishing WCO and CDO to aid oil fingerprinting under the conditions in this study. The results proved the proposed method as a potential analysis tool for oil spill source identification through ML-aided oil fingerprinting. The study also showed the value of ML methods in oil spill response research and practice.

Photoconversion of polychlorinated naphthalenes in organic solvents under simulated sunlight: Solvent effect and mechanism.

In this work, the organic solvent effect on the photoconversion of polychlorinated naphthalenes (PCNs) under the simulated sunlight, as well as the mechanism and influence factor were studied. Eight organic solvents were selected to demonstrate the solvent effect on the photoconversion by the theoretical calculation method. It was found that the photoconversion rates of 1-chloronaphthalene (CN-1) in different organic solvents were in the order of dimethyl sulfoxide > methanol > acetonitrile > ethanol > dichloromethane > toluene > n-hexane > acetone. The result, obtained by the density functional theory (DFT) computation and the polarized continuum model (PCM) analysis in the framework of self-consistent reaction field (SCRF), indicated that the photoconversion was affected by the hydrogen-donating ability and electron-withdrawing potential of the solvents, as well as non-specific solute-solvent interactions. The photoconversion in acetonitrile for the five PCNs (1-chloronaphthalene, 2-chloronaphthalene, 2,3-dichloronaphthalene, 1,2,3,4-tetrachloronaphthalene, and 1,2,3,4,5,6,7,8- octachloronaphthalene) all fitted well with the first-order kinetic equation; and the reaction rate decreased with the increasing of number of chlorine atoms of the PCNs. Products analysis proved that the photoconversion process of PCNs went through two stages, namely the initial stage of dechlorination and the later stage of oxidative ring opening. It was found that inorganic ions (NO3-, Cl-, Fe3+, and Fe2+) promoted or inhibited the photoconversion by generating or quenching of the reactive oxygen species, and chlorophyll a promoted the photoconversion through the generation of singlet oxygen.

Phototransformation of three polychlorinated naphthalenes on surface of atmospheric particulate matter.

Polychlorinated naphthalenes (PCNs) are a new class of persistent organic pollutants. Photoconversion is an important pathway for their transformation in the environment. In this work, silica gel was used to simulate atmospheric mineral particles, and the photochemical reaction of three PCNs 1-chloronaphthalene (CN-1), 2-chloronaphthalene (CN-2) and 2,3-dichloronaphthalene (CN-10)) on silica gel surface was studied under the irradiation of high-pressure mercury lamp, the phototransformation intermediates and pathways of PCNs were investigated, effects of reactive oxygen species (ROS, ·OH, 1O2 and O2-·) were proved by free radical scavenging method and the effects of co-existing components (water, inorganic ions and fulvic acid) were examined. The results showed that all the three PCNs could be photochemical degraded on silica gel surface. The order of the apparent rate constants was CN-2 ≈ CN-1 > CN-10. ROS accelerated the photochemical reaction. The three PCNs didn't produce completely identical photoproducts, but all underwent a series of reactions such as reductive dechlorination, hydroxylation, oxidation, decarboxylation and ring opening. In addition, for the photoconversion of CN-1, the presence of water, NO3- or fulvic acid all promoted the photochemical transformation, while the presence of Cu2+ had an inhibitory effect.

Systemic Expression Analysis Reveals Prognostic Significance of WIPI3 in Hepatocellular Carcinoma.

INTRODUCTION: WD repeat domain phosphoinositide-interacting protein 3 (WIPI3) is a member of the WIPI protein family, autophagy marker, that is associated with the malignant progression of various human cancers, but its role in hepatocellular carcinoma (HCC) is still unclear. MATERIALS AND METHODS: Firstly, we collected the mRNA expression of WIPI3 in HCC through the platform of Oncomine, as well as the DNA copy number variations (CNVs), and verified it on human HCC cell line and the GEO database. Then, the subgroups and prognosis of HCC were performed by the UALCAN web tool. The mutation of WIPI3 was analyzed by cBioPortal. The coexpression of WIPI3 in HCC was identified from the LinkedOmics database, and function enrichment analysis was done using the LinkFinder module in LinkedOmics. Coexpression gene network was constructed through the STRING database, and the MCODE plug-in of which was used to build the gene modules; both of them were visualized by the Cytoscape software. Finally, the top modular genes in the same patient cohort were constructed through data mining in The Cancer Genome Atlas (TCGA) liver hepatocellular carcinoma (LIHC) by using the UCSC Xena browser. RESULTS: The results indicated that WIPI3 was frequently overexpressed in HCC, which could lead to a poor prognosis through the Kaplan-Meier (KM) analysis. Moreover, there existed mutations of WIPI3 in HCC, and the prognosis of WIPI3-altered group was significantly poor based on KM plotter data. Coexpression analysis showed that the coexpression gene of WIPI3 was associated with cell cycle and spliceosome. Further analysis suggested that WIPI3 and eukaryotic translation initiation factor 4A3 (EIF4A3) coordinately regulated the cancer cell cycle by spliceosome as a result of the strong positive correlation between them. CONCLUSION: In summary, WIPI3 is constantly overexpressed in HCC tissues, resulting in a poor prognosis; therefore, we can identify it as an effective target for the treatment of HCC.

Fate and Transport Modelling of Emerging Pollutants from Watersheds to Oceans: A Review.

This chapter provides a review of the fate and transport modelling of emerging pollutants (EPs) and discusses the major research challenges. The overwhelming limitation of the past modelling studies has been the lack of data necessary for model validation, thus calling for large-scale field data sampling. The identification and understanding of fate and transport processes and their interactions of the target EPs and the corresponding selection of appropriate parameter values were also challenging. Such limitations and challenges were evidenced by the elaboration of the representative models in the field. The review also reveales that the model parameter values varied significantly with the EPs (and chemical compositions) and media of concerns. Sensitivity analysis was found to be necessary for modelling of those EPs with limited references in the literature. In comparison with traditional water pollutants, the concentrations of many EPs in water bodies are usually low and even at a trace level, leading to uncertainties or inaccuracy in measured data. This could further challenge model calibration and validation, and especially the determination of parameter values when lacking sufficient data support. How to improve the existing models to address such an issue special for EPs is an urgent task for researchers to ensure the accuracy and reliability of modelling results.

[Construction of a stable 4.1R gene knockout cell model in RAW264.7 cells using CRISPR/Cas9 technique].

OBJECTIVE: To construct a cell model of 4.1R gene knockout in murine macrophage cell line RAW264.7 using CRISPR/Cas9 technique. METHODS: Three high?grade small?guide RNAs (sgRNAs) that could specifically identify 4.1R gene were synthesized and inserted into lentiCRISPRv2 plasmid. RAW264.7 cells were infected with sgRNA?Cas9 lentivirus from 293T cells transfected with the recombinant sgRNA?lentiCRISPRv2 plasmid, and the positive cells were screened using puromycin and the monoclonal cells were obtained. The expression of 4.1R protein in the monoclonal cells was measured by Western blotting, and the mutation site was confirmed by sequence analysis. Result A 4.1R gene knockout RAW264.7 cell line was obtained, which showed a 19?bp deletion mutation in the 4.1R gene sequence and obviously enhanced proliferation. CONCLUSION: We successfully constructed a 4.1R gene knockout macrophage cell line using CRISPR/Cas9 technique, which may facilitate further investigation of the function of 4.1R in macrophages.