Emerging contaminants: A One Health perspective.

Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary health. Despite global efforts to mitigate legacy pollutants, the continuous introduction of new substances remains a major threat to both people and the planet. In response, global initiatives are focusing on risk assessment and regulation of emerging contaminants, as demonstrated by the ongoing efforts to establish the UN's Intergovernmental Science-Policy Panel on Chemicals, Waste, and Pollution Prevention. This review identifies the sources and impacts of emerging contaminants on planetary health, emphasizing the importance of adopting a One Health approach. Strategies for monitoring and addressing these pollutants are discussed, underscoring the need for robust and socially equitable environmental policies at both regional and international levels. Urgent actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.

Designing high thermally stable deep red phosphors based on low thermal expansion coefficients for optical applications.

Mn4+-activated oxide phosphors with low cost and unique luminescent properties have been considered as a promising candidate for various optical applications, while the search for high thermal stable red-emitting phosphors is still a huge challenge. In our work, we find and unveil the relationship between luminescence thermal quenching behavior and thermal expansion coefficients (α/10-6 K-1) based on double-perovskite niobate phosphors Ca2LnNbO6:Mn4+ (Ln3+ = Y3+, Gd3+, La3+, or Lu3+). It can be concluded that the phosphors with low thermal expansion coefficients contribute to high thermal stability. Subsequently, Ca2LuNbO6:Mn4+ accomplishes accurate temperature testing and high-CRI white light-emitting diodes. Thus, a thermal expansion coefficient strategy is a new guide to select the appropriate substrate with high thermal stability for an Mn4+-activated emitter.

Influence mechanism of anions on iron doping into swine bone char: Promoting non-radical oxidation of acetaminophen in a Fenton-like system.

The application of iron-doped biochar in peroxymonosulfate (PMS) activation systems has gained increasing attention due to their effectiveness and environmental friendliness in addressing environmental issues. However, the behavioral mechanism of iron doping and the detailed 1O2 generation mechanism in PMS activation systems remain ambiguous. Here, we investigated the effects of three anions (Cl-, NO3-and SO42-) on the process of iron doping into bone char, leading to the synthesis of three iron-doped bone char (Fe-ClBC, Fe-NBC and Fe -SBC). These iron-doped bone char were used to catalyze PMS to degrade acetaminophen (APAP) and exhibited the following activity order: Fe-ClBC > Fe-NBC > Fe-SBC. Characterization results indicated that iron doping primarily occurred through the substitution of calcium in hydroxyapatite within BC. In the course of the impregnation, the binding of SO42- and Ca2+ hindered the exchange of iron ions, resulting in lower catalytic activity of Fe-SBC. The primary reactive oxygen species in the Fe-ClBC/PMS and Fe-NBC/PMS systems were both 1O2. 1O2 is produced through O2•- conversion and PMS self-dissociation, which involves the generation of metastable iron intermediates and electron transfer within iron species. The presence of oxygen vacancies and more carbon defects in the Fe-ClBC catalyst facilitates 1O2 generation, thereby enhancing APAP degradation within the Fe-ClBC/PMS system. This study is dedicated to in-depth exploration of the mechanisms underlying iron doping and defect materials in promoting 1O2 generation.

Predicting circRNA-RBP Binding Sites Using a Hybrid Deep Neural Network.

Circular RNAs (circRNAs) are non-coding RNAs generated by reverse splicing. They are involved in biological process and human diseases by interacting with specific RNA-binding proteins (RBPs). Due to traditional biological experiments being costly, computational methods have been proposed to predict the circRNA-RBP interaction. However, these methods have problems of single feature extraction. Therefore, we propose a novel model called circ-FHN, which utilizes only circRNA sequences to predict circRNA-RBP interactions. The circ-FHN approach involves feature coding and a hybrid deep learning model. Feature coding takes into account the physicochemical properties of circRNA sequences and employs four coding methods to extract sequence features. The hybrid deep structure comprises a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU). The CNN learns high-level abstract features, while the BiGRU captures long-term dependencies in the sequence. To assess the effectiveness of circ-FHN, we compared it to other computational methods on 16 datasets and conducted ablation experiments. Additionally, we conducted motif analysis. The results demonstrate that circ-FHN exhibits exceptional performance and surpasses other methods. circ-FHN is freely available at https://github.com/zhaoqi106/circ-FHN .

m5UMCB: Prediction of RNA 5-methyluridine sites using multi-scale convolutional neural network with BiLSTM.

As a prevalent RNA modification, 5-methyluridine (m5U) plays a critical role in diverse biological processes and disease pathogenesis. High-throughput identification of m5U typically relies on labor-intensive biochemical experiments using various sequencing-based techniques, which are not only time-consuming but also expensive. Consequently, there is a pressing need for more efficient and cost-effective computational methods to complement these high-throughput techniques. In this study, we present m5UMCB, a novel approach that harnesses a multi-scale convolutional neural network (CNN) in tandem with bidirectional long short-term memory (BiLSTM) to recognize m5U sites. Our method involves segmenting RNA sequences into smaller fragments based on a 3-mer length and subsequently mapping each fragment to a lower-dimensional vector representation using the global vectors for word representation (GloVe) technique. Through a series of multi-scale convolution and pooling operations, local features are extracted from RNA sequences and transformed into abstract, high-level features. The feature matrix is then inputted into a BiLSTM network, enabling the capture of contextual information and long-term dependencies within the sequence. Ultimately, a fully connected layer is employed to classify m5U sites. The validation results from 5-fold cross-validation (5-fold CV) test indicate that m5UMCB outperforms existing state-of-the-art predictive methods, demonstrating a 1.98% increase in the area under ROC curve (AUC) and significant improvements in relevant evaluation metrics. We are confident that m5UMCB will serve as a valuable tool for m5U prediction.

Predicting drug-induced liver injury using graph attention mechanism and molecular fingerprints.

Drug-induced liver injury (DILI) is a significant issue in drug development and clinical treatment due to its potential to cause liver dysfunction or damage, which, in severe cases, can lead to liver failure or even fatality. DILI has numerous pathogenic factors, many of which remain incompletely understood. Consequently, it is imperative to devise methodologies and tools for anticipatory assessment of DILI risk in the initial phases of drug development. In this study, we present DMFPGA, a novel deep learning predictive model designed to predict DILI. To provide a comprehensive description of molecular properties, we employ a multi-head graph attention mechanism to extract features from the molecular graphs, representing characteristics at the level of compound nodes. Additionally, we combine multiple fingerprints of molecules to capture features at the molecular level of compounds. The fusion of molecular fingerprints and graph features can more fully express the properties of compounds. Subsequently, we employ a fully connected neural network to classify compounds as either DILI-positive or DILI-negative. To rigorously evaluate DMFPGA's performance, we conduct a 5-fold cross-validation experiment. The obtained results demonstrate the superiority of our method over four existing state-of-the-art computational approaches, exhibiting an average AUC of 0.935 and an average ACC of 0.934. We believe that DMFPGA is helpful for early-stage DILI prediction and assessment in drug development.

Finless Porpoise Bycatch and Stranding along the Shandong Peninsula, China, Based on Public Reports from 2000 to 2018.

The Shandong Peninsula is located on the western coast of the Pacific and is adjacent to the Bohai Sea (BS) and the Yellow Sea (YS) to the east. The East Asian finless porpoise Neophocaena asiaeorientalis sunameri, a subspecies of the narrow-ridged finless porpoise N. asiaeorientalis, is the dominant cetacean resident along the Shandong Peninsula. However, there is insufficient monitoring data to determine the status of the cetacean species in this region. Based on the publicly available literature, media, and internet social website, this study investigated the spatial-temporal distribution of porpoise stranding and bycatch along the coast of the Shandong Peninsula. Data on over five hundred porpoises from two hundred reports between 2000 and 2018 were compiled and analyzed. Results showed that the bycatch and stranding of porpoises occurred widely across the peninsula throughout all months and increased rapidly between 2010 and 2017. The incidents were more frequent in the area where the BS and YS converged during the spring and early summer than in other seasons. The mean body length of bycaught porpoises was smaller than that of those found stranded. Fishing activities could be the principal cause of local finless porpoise incidents. However, limited data hindered a quantitative evaluation of the living conditions of finless porpoises in this area. Establishing a comprehensive monitoring system, which includes standardized reporting, rescue operations, and scientific research, is essential to finless porpoise protection along the Shandong Peninsula.

Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping method PlantServation.

Long-term field monitoring of leaf pigment content is informative for understanding plant responses to environments distinct from regulated chambers but is impractical by conventional destructive measurements. We developed PlantServation, a method incorporating robust image-acquisition hardware and deep learning-based software that extracts leaf color by detecting plant individuals automatically. As a case study, we applied PlantServation to examine environmental and genotypic effects on the pigment anthocyanin content estimated from leaf color. We processed >4 million images of small individuals of four Arabidopsis species in the field, where the plant shape, color, and background vary over months. Past radiation, coldness, and precipitation significantly affected the anthocyanin content. The synthetic allopolyploid A. kamchatica recapitulated the fluctuations of natural polyploids by integrating diploid responses. The data support a long-standing hypothesis stating that allopolyploids can inherit and combine the traits of progenitors. PlantServation facilitates the study of plant responses to complex environments termed "in natura".

scAAGA: Single cell data analysis framework using asymmetric autoencoder with gene attention.

In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating cellular heterogeneity and structure. However, analyzing scRNA-seq data remains challenging, especially in the context of COVID-19 research. Single-cell clustering is a key step in analyzing scRNA-seq data, and deep learning methods have shown great potential in this area. In this work, we propose a novel scRNA-seq analysis framework called scAAGA. Specifically, we utilize an asymmetric autoencoder with a gene attention module to learn important gene features adaptively from scRNA-seq data, with the aim of improving the clustering effect. We apply scAAGA to COVID-19 peripheral blood mononuclear cell (PBMC) scRNA-seq data and compare its performance with state-of-the-art methods. Our results consistently demonstrate that scAAGA outperforms existing methods in terms of adjusted rand index (ARI), normalized mutual information (NMI), and adjusted mutual information (AMI) scores, achieving improvements ranging from 2.8% to 27.8% in NMI scores. Additionally, we discuss a data augmentation technology to expand the datasets and improve the accuracy of scAAGA. Overall, scAAGA presents a robust tool for scRNA-seq data analysis, enhancing the accuracy and reliability of clustering results in COVID-19 research.

Congener-specific uptake and accumulation of bisphenols in edible plants: Binding to prediction of bioaccumulation by attention mechanism multi-layer perceptron machine learning model.

Plant accumulation of phenolic contaminants from agricultural soils can cause human health risks via the food chain. However, experimental and predictive information for plant uptake and accumulation of bisphenol congeners is lacking. In this study, the uptake, translocation, and accumulation of five bisphenols (BPs) in carrot and lettuce plants were investigated through hydroponic culture (duration of 168 h) and soil culture (duration of 42 days) systems. The results suggested a higher bioconcentration factor (BCF) of bisphenol AF (BPAF) in plants than that of the other four BPs. A positive correlation was found between the log BCF and the log Kow of BPs (R2carrot = 0.987, R2lettuce = 0.801, P < 0.05), while the log (translocation factor) exhibited a negative correlation with the log Kow (R2carrot = 0.957, R2lettuce = 0.960, P < 0.05). The results of molecular docking revealed that the lower binding energy of BPAF with glycosyltransferase, glutathione S-transferase, and cytochrome P450 (-4.34, -4.05, and -3.52 kcal/mol) would be responsible for its higher accumulation in plants. Based on the experimental data, an attention mechanism multi-layer perceptron (AM-MLP) model was developed to predict the BCF of eight untested BPs by machine learning, suggesting the relatively high BCF of bisphenol BP, bisphenol PH, and bisphenol TMC (BCFcarrot = 1.37, 1.50, 1.03; BCFlettuce = 1.02, 0.98, 0.67). The prediction of BCF for ever-increasing varieties of BPs by machine learning would reduce repetitive experimental tests and save resources, providing scientific guidance for the production and application of BPs from the perspective of priority pollutants.

DCAMCP: A deep learning model based on capsule network and attention mechanism for molecular carcinogenicity prediction.

The carcinogenicity of drugs can have a serious impact on human health, so carcinogenicity testing of new compounds is very necessary before being put on the market. Currently, many methods have been used to predict the carcinogenicity of compounds. However, most methods have limited predictive power and there is still much room for improvement. In this study, we construct a deep learning model based on capsule network and attention mechanism named DCAMCP to discriminate between carcinogenic and non-carcinogenic compounds. We train the DCAMCP on a dataset containing 1564 different compounds through their molecular fingerprints and molecular graph features. The trained model is validated by fivefold cross-validation and external validation. DCAMCP achieves an average accuracy (ACC) of 0.718 ± 0.009, sensitivity (SE) of 0.721 ± 0.006, specificity (SP) of 0.715 ± 0.014 and area under the receiver-operating characteristic curve (AUC) of 0.793 ± 0.012. Meanwhile, comparable results can be achieved on an external validation dataset containing 100 compounds, with an ACC of 0.750, SE of 0.778, SP of 0.727 and AUC of 0.811, which demonstrate the reliability of DCAMCP. The results indicate that our model has made progress in cancer risk assessment and could be used as an efficient tool in drug design.

Astaxanthin enhances the development of bovine cloned embryos by inhibiting apoptosis and improving DNA methylation reprogramming of pluripotency genes.

Low cloning efficiency limits the wide application of somatic cell nuclear transfer technology. Apoptosis and incomplete DNA methylation reprogramming of pluripotency genes are considered as the main causes for low cloning efficiency. Astaxanthin (AST), a powerfully antioxidative and antiapoptotic carotenoid, is recently shown to improve the development of early embryos, however, the potential role of AST during the development of cloned embryos remains unclear. This study displayed that treating cloned embryos with AST significantly increased the blastocyst rate and total blastocyst cell number in a concentration dependent manner, and also alleviated the damage of H2O2 to the development of cloned embryos. In addition, compared with the control group, AST significantly reduced the apoptotic cell number and rate in cloned blastocysts, and the significantly upregulated expression of anti-apoptotic gene Bcl2l1 and antioxidative genes (Sod1 and Gpx4) and downregulated transcription of pro-apoptotic genes (Bax, P53 and Caspase3) were observed in the AST group. Moreover, AST treatment facilitated DNA demethylation of pluripotency genes (Pou5f1, Nanog and Sox2), in accompany with the improved transcription levels of DNA methylation reprogramming genes (Tet1, Tet3, Dnmt1, Dnmt3a and Dnmt3b) in cloned embryos, and then, the significantly upregulated expression levels of embryo development related genes including Pou5f1, Nanog, Sox2 and Cdx2 were observed in comparison with the control group. In conclusion, these results revealed that astaxanthin enhanced the developmental potential of bovine cloned embryos by inhibiting apoptosis and improving DNA methylation reprogramming of pluripotency genes, and provided a promising approach to improve cloning efficiency.

Interactions between microplastics and contaminants: A review focusing on the effect of aging process.

Microplastics (MPs) in the environment are a major global concern due to their persistent nature and wide distribution. The aging of MPs is influenced by several processes including photodegradation, thermal degradation, biodegradation and mechanical fragmentation, which affect their interaction with contaminants. This comprehensive review aims to summarize the aging process of MPs and the factors that impact their aging, and to discuss the effects of aging on the interaction of MPs with contaminants. A range of characterization methods that can effectively elucidate the mechanistic processes of these interactions are outlined. The rate and extent of MPs aging are influenced by their physicochemical properties and other environmental factors, which ultimately affect the adsorption and aggregation of aged MPs with environmental contaminants. Pollutants such as heavy metals, organic matter and microorganisms have a tendency to accumulate on MPs through adsorption and the interactions between them impact their environmental behavior. Aging enhances the specific surface area and oxygen-containing functional groups of MPs, thereby affecting the mechanism of interaction between MPs and contaminants. To obtain a more comprehensive understanding of how aging affects the interactions, this review also provides an overview of the mechanisms by which MPs interact with contaminants. In the future, there should be further in-depth studies of the potential hazards of aged MPs in different environments e.g., soil, sediment, aquatic environment, and effects of their interaction with environmental pollutants on human health and ecology.

Improving wheat seedling quality through deep ploughing and soil compaction at sowing in lime concretion black soil.

The straw incorporation in lime concretion black soil compromises the emergence and quality of winter wheat seedlings in Huaibei Plain, China, lowering the potential of wheat productivity. To overcome the disadvantage, a two-year field experiment was conducted in 2017-18 and 2018-19 to investigate the effects of different tillage modes on seedling emergence and subsequent seedling growth, and final grain yield (GY) in winter wheat. The modes are rotary tillage with compaction after sowing (RCT), rotary tillage after deep ploughing (PT) and rotary tillage after deep ploughing with compaction after sowing (PCT), with the traditional rotary tillage (RT) method as the control. Compared to RT, greater soil moisture content (SMC) at the seedling stage was observed in deep ploughing or compaction treatment, and the highest SMC was achieved in PCT; the time of reaching the maximum number of seedlings was 1 d sooner in RCT or PT, and 3 d in PCT; the seedling number in RCT, PT and PCT was significantly increased by 32.6%, 34.5% and 61.5% respectively. The population size, shoot and root growth of winter wheat in ploughing mode was significantly enhanced than that of rotary treatment at the over-wintering stage; compared to no compaction after sowing, plant growth in compaction treatments was significantly promoted with greater plant population size and height of seedlings. At harvest, GY in RCT, PT and PCT was significantly improved by 5.87%, 10.8% and 16.4%, respectively, compared to RT and the highest GY was achieved in PCT by up to 8, 350.1 kg ha-1 due to the increased spike number. In conclusion, the seedling quality in the straw incorporation practice was improved through rotary after deep ploughing and compaction after sowing for lime concretion black soil in Huaibei Plain, China or a similar soil type.

A low-coverage 3' RNA-seq to detect homeolog expression in polyploid wheat.

Although allopolyploid species are common among natural and crop species, it is not easy to distinguish duplicated genes, known as homeologs, during their genomic analysis. Yet, cost-efficient RNA sequencing (RNA-seq) is to be developed for large-scale transcriptomic studies such as time-series analysis and genome-wide association studies in allopolyploids. In this study, we employed a 3' RNA-seq utilizing 3' untranslated regions (UTRs) containing frequent mutations among homeologous genes, compared to coding sequence. Among the 3' RNA-seq protocols, we examined a low-cost method Lasy-Seq using an allohexaploid bread wheat, Triticum aestivum. HISAT2 showed the best performance for 3' RNA-seq with the least mapping errors and quick computational time. The number of detected homeologs was further improved by extending 1 kb of the 3' UTR annotation. Differentially expressed genes in response to mild cold treatment detected by the 3' RNA-seq were verified with high-coverage conventional RNA-seq, although the latter detected more differentially expressed genes. Finally, downsampling showed that even a 2 million sequencing depth can still detect more than half of expressed homeologs identifiable by the conventional 32 million reads. These data demonstrate that this low-cost 3' RNA-seq facilitates large-scale transcriptomic studies of allohexaploid wheat and indicate the potential application to other allopolyploid species.

Predicting metabolite-disease associations based on auto-encoder and non-negative matrix factorization.

Metabolism refers to a series of orderly chemical reactions used to maintain life activities in organisms. In healthy individuals, metabolism remains within a normal range. However, specific diseases can lead to abnormalities in the levels of certain metabolites, causing them to either increase or decrease. Detecting these deviations in metabolite levels can aid in diagnosing a disease. Traditional biological experiments often rely on a lot of manpower to do repeated experiments, which is time consuming and labor intensive. To address this issue, we develop a deep learning model based on the auto-encoder and non-negative matrix factorization named as MDA-AENMF to predict the potential associations between metabolites and diseases. We integrate a variety of similarity networks and then acquire the characteristics of both metabolites and diseases through three specific modules. First, we get the disease characteristics from the five-layer auto-encoder module. Later, in the non-negative matrix factorization module, we extract both the metabolite and disease characteristics. Furthermore, the graph attention auto-encoder module helps us obtain metabolite characteristics. After obtaining the features from three modules, these characteristics are merged into a single, comprehensive feature vector for each metabolite-disease pair. Finally, we send the corresponding feature vector and label to the multi-layer perceptron for training. The experiment demonstrates our area under the receiver operating characteristic curve of 0.975 and area under the precision-recall curve of 0.973 in 5-fold cross-validation, which are superior to those of existing state-of-the-art predictive methods. Through case studies, most of the new associations obtained by MDA-AENMF have been verified, further highlighting the reliability of MDA-AENMF in predicting the potential relationships between metabolites and diseases.

MPFFPSDC: A multi-pooling feature fusion model for predicting synergistic drug combinations.

Drug combination therapies are common practice in the treatment of cancer, but not all combinations result in synergy. As traditional screening approaches are restricted in their ability to uncover synergistic drug combinations, computer-aided medicine is becoming a increasingly prevalent in this field. In this work, a predictive model of potential interactions between drugs named MPFFPSDC is presented, which can maintain the symmetry of drug inputs and eliminate inconsistencies in predictive results caused by different drug inputting sequences or positions. The experimental results show that MPFFPSDC outperforms comparative models in major performance indicators and exhibits better generalization for independent data. Furthermore, the case study demonstrates that our model can capture molecular substructures that contribute to the synergistic effect of two drugs. These results indicate that MPFFPSDC not only offers strong predictive performance, but also has good model interpretability that may provide new insights for the study of drug interaction mechanisms and the development of new drugs.

Characteristic features of statistical models and machine learning methods derived from pest and disease monitoring datasets.

While many studies have used traditional statistical methods when analysing monitoring data to predict future population dynamics of crop pests and diseases, increasing studies have used machine learning methods. The characteristic features of these methods have not been fully elucidated and arranged. We compared the prediction performance between two statistical and seven machine learning methods using 203 monitoring datasets recorded over several decades on four major crops in Japan and meteorological and geographical information as the explanatory variables. The decision tree and random forest of machine learning were found to be most efficient, while regression models of statistical and machine learning methods were relatively inferior. The best two methods were better for biased and scarce data, while the statistical Bayesian model was better for larger dataset sizes. Therefore, researchers should consider data characteristics when selecting the most appropriate method.

Astaxanthin protects oocyte maturation against cypermethrin-induced defects in pigs.

Cypermethrin (CYP), a pyrethroid insecticide, exerts the detrimental effect on the reproductive system, while astaxanthin (AST), a xanthophyll carotenoid, possesses the powerful antioxidant property and can protect oocyte maturation. However, the toxicity of CYP and the protective role of AST against CYP during oocyte maturation remain unclear. Here, porcine oocytes were applied to investigate the potential effects and underlying mechanisms of CYP and AST during oocyte maturation. This work demonstrated that CYP significantly decreased oocyte maturation rate and subsequent embryo development in a dose-dependent manner (P < 0.05). And, CYP obviously induced the overproduction of reactive oxygen species and the reduction of glutathione content by downregulating the expression of redox genes in oocytes (P < 0.05). Moreover, CYP significantly caused oocyte DNA damage and disturbed the function of endoplasmic reticulum by altering the transcription of DNA damage repair and endoplasmic reticulum stress related genes (P < 0.05). Whereas CYP-exposed oocytes were treated with AST, these defects caused by CYP were significantly ameliorated (P < 0.05). In conclusion, this study demonstrated that CYP exerted the toxic effect on porcine oocytes, while AST effectively alleviated CYP-induced defects. This work provides a potential strategy to prevent pesticide toxicity and protect oocyte maturation in mammalian reproduction.

Phthalate esters and their metabolites in paired soil-crop systems from farmland in major provinces of eastern China: Pollution characteristics and implications for human exposure.

The extensive application of phthalate esters (PAEs) as plasticizers has raised considerable concern regarding their environmental load, but the associated occurrence of PAE metabolites has often been ignored. The soil-plant system is a vital source of human exposure to PAEs via crop intake. Here, paired soil-plant samples were collected from eastern China to investigate the occurrence characteristics of seven PAE congeners and two primary monoester phthalate metabolites (mPAEs) in farmland. The detection frequencies of PAEs and mPAEs in the investigated soil-plant systems were 100 %. The total concentrations of PAEs in the collected soil and plant samples ranged from 0.07 to 1.83 mg/kg (dw) and from 3.9 to 24 mg/kg (dw), respectively. Moreover, di-(2-ethylhexyl) phthalate, diisobutyl phthalate and di-n-butyl phthalate were the predominant PAE congeners in the farmlands of eastern China, collectively accounting for >90 % of the total concentration of PAEs. In addition, the total concentrations of the two mPAEs were markedly higher in plant samples (49 ng/g dw to 549 ng/g dw) than in soil samples (3 ng/g dw to 22 ng/g dw), indicating that PAEs are readily metabolized in plants. The hazard index (HI) values of all PAEs in all crops were <1, demonstrating that the risks of PAEs in the crops were acceptable. However, the daily intake of mPAEs from the consumption of cabbage was higher than or comparable to that of some PAEs (such as di-n-octyl phthalate). This highlights the importance of taking metabolites into consideration in further environmental investigations and risk assessments of PAEs.