Comprehensive assessment of health and ecological risk of cadmium in agricultural soils across China: A tiered framework.

Soil cadmium (Cd) contamination has been increasingly serious in agricultural land across China, posing unexpected risks to human health concerning crop safety and terrestrial ecosystems. This study collected Cd concentration data from 3388 soil sites in agricultural regions. To assess the Cd risk to crop safety, a comprehensive sampling investigation was performed to develop reliable Soil Plant Transfer (SPT) model. Eco-toxicity tests with representative soils and organism was conducted to construct the Species Sensitivity Distribution (SSD) for ecological risk assessment. Then, a tiered framework was applied based on Accumulation index, deterministic method (Hazard quotient), and probabilistic assessment (Monte Carlo and Joint Probability Curve). The results revealed the widespread Cd enrichment in agricultural soils, mainly concentrated in Central, Southern, and Southwest China. Risk assessments demonstrated the greater risks related to crop safety, while the ecological risks posed by soil Cd were manageable. Notably, agricultural soils in southern regions of China exhibited more severe risks to both crop safety and soil ecosystem, compared to other agricultural regions. Furthermore, tiered methodology proposed here, can be adapted to other trace elements with potential risks to crop safety and terrestrial ecosystem.

Identifying multiple soil pollutions of potentially contaminated sites based on multi-gate mixture-of-experts network.

With the rapid increase in the amount and sources of big data, using big data and machine learning methods to identify site soil pollution has become a research hotspot. However, previous studies that used basic information of sites as pollution identification indexes mainly have problems of low accuracy and efficiency when conducting complex model predictions for multiple soil pollution types. In this study, we collected the environmental data of 199 sites in 6 typical industries involving heavy metal and organic pollution. After feature fusion and selection, 10 indexes based on pollution sources and pathways were used to establish the soil pollution identification index system. The Multi-gate Mixture-of-Experts network (MMoE) were constructed to carry out the multi-tasks of soil heavy metals, VOCs and SVOCs pollution identification simultaneously. The SHAP framework was used to reveal the importance of pollution identification indexes on the multiple outputs of MMoE and obtain their driving factors. The results showed that the accuracies of MMoE model were 0.600, 0.783 and 0.850 for soil heavy metals, VOCs and SVOCs pollution identifications, respectively, which were 0-20 % higher than their accuracies of BP neural networks of single tasks. The indexes of raw material containing organic compounds, enterprise scale, soil pollution traces and industry types have the different significant importance on site soil pollutions. This study proposed a more efficient and accurate method to identify site soil pollutions and their driving factors, which offers a step towards realizing intelligent identification and risk control of site soil pollution globally.

Feature fusion improves performance and interpretability of machine learning models in identifying soil pollution of potentially contaminated sites.

Owing to the rapid development of big data technology, use of machine learning methods to identify soil pollution of potentially contaminated sites (PCS) at regional scales and in different industries has become a research hot spot. However, due to the difficulty in obtaining key indexes of site pollution sources and pathways, current methods have problems such as low accuracy of model predictions and insufficient scientific basis. In this study, we collected the environmental data of 199 PCS in 6 typical industries involving heavy metal and organic pollution. Then, 21 indexes based on basic information, potential for pollution from product and raw material, pollution control level, and migration capacity of soil pollutants were used to established the soil pollution identification index system. We fused the original indexes into the new feature subset with 11 indexes through the method of consolidation calculation. The new feature subset was then used to train machine learning models of random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP), and tested to determine whether it improved the accuracy and precision of soil pollination identification models. The results of correlation analysis showed that the four new indexes created by feature fusion have the correlation with soil pollution is similar to the original indexes. The accuracies and precisions of three machine learning models trained on the new feature subset were 67.4%- 72.9% and 72.0%- 74.7%, which were 2.1%- 2.5% and 0.3%- 5.7% higher than these of the models trained on original indexes, respectively. When the PCS were divided into typical heavy metal and organic pollution sites according to the enterprise industries, the accuracy of the model trained on the two datasets for identifying soil heavy metal and organic pollution were significantly improve to approximately 80%. Owing to the imbalance in positive and negative samples in the prediction of soil organic pollution, the precisions of soil organic pollution identification models were 58%- 72.5%, which were significantly lower than their accuracies. According to the factors analysis based on the model interpretability of SHAP, most of the indexes of basic information, potential for pollution from product and raw material, and pollution control level had different degrees of impact on soil pollution. However, the indexes of migration capacity of soil pollutants had the least effect in the classification task of soil pollution identification of PCS. Among the indexes, traces of soil pollution, industrial utilization years/start-up time, pollution control risk scores and enterprise scale having the greatest effects on soil pollution with the mean SHAP values of 0.17-0.36, which reflected their contribution rate on soil pollution and could help to optimize the current index scoring of the technical regulation for identifying site soil pollution. This study provides a new technical method to identify soil pollution based on big data and machine learning methods, in addition to providing a reference and scientific basis for environmental management and soil pollution control of PCS.

Derivation of Soil Criteria of Cadmium for Safe Rice Production Applying Soil-Plant Transfer Model and Species Sensitivity Distribution.

Widespread soil contamination is hazardous to agricultural products, posing harmful effects on human health through the food chain. In China, Cadmium (Cd) is the primary contaminant in soils and easily accumulates in rice, the main food for the Chinese population. Therefore, it is essential to derive soil criteria to safeguard rice products by assessing Cd intake risk through the soil-grain-human pathway. Based on a 2-year field investigation, a total of 328 soil-rice grain paired samples were collected in China, covering a wide variation in soil Cd concentrations and physicochemical properties. Two probabilistic methods used to derive soil criteria are soil-plant transfer models (SPT), with predictive intervals, and species sensitivity distribution (SSD), composed of soil type-specific bioconcentration factor (BCF, Cd concentration ratio in rice grain to soil). The soil criteria were back-calculated from the Chinese food quality standard. The results suggested that field data with a proper Cd concentration gradient could increase the model accuracy in the soil-plant transfer system. The derived soil criteria based on soil pH were 0.06-0.11, 0.33-0.59, and 1.51-2.82 mg kg-1 for protecting 95%, 50% and 5% of the rice safety, respectively. The soil criteria with soil pH further validated the soil as being safe for rice grains.

Integrated Analysis of MicroRNA and mRNA Expression Profiles in the Fat Bodies of MbMNPV-Infected Helicoverpa armigera.

MicroRNAs (miRNAs), are a novel class of gene expression regulators, that have been found to participate in regulating host-virus interactions. However, the function of insect-derived miRNAs in response to virus infection is poorly understood. We analyzed miRNA expression profiles in the fat bodies of Helicoverpa armigera (H. armigera) infected with Mamestra brassicae multiple nucleopolyhedroviruses (MbMNPV). A total of 52 differentially expressed miRNAs (DEmiRNAs) were filtered out through RNA-seq analysis. The targets of 52 DEmiRNAs were predicted and 100 miRNA-mRNA interaction pairs were obtained. The predicted targets of DEmiRNAs were mainly enriched in the Wnt signaling pathway, phagosome, and mTOR signaling pathway, which are related to the virus infection. Real-time PCR was used to verify the RNA sequencing results. ame-miR-317-3p, mse-miR-34, novel1-star, and sfr-miR-6094-5p were shown to be involved in the host response to MbMNPV infection. Results suggest that sfr-miR-6094-5p can negatively regulate the expression of four host genes eIF3-S7, CG7583, CG16901, and btf314, and inhibited MbMNPV infection significantly. Further studies showed that RNAi-mediated knockdown of eIF3-S7 inhibited the MbMNPV infection. These findings suggest that sfr-miR-6094-5p inhibits MbMNPV infection by negatively regulating the expression of eIF3-S7. This study provides new insights into MbMNPV and H. armigera interaction mechanisms.

High-level expression and immunogenicity of porcine circovirus type 2b capsid protein without nuclear localization signal expressed in Hansenula polymorpha.

Currently, porcine circovirus type 2b (PCV2b) is the dominant PCV2 genotype causing postweaning multisystemic wasting disease (PMWS) in pigs worldwide. Efforts have been made to develop various recombinant capsid proteins of PCV2b used in vaccines against PCV2b. However, the nuclear localization signal (NLS) of PCV2b capsid protein (CP) was found to inhibit the expression of the whole length capsid protein in E.coli. Here, we expressed a NLS-deleted capsid protein (ΔCP) of PCV2b in Hansenula polymorpha based on the capsid protein of PCV2b strain Y-7 isolated in China. Comparatively, the ΔCP was expressed at a higher level than the CP. The purified ΔCP could self-assemble into virus like particles (VLPs) with similar morphology of the VLPs formed by CP. The purified ΔCP could be recognized by the anti-sera derived from the mice immunized by inactivated PCV2b particles. Furthermore, it induced higher levels of PCV2b specific antibodies than the purified CP in mice. These results showed that the ΔCP, a recombinant PCV2b capsid protein without nuclear localization signal sequence, could be efficiently expressed in Hansenula polymorpha, and used as a candidate antigen for the development of PCV2b vaccines.

Single immunization with a recombinant multiple-epitope protein induced protection against FMDV type Asia 1 in cattle.

To develop recombinant epitope vaccines against the foot-and-mouth disease virus (FMDV) serotype Asia 1, genes encoding six recombinant proteins (A1-A6) consisting of different combinations of B-cell and T-cell epitopes from VP1 capsid protein (VP1) of FMDV were constructed. These proteins were expressed in Escherichia coli and used to immunize animals. Our results showed that A6 elicited higher titers of neutralizing antibodies after single inoculation in guinea pigs than did the other five recombinant proteins, as determined by micro-neutralization tests. In addition, a strong lymphocyte proliferation response and Th1 type immunity were observed in splenocytes from the mice immunized with A6. Further tests carried out in cattle demonstrated that a single inoculation with A6 generated comparable levels of neutralizing antibodies as inactivated vaccine and protected 4 of 5 cattle against challenge with FMDV type Asia 1. Our results suggest that A6 might be a promising recombinant vaccine against FMDV type Asia 1 in cattle.