Three-parameter modeling of the soil sorption of acetanilide and triazine herbicide derivatives

A machine learning based approach for estimating site-specific partition coefficient Kd of organic compounds: Application to nonionic pesticides

The partitioning coefficient K_d for a specific compound and location is not only a key input parameter of fate and transport models, but also critical in estimating the safety environmental concentration threshold. In order to reduce the uncertainty caused by non-linear interactions among environmental factors, machine learning based models for predicting K_d were developed in this work based on literature datasets of nonionic pesticides including molecular descriptors, soil properties, and experimental settings. The equilibrium concentration (C_e) values were specifically included for the reason that a varied range of K_d corresponding to a given C_e occurred in a real environment. By transforming 466 isotherms reported in the literature, 2618 paired equilibrium concentrations of liquid-solid (C_e-Q_e) data points were obtained. Results of SHapley Additive exPlanations revealed that soil organic carbon, C_e, and cavity formation were the most important. The distance-based applicability domain analysis was conducted for the 27 most frequently used pesticides with 15952 pieces of soil information from the HWSD-China dataset by setting three C_e scenarios (i.e., 10, 100, and 1000 μg L^-1). It was revealed the groups of compounds showing log K_d < 0.06 and log K_d > 1.19 were composed mostly of those with log K_ow of -0.800 and 5.50, respectively. When log K_d varied between 0.100 and 1.00, it was impacted by interactions among soil types, molecular descriptors, and C_e comprehensively, which accounted for 55% of the total 2618 calculations. It could be concluded that site-specific models developed in this work are necessary and practicable for the environmental risk assessment and management of nonionic organic compounds.

Bioinformatics analysis of the potential mechanisms of Alzheimer's disease induced by exposure to combined triazine herbicides

BACKGROUND

The development of Alzheimer's disease (AD) is promoted by a combination of genetic and environmental factors. Notably, combined exposure to triazine herbicides atrazine (ATR), simazine (SIM), and propazine (PRO) may promote the development of AD, but the mechanism is unknown.

AIM

To study the molecular mechanism of AD induced by triazine herbicides.

METHODS

Differentially expressed genes (DEGs) of AD patients and controls were identified. The intersectional targets of ATR, SIM, and PRO for possible associations with AD were screened through network pharmacology and used for gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analysis. The binding potentials between the core targets and herbicides were validated by molecular docking and molecular dynamics.

RESULTS

A total of 1,062 DEGs were screened between the AD patients and controls, which identified 148 intersectional targets of herbicides causing AD that were screened by network pharmacology analysis. GO and KEGG enrichment analysis revealed that cell cycling and cellular senescence were important signalling pathways. Finally, the core targets EGFR, FN1, and TYMS were screened and validated by molecular docking and molecular dynamics.

CONCLUSION

Our results suggest that combined exposure to triazine herbicides might promote the development of AD, thereby providing new insights for the prevention of AD.

Presence and distribution of triazine herbicides and their effects on microbial communities in the Laizhou Bay, Northern China

This study investigated the distribution of triazine herbicides in the Laizhou Bay, China and found that the total concentrations of triazine herbicides in the seawater and sediments were 111.15-234.85 ng/L and 0.902-4.661 μg/kg, respectively. Triazine herbicides especially ametryn, atrazine, and simazine were negatively correlated with prokaryote diversity in the seawater. While ametryn, desethylatrazine and desisopropylatrazine had positively significant effects on eukaryotes Dinophyceae, Bacillariophyta, and Cercozoa in the sediments. Moreover, the degree of fragmentation of eukaryotic networks increased dramatically with the increasing numbers of removed nodes, but prokaryotic networks did not change with the decrease of nodes. In addition, the stability analysis and neutral community models revealed that eukaryotes were more sensitive to triazine herbicides than prokaryotes. These results suggest that triazine herbicides might affect the structure and interactions of microbial communities. Therefore, more attentions should be paid to the ecological risk of triazine herbicides in marine ecosystems.

Coloured chemical image-based models for the prediction of soil sorption of herbicides

Herbicides with high soil sorption profiles constitute important organic pollutants leading to detrimental environmental effects, particularly due to prolonged use.

Aug-MIA-QSPR modeling of the soil sorption of carboxylic acid herbicides

Soil sorption, described as logK OC (the logarithm of the soil/water partition coefficient normalized to organic carbon), was modeled using the augmented multivariate image analysis applied to quantitative structure-property relationship method for a series of 11 carboxylic acid herbicides. The statistical model was found to be highly predictive and reliable to estimate logK OC of other persistent organic pollutants in the soil, which are analogues of the carboxylic acids used in the QSPR model. The QSPR model derived from images corresponding to the chemical structures of the 11 herbicides is superior to the uniparameter model based on the octanol/water partition coefficient (logP) and, in addition, a pattern recognition model was built using principal component analysis. This model allowed clustering and separating compounds with low/moderate soil sorption from those with moderate/high soil sorption (compounds with the aryloxy function) using the second principal component.