A methodological framework for identifying potential sources of soil heavy metal pollution based on machine learning: a case study in the Yangtze Delta, China

It is a great challenge to identify the many and varied sources of soil heavy metal pollution. Often little information is available regarding the anthropogenic factors and enterprises that could potentially pollute soils. In this study we use freely available geographical data from a search engine in conjunction with machine learning methodologies to identify and classify potentially polluting enterprises in the Yangtze Delta, China. The data were classified into 31 separate and five integrated industry types by five different machine learning approaches. Multinomial naive Bayesian methods achieved an accuracy of 86.5% and Kappa coefficient of 0.82 and were used to classify the geographic data from more than 250 000 enterprises. The relationship between the different industry classes and measurements of soil cadmium and mercury concentrations was explored using bivariate local Moran's I analysis. The analysis revealed areas where different industry classes had led to soil pollution. In the case of cadmium, elevated concentrations also occurred in some areas because of natural sources. This study provides a new approach to investigate the interaction between anthropogenic pollution and natural sources of soil heavy metals to inform pollution control and planning decisions regarding the location of industrial sites

Critical Roles of microRNA-141-3p and CHD8 in Hypoxia/Reoxygenation-Induced Cardiomyocyte Apoptosis

Background: Cardiovascular diseases are currently the leading cause of death in humans. The high mortality of cardiac diseases is associated with myocardial ischemia and reperfusion (I/R). Recent studies have reported that microRNAs (miRNAs) play important roles in cell apoptosis. However, it is not known yet whether miR-141-3p contributes to the regulation of cardiomyocyte apoptosis. It has been well established that in vitro hypoxia/reoxygenation (H/R) model can follow in vivo myocardial I/R injury. This study aimed to investigate the effects of miR-141-3p and CHD8 on cardiomyocyte apoptosis following H/R. Results: We found that H/R remarkably reduces the expression of miR-141-3p but enhances CHD8 expression both in mRNA and protein in H9c2 cardiomyocytes. We also found either overexpression of miR-141-3p by transfection of miR-141-3p mimics or inhibition of CHD8 by transfection of small interfering RNA (siRNA) significantly decrease cardiomyocyte apoptosis induced by H/R. Moreover, miR-141-3p interacts with CHD8. Furthermore, miR-141-3p and CHD8 reduce the expression of p21. Conclusion: MiR-141-3p and CHD8 play critical roles in cardiomyocyte apoptosis induced by H/R. These studies suggest that miR-141-3p and CHD8 mediated cardiomyocyte apoptosis may offer a novel therapeutic strategy against myocardial I/R injury-induced cardiovascular diseases

Innate Immune Response of Human Alveolar Macrophages during Influenza A Infection

Alveolar macrophages (AM) are one of the key cell types for initiating inflammatory and immune responses to influenza virus in the lung. However, the genome-wide changes in response to influenza infection in AM have not been defined. We performed gene profiling of human AM in response to H1N1 influenza A virus PR/8 using Affymetrix HG-U133 Plus 2.0 chips and verified the changes at both mRNA and protein levels by real-time RT-PCR and ELISA. We confirmed the response with a contemporary H3N2 influenza virus A/New York/238/2005 (NY/238). To understand the local cellular response, we also evaluated the impact of paracrine factors on virus-induced chemokine and cytokine secretion. In addition, we investigated the changes in the expression of macrophage receptors and uptake of pathogens after PR/8 infection. Although macrophages fail to release a large amount of infectious virus, we observed a robust induction of type I and type III interferons and several cytokines and chemokines following influenza infection. CXCL9, 10, and 11 were the most highly induced chemokines by influenza infection. UV-inactivation abolished virus-induced cytokine and chemokine response, with the exception of CXCL10. The contemporary influenza virus NY/238 infection of AM induced a similar response as PR/8. Inhibition of TNF and/or IL-1β activity significantly decreased the secretion of the proinflammatory chemokines CCL5 and CXCL8 by over 50%. PR/8 infection also significantly decreased mRNA levels of macrophage receptors including C-type lectin domain family 7 member A (CLEC7A), macrophage scavenger receptor 1 (MSR1), and CD36, and reduced uptake of zymosan. In conclusion, influenza infection induced an extensive proinflammatory response in human AM. Targeting local components of innate immune response might provide a strategy for controlling influenza A infection-induced proinflammatory response in vivo

Dynamic Modeling and Analysis of Demagnetizing Rotor of Permanent Magnet Synchronous Motor

This study performs a dynamic modeling and analysis of the axial uniform demagnetization rotor of a motor to investigate the demagnetization peculiar for permanent magnet synchronous motor (PMSM). First, the air-gap change in the motor is analyzed by constructing a dual coordinate system of stator and rotor, and the unbalanced magnetic pull (UMP) model under uniform demagnetization and eccentricity is constructed. Second, combined with the UMP and the self-gravity, a dynamic model of the rotor under a uniform demagnetization and eccentricity is established. Lastly, the accuracy of the mathematical model of UMP under uniform demagnetization and eccentricity is confirmed by the mutual corroboration of Maxwell simulation and MATLAB calculation; and based on the dynamics model, the dynamic characteristics of the rotor system under different degrees of uniform demagnetization are studied. This study provides a theoretical basis for the accurate demagnetization fault diagnosis and vibration control of PMSMs in the future

Exponential Synchronization of a Class of N-Coupled Complex Partial Differential Systems with Time-Varying Delay

This paper is concerned with the exponential synchronization for a class of N-coupled complex partial differential systems (PDSs) with time-varying delay. The synchronization error dynamic of the PDSs is defined in the q-dimensional spatial domain. To achieve synchronization, we added a linear feedback controller. A sufficient condition is derived to ensure the exponential synchronization of the proposed networks using the Lyapunov–Krasovskii stability approach and matrix inequality technology. The proposed system has broad applications. Two example applications are presented in the final section of this paper to verify the proposed theoretical result

Current Status and Temporal Trend of Potentially Toxic Elements Pollution in Agricultural Soil in the Yangtze River Delta Region: A Meta-Analysis

Potentially toxic elements (PTEs) pollution in the agricultural soil of China, especially in developed regions such as the Yangtze River Delta (YRD) in eastern China, has received increasing attention. However, there are few studies on the long-term assessment of soil pollution by PTEs over large regions. Therefore, in this study, a meta-analysis was conducted to evaluate the current state and temporal trend of PTEs pollution in the agricultural land of the Yangtze River Delta. Based on a review of 118 studies published between 1993 and 2020, the average concentrations of Cd, Hg, As, Pb, Cr, Cu, Zn, and Ni were found to be 0.25 mg kg−1, 0.14 mg kg−1, 8.14 mg kg−1, 32.32 mg kg−1, 68.84 mg kg−1, 32.58 mg kg−1, 92.35 mg kg−1, and 29.30 mg kg−1, respectively. Among these elements, only Cd and Hg showed significant accumulation compared with their background values. The eastern Yangtze River Delta showed a relatively high ecological risk due to intensive industrial activities. The contents of Cd, Pb, and Zn in soil showed an increasing trend from 1993 to 2000 and then showed a decreasing trend. The results obtained from this study will provide guidance for the prevention and control of soil pollution in the Yangtze River Delta

Heavy Metal Pollution Delineation Based on Uncertainty in a Coastal Industrial City in the Yangtze River Delta, China

Assessing heavy metal pollution and delineating pollution are the bases for evaluating pollution and determining a cost-effective remediation plan. Most existing studies are based on the spatial distribution of pollutants but ignore related uncertainty. In this study, eight heavy-metal concentrations (Cr, Pb, Cd, Hg, Zn, Cu, Ni, and Zn) were collected at 1040 sampling sites in a coastal industrial city in the Yangtze River Delta, China. The single pollution index (PI) and Nemerow integrated pollution index (NIPI) were calculated for every surface sample (0–20 cm) to assess the degree of heavy metal pollution. Ordinary kriging (OK) was used to map the spatial distribution of heavy metals content and NIPI. Then, we delineated composite heavy metal contamination based on the uncertainty produced by indicator kriging (IK). The results showed that mean values of all PIs and NIPIs were at safe levels. Heavy metals were most accumulated in the central portion of the study area. Based on IK, the spatial probability of composite heavy metal pollution was computed. The probability of composite contamination in the central core urban area was highest. A probability of 0.6 was found as the optimum probability threshold to delineate polluted areas from unpolluted areas for integrative heavy metal contamination. Results of pollution delineation based on uncertainty showed the proportion of false negative error areas was 6.34%, while the proportion of false positive error areas was 0.86%. The accuracy of the classification was 92.80%. This indicated the method we developed is a valuable tool for delineating heavy metal pollution

Gut Bacterial Diversity of Insecticide-Susceptible and Insecticide-Resistant <i>Megalurothrips usitatus</i> (Thysanoptera: Thripidae) and Elucidation of Their Putative Functional Roles

The gut bacterial microbiota of insects plays a crucial role in physiological, metabolic, and innate immune processes. In the current study, the gut bacterial communities of an insecticide-susceptible (IS), and a resistant (IR) population of a major legume pest, Megalurothrips usitatus (Bagnall), were evaluated. The 16S rDNA V3 + V4 regions of M. usitatus infected with Beauveria brongniartii along with the intestinal flora of both populations were sequenced based on a High-throughput sequencing platform. Toxicological bioassays revealed that the IR population exhibited resistance to acetamiprid and B. brongniartii isolate SB010 at levels of 138.0-fold and 55.6-fold higher, respectively, compared to the IS population. Through 16S High-throughput sequencing, the results indicate that both resistant populations, as well as B. brongniartii infestation, reduce the number of species of M. usitatus gut microbes. Using KEGG function prediction, it was found that most intestinal bacteria were involved in various metabolic activities, and the abundance of resistant populations was higher than that of sensitive populations. The bacteria in the gut of M. usitatus are mainly involved in various metabolic activities to achieve the degradation of B. brongniartii. This study provides valuable insights into the interaction between gut bacteria, insecticide resistance, and Beauveria. brongniartii infection in Megalurothrips usitatus, which can help inform future pest control strategies