Annual atrazine residue estimation in Chinese agricultural soils by integrated modeling of machine learning and mechanism-based models.

This study presents a comprehensive approach to estimating annual atrazine residues in China's agricultural soils, integrating machine learning algorithms and mechanism-based models. First, machine learning was used to predict essential parameters influencing atrazine's adsorption, degradation, and dispersivity of solute transport. The results demonstrated that soil organic matter was the most important input variable for predicting adsorption and degradation; clay content was the primary variable for predicting dispersivity. The SHapley Additive exPlanations (SHAP) contribution of various soil properties on target variables were also analyzed to reveal whether each input variable has a positive, negative, or complex effect. Subsequently, these parameters inform the construction of a detailed model across 23,692 subregions of China, with a 20 km × 20 km resolution. The model considered regional variations and soil layer heterogeneity, including rainfall, soil depth-specific properties, and parameters for adsorption, degradation, and dispersivity. Utilizing the convection-dispersion equations and the Phydrus, the model simulated atrazine's transport and degradation patterns across diverse soil environments after applying 250 mL of atrazine (40%) per Chinese mu. The outcomes provided a spatially explicit distribution of atrazine residues, specifying that the arid areas have the highest residual risk, followed by the Northeast, Southwest, and Southeast. Atrazine levels may exceed national drinking water standards at 50 cm depth in Inner Mongolia, the Qinghai-Tibet Plateau, and the Jungar Basin. This study's integrative approach may also offer valuable insights and tools for evaluating residues of various pesticides and herbicides in agricultural soils.

Prediction of atrazine degradation in soil based on XGBoost model.

We established the optimal model by using the automatic machine learning method to predict the degradation efficiency of herbicide atrazine in soil, which could be used to assess the residual risk of atrazine in soil. We collected 494 pairs of data from 49 published articles, and selected seven factors as input features, including soil pH, organic matter content, saturated hydraulic conductivity, soil moisture, initial concentration of atrazine, incubation time, and inoculation dose. Using the first-order reaction rate constant of atrazine in soil as the output feature, we established six models to predict the degradation efficiency of atrazine in soil, and conducted comprehensive analysis of model performance through linear regression and related evaluation indicators. The results showed that the XGBoost model had the best performance in predicting the first-order reaction rate constant (k). Based on the prediction model, the feature importance ranking of each factor was in an order of soil moisture > incubation time > pH > organic matter > initial concentration of atrazine > saturated hydraulic conductivity > inoculation dose. We used SHAP to explain the potential relationship between each feature and the degradation ability of atrazine in soil, as well as the relative contribution of each feature. Results of SHAP showed that time had a negative contribution and saturated hydraulic conductivity had a positive contribution. High values of soil moisture, initial concentration of atrazine, pH, inoculation dose and organic matter content were generally distributed on both sides of SHAP=0, indicating their complex contributions to the degradation of atrazine in soil. The XGBoost model method combined with the SHAP method had high accuracy in predicting the performance and interpretability of the k model. By using machine learning method to fully explore the value of historical experimental data and predict the degradation efficiency of atrazine using environmental parameters, it is of great significance to set the threshold for atrazine application, reduce the residual and diffusion risks of atrazine in soil, and ensure the safety of soil environment.

Predicting bacterial transport through saturated porous media using an automated machine learning model.

Escherichia coli, as an indicator of fecal contamination, can move from manure-amended soil to groundwater under rainfall or irrigation events. Predicting its vertical transport in the subsurface is essential for the development of engineering solutions to reduce the risk of microbiological contamination. In this study, we collected 377 datasets from 61 published papers addressing E. coli transport through saturated porous media and trained six types of machine learning algorithms to predict bacterial transport. Eight variables, including bacterial concentration, porous medium type, median grain size, ionic strength, pore water velocity, column length, saturated hydraulic conductivity, and organic matter content were used as input variables while the first-order attachment coefficient and spatial removal rate were set as target variables. The eight input variables have low correlations with the target variables, namely, they cannot predict target variables independently. However, using the predictive models, input variables can effectively predict the target variables. For scenarios with higher bacterial retention, such as smaller median grain size, the predictive models showed better performance. Among six types of machine learning algorithms, Gradient Boosting Machine and Extreme Gradient Boosting outperformed other algorithms. In most predictive models, pore water velocity, ionic strength, median grain size, and column length showed higher importance than other input variables. This study provided a valuable tool to evaluate the transport risk of E.coli in the subsurface under saturated water flow conditions. It also proved the feasibility of data-driven methods that could be used for predicting other contaminants' transport in the environment.

Bioremediation of Petroleum-Contaminated Soils with Biosurfactant-Producing Degraders Isolated from the Native Desert Soils.

A crude oil spill in 2014 resulted in extensive soil contamination of the hyper arid Evrona Nature Reserve in Israel's Negev Desert. The contaminated soils became highly hydrophobic, threatening the existence of plants in the habitat. We hypothesized that bioaugmenting the soil with indigenous biosurfactant-producing, hydrocarbon-degrading bacteria (HDB) would accelerate the reduction in the soil's hydrophobicity. We aimed to isolate and characterize biosurfactant-producing HDBs from the desert-contaminated soil and test if they can be used for augmenting the soil. Twelve hydrocarbon-degrading strains were isolated, identified as Pseudomonas, and classified as biosurfactants "producing" and "nonproducing". Inoculating 109 CFU/g of "producing" strains into the polluted soil resulted in a 99.2% reduction in soil hydrophobicity within seven days. At the same time, nonproducing strains reduced hydrophobicity by only 17%, while no change was observed in the untreated control. The microbial community in the inoculated soil was dominated by the introduced strains over 28 days, pointing to their persistence. Rhamnolipid biosynthesis gene rhlAB remained persistent in soil inoculated with biosurfactants, indicating in situ production. We propose that the success of the treatment is due to the use of inoculum enriched from the polluted soil.

Quantifying bacterial concentration in water and sand media during flow-through experiments using a non-invasive, real-time, and efficient method.

Monitoring the dynamics of bacteria in porous media is of great significance to understand the bacterial transport and the interplay between bacteria and environmental factors. In this study, we reported a non-invasive, real-time, and efficient method to quantify bioluminescent bacterial concentration in water and sand media during flow-through experiments. First, 27 column experiments were conducted, and the bacterial transport was monitored using a real-time bioluminescent imaging system. Next, we quantified the bacterial concentration in water and sand media using two methods-viable count and bioluminescent count. The principle of the bioluminescent count in sand media was, for a given bioluminescence image, the total number of bacteria was proportionally allocated to each segment according to its bioluminescence intensity. We then compared the bacterial concentration for the two methods and found a good linear correlation between the bioluminescent count and viable count. Finally, the effects of porous media surface coating, pore water velocity, and ionic strength on the bioluminescent count in sand media were investigated, and the results showed that the bioluminescence counting accuracy was most affected by surface coating, followed by ionic strength, and was hardly affected by pore water velocity. Overall, the study proved that the bioluminescent count was a reliable method to quantify bacterial concentration in water (106 to 2 × 108 cell mL-1) or sand media (5 × 106-5 × 108 cell cm-3). This approach also offers a new way of thinking for in situ bacterial enumeration in two-dimensional devices such as 2D flow cells, microfluidic devices, and rhizoboxes.

A Novel Regulation Strategy of Solid Electrolyte Interphase Based on Anion-Solvent Coordination for Magnesium Metal Anode.

Magnesium (Mg) metal anode is a highly desirable candidate among various high energy density metal anodes, possessing higher volumetric capacity and better safety characteristic compared to lithium metal. However, most Mg salts in conventional Mg electrolytes easily react with Mg metal to form blocking layers, leading to inferior reversibility of Mg plating/stripping. Here, a stable Mg2+ -conducting solid electrolyte interphase (SEI) is successfully constructed on Mg metal anode by regulating the molecular-orbital-energy-level toward an aluminum(III)-centered anion Mg salt through anion-solvent coordination. Of which, the LUMO energy level of perfluorinated pinacolatoaluminate (Al(O2 C2 (CF3 )4 )2 - , abbreviated as FPA) anion has been adjusted by coordinating with solvent molecule (tetrahydrofuran) for facilitating the formation of advantageous SEI. The existence of SEI formed by FPA anion greatly improves the reversibility and long-term stability of Mg plating/stripping process. More importantly, based on this aluminum(III)-centered Mg electrolyte, the Mo6 S8 /Mg batteries can achieve a fantastic cycle performance of 9000 cycles, proving the beneficial effect of SEI on the cycling stability of Mg battery system. These findings open up a promising avenue to construct stable and compatible SEI on Mg metal anode, and lay significant foundations for the successful development of rechargeable Mg metal batteries.

Increased Salivary microRNAs That Regulate DJ-1 Gene Expression as Potential Markers for Parkinson's Disease.

Small molecule RNAs (microRNAs) are a kind of endogenous, stable, and noncoding RNA molecule that can regulate the expression of target genes such as DJ-1 at the posttranscriptional level. This study aimed to detect the expression of salivary microRNAs and to discover their value as a salivary potential biomarker for Parkinson's disease (PD). Through a case-control study, RT-qPCR technology was used to detect the expression of miR-874 and miR-145-3p in the saliva of 30 PD patients and 30 healthy volunteers. Then we compared the differences in the expression levels of salivary miR-874 and miR-145-3p between the PD group and the control group and analyzed the correlation between the expression of salivary miR-874 and miR-145-3p in terms of age, gender, disease condition, and disease course. We found that salivary miR-874 and miR-145-3p were both positively expressed in the PD group and control group, and their expression in the PD group was higher than that in the control group. The expression of salivary miRNA-874 and miR-145-3p had no clear correlation to age, gender, total RNA concentrations in saliva, the score of UPDRSII, UPDRSIII, olfactory test scale, MMSE, MoCA, Hohn-Yahr stage and disease course. In conclusion, in the PD group and the control group with positive expression, the expression levels of miR-874 and miR-145-3p in the PD group were higher than those in the control group. The detection of miR-874 and miR-145-3p expression in saliva can be used as an auxiliary biomarker for PD.

The Internet Hospital Plus Drug Delivery Platform for Health Management During the COVID-19 Pandemic: Observational Study.

BACKGROUND: Widespread access to the internet has boosted the emergence of online hospitals. A new outpatient service called "internet hospital plus drug delivery" (IHDD) has been developed in China, but little is known about this platform. OBJECTIVE: The aim of this study is to investigate the characteristics, acceptance, and initial impact of IHDD during the outbreak of COVID-19 in a tertiary hospital in South China. METHODS: The total number of and detailed information on online prescriptions during the first 2 months after work resumption were obtained. Patients' gender, age, residence, associated prescription department, time of prescription, payment, and drug delivery region were included in the analysis. RESULTS: A total of 1380 prescriptions were picked up or delivered between March 2 and April 20, 2020. The largest group of patients were 36-59 years old (n=680, 49.3%), followed by the 18-35 years age category (n=573, 41.5%). In total, 39.4% (n=544) of the patients chose to get their medicine by self-pickup, while 60.6% (n=836) preferred to receive their medicine via drug delivery service. The top five online prescription departments were infectious diseases (n=572, 41.4%), nephrology (n=264, 19.1%), endocrinology (n=145, 10.5%), angiocardiopathy (n=107, 7.8%), and neurology (n=42, 3%). Of the 836 delivered prescriptions, 440 (52.6%) were sent to Guangdong Province (including 363 [43.4%] to Shenzhen), and 396 (47.4%) were sent to other provinces in China. CONCLUSIONS: The IHDD platform is efficient and convenient for various types of patients during the COVID-19 crisis. Although offline visits are essential for patients with severe conditions, IHDD can help to relieve pressure on hospitals by reducing an influx of patients with mild symptoms. Further efforts need to be made to improve the quality and acceptance of IHDD, as well as to regulate and standardize the management of this novel service.

Spectroscopic techniques for quantitative characterization of Cu (II) and Hg (II) complexation by dissolved organic matter from lake sediment in arid and semi-arid region.

Dissolved organic matter (DOM) was extracted from six sediment samples in arid and semi-arid region, which was characterized by fluorescence excitation-emission matrices (EEMs). The results showed that four fluorescent peak, fulvic-like (peak A), humic-like (peak C) and two tryptophan-like (peaks B and D), were identified in lake sediment DOM. Fluorescence quenching titration showed that peaks B and D were quenched gradually by adding additional Cu (II) and Hg (II), whereas humic-like substances had no systematic trend of the change of fluorescence intensity. Increasing fluorescence intensity value of humic-like substances can also be found. The modified Stern-Volmer model was used to calculate conditional stability constants (logK) and the percent of fluorophores (f %) which participate in the complexation between DOM and Cu (II), and Hg (II). The results showed that DOM-Cu (II) and DOM-Hg (II) complexes had higher logK values of 4.21-5.23 and the logK values of DOM-Cu (II) are much larger than the corresponding values for Hg (II). Peak B showed relatively low logK and high f % values than those of peak D. Different pollution sources which are mainly obtained from the upstream industrial wastewater, domestic sewage and return water of farmland irrigation tend to affect the stability constants and complexing capacities of Cu (II) and Hg (II).

Effects of co-inoculation with arbuscular mycorrhizal fungi and rhizobia on soybean growth as related to root architecture and availability of N and P.

Soybean plants can form tripartite symbiotic associations with rhizobia and arbuscular mycorrhizal (AM) fungi, but little is known about effects of co-inoculation with rhizobia and AM fungi on plant growth, or their relationships to root architecture as well as nitrogen (N) and phosphorus (P) availability. In the present study, two soybean genotypes contrasting in root architecture were grown in a field experiment to evaluate relationships among soybean root architecture, AMF colonization, and nodulation under natural conditions. Additionally, a soil pot experiment in greenhouse was conducted to investigate the effects of co-inoculation with rhizobia and AM fungi on soybean growth, and uptake of N and P. Our results indicated that there was a complementary relationship between root architecture and AMF colonization in the field. The deep root soybean genotype had greater AMF colonization at low P, but better nodulation with high P supply than the shallow root genotype. A synergistic relationship dependent on N and P status exists between rhizobia and AM fungi on soybean growth. Co-inoculation with rhizobia and AM fungi significantly increased soybean growth under low P and/or low N conditions as indicated by increased shoot dry weight, along with plant N and P content. There were no significant effects of inoculation under adequate N and P conditions. Furthermore, the effects of co-inoculation were related to root architecture. The deep root genotype, HN112, benefited more from co-inoculation than the shallow root genotype, HN89. Our results elucidate new insights into the relationship between rhizobia, AM fungi, and plant growth under limitation of multiple nutrients, and thereby provides a theoretical basis for application of co-inoculation in field-grown soybean.