Accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of arid regions. As a branch of artificial intelligence, machine learning acquires new knowledge through self-learning and continuously improves its own performance. The purpose of this study is to combine Sentinel-2 Multispectral Imager (MSI) data and MSI-derived covariates with measured soil salinity data and to apply three machine learning algorithms in modeling to estimate and map the soil salinity in the study sample area. According to the convenient transportation conditions, the study area and sampling quadrat were set up, and the 5-point method was used to collect the soil mixed samples, and 160 soil mixed samples were collected. Kennard–Stone (K–S) algorithm was used for sample classification, 70% for modeling and 30% for verification. The machine learning algorithm uses Support Vector Machines (SVM), Artificial Neural Network (ANN), and Random Forest (RF). The results showed that (1) the average reflectance of each band of the MSI data ranged from 0.21–0.28. According to the spectral characteristics corresponding to different soil electrical conductivity (EC) levels (1.07–79.6 dS m−1), the spectral reflectance of salinized soil in the MSI data ranged from 0.09–0.35. (2) The correlation coefficient between the MSI data and MSI-derived covariates and soil EC was moderate, and the correlation between certain MSI data sets and soil EC was not significant. (3) The SVM soil EC estimation model established with the MSI data set attained a higher performance and accuracy (R2 = 0.88, root mean square error (RMSE) = 4.89 dS m−1, and ratio of the performance to the interquartile range (RPIQ) = 1.96, standard error of the laboratory measurements to the standard error of the predictions (SEL/SEP) = 1.11) than those attained with the soil EC estimation models established with the RF and ANN models. (4) We applied the SVM soil EC estimation model to map the soil salinity in the study area, which showed that the farmland with higher altitudes discharged a large amount of salt to the surroundings due to long-term irrigation, and the secondary salinization of the farmland also caused a large amount of salt accumulation. This research provides a scientific basis for the simulation of soil salinization scenarios in arid areas in the future.
The xenoestrogen bisphenol A (BPA) is a synthetic endocrine disrupting chemical, having the potential to increase the risk of hormone-dependent ovarian cancer. Thus, a deeper understanding of the molecular and cellular mechanisms is urgently required in the novel cell models of ovarian cancer which express estrogen receptors. To understand the possible mechanisms underlying the effects of BPA, human ovarian adenocarcinoma SKOV3 cells were exposed to BPA (10 or 100 nM) or 0.1% DMSO for 24 h, and then global gene expression profile was determined by high-throughput RNA sequencing. Also, enrichment analysis was carried out to find out relevant functions and pathways within which differentially expressed genes were significantly enriched. Transcriptomic analysis revealed 94 differential expression genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses indicated that these genes related to tumorigenesis and metastasis. Further studies were carried out to validate the results of functional annotation, which indicated that BPA (10 and 100 nM) increased migration and invasion as well as induced epithelial to mesenchymal transitions in SKOV3 and A2780 cells. Accordingly, environmentally relevant-dose BPA activated the canonical Wnt signaling pathway. Our study first comprehensively analyzed the possible mechanisms underlying the effects of BPA on ovarian cancer. Environmentally relevant doses of BPA modulated the gene expression profile, promoted epithelial to mesenchymal transition progress via canonical Wnt signaling pathway of ovarian cancer.
Abstract Oil and gas exploration in the South China Sea (SCS) has garnered global attention recently; however, uncertainty regarding the accurate number of offshore platforms in the SCS, let alone their detailed spatial distribution and dynamic change, may lead to significant misjudgment of the true status of offshore hydrocarbon production in the region. Using both fresh and archived space-borne images with multiple resolutions, we enumerated the number, distribution, and annual rate of increase of offshore platforms across the SCS. Our results show that: ( 1 ) a total of 1082 platforms are present in the SCS, mainly located in shallow-water; and ( 2 ) offshore oil/gas exploitation in the SCS is increasing in intensity and advancing from shallow to deep water, and even to ultra-deep-water. Nevertheless, our findings suggest that oil and gas exploration in the SCS may have been over-estimated by one-third in previous reports. However, this overestimation does not imply any amelioration of the potential for future maritime disputes, since the rate of increase of platforms in disputed waters is twice that in undisputed waters.
Animal waste from concentrated swine farms is widely considered to be a source of environmental pollution and the introduction of veterinary antibiotics in animal manure to ecosystems is rapidly becoming a major public health concern. A housefly larvae (Musca domestica) vermireactor has been increasingly adopted for swine manure value-added bioconversion and pollution control, but few studies have investigated its efficiency on antibiotic attenuation during manure vermicomposting. In this study we explored the capacity and related attenuation mechanisms of antibiotic degradation and its linkage with waste reduction by field sampling during a typical cycle (6 days) of full-scale larvae manure vermicomposting. Nine antibiotics were dramatically removed during the 6-day vermicomposting process, including tetracyclines, sulfonamides and fluoroquinolones. Of these, oxytetracycline and ciprofloxacin exhibited the greater reduction rate of 23.8 and 32.9 mg m−2, respectively. Environmental temperature, pH and total phosphorus were negatively linked to the level of residual antibiotics, while organic matter, total Kjeldahl nitrogen, microbial respiration intensity and moisture exhibited a positive effect. Pyrosequencing data revealed that the dominant phyla related to Firmicutes, Bacteroidetes and Proteobacteria accelerated manure biodegradation likely through enzyme catalytic reactions, which may enhance antibiotic attenuation during vermicomposting.
Abstract Due to the adverse impact of DDTs on ecosystems and humans, a full fate assessment deems a comprehensive study on their occurrence in soils over a large region. Through a sampling campaign across China, we measured the concentrations, enantiomeric fractions (EFs), compound-specific carbon isotope composition of DDT and its metabolites, and the microbial community in related arable soils. The geographically total DDT concentrations are higher in eastern than western China. The EFs and δ 13 C of o,p’ -DDT in soils from western China show smaller deviations from those of racemic/standard compound, indicating the DDT residues there mainly result from atmospheric transport. However, the sources of DDT in eastern China are mainly from historic application of technical DDTs and dicofol. The inverse dependence of o,p’ -DDT and p,p’ -DDE on temperature evidences the transformation of parent DDT to its metabolites. Initial usage, abiotic parameters and microbial communities are found to be the main factors influencing the migration and transformation of DDT isomers and their metabolites in soils. In addition, a prediction equation of DDT concentrations in soils based on stepwise multiple regression analysis is developed. Results from this study offer insights into the migration and transformation pathways of DDTs in Chinese arable soils, which will allow data-based risk assessment on their use.
Abstract Soil organic matter (SOM) plays a critical role in terrestrial ecosystem functioning and is closely related to many global issues like soil fertility, soil health and climate regulation. Therefore, obtaining accurate information on the spatial distribution of SOM and its potential controlling factors is of global interest. However, this remains a great challenge since SOM is affected by numerous natural and anthropogenic factors and usually showed strong heterogeneity. In this study, we collected a total of 16,580 surface soil (0–20 cm) samples from the farmland throughout Jiangxi Province. And the Random Forest (RF), Cubist and gradient‐boosted models were compared and used to define the factor which is most associated with SOM. Then the ordinary kriging (OK) and machine learning‐ordinary co‐kriging (ML‐COK) were used to map SOM. We found that on average, 30.86 g kg −1 SOM was present in farmland soil of Jiangxi Province. Anthropogenic activities strongly affected SOM level, with five of the top 10 most important factors are anthropogenic related. The straw return amount was proved to have the largest importance (31.46%) for modelling SOM and a significant ( p < 0.001) positive relationship between SOM content and the amount of straw returned to farmland was detected. Additionally, returning straw improved crop production. Soil derived from the Quaternary Subred Sand has the highest SOM content (37.82 g kg −1 ). Crop rotation also improved SOM content and the rice‐bean rotation system has the highest SOM content (34.27 g kg −1 ). With the best performance, the RF algorithm ( R 2 = 0.49, RMSE = 6.77 g kg −1 ) was selected to identify the primary control of SOM and integrated with COK, which we termed as ML‐COK, to map the SOM in the farmland of Jiangxi Province. ML‐COK outperformed OK method for mapping the SOM in farmland of Jiangxi Province with R 2 of 0.351 and Lin's concordance correlation coefficient of 0.549. Farmland distributed in the central part of the province had high SOM content. In contrast, farmland in the north, south and east parts had relatively low SOM. Our study offers new insight for mapping soil properties, identifying potential factors driving variation in SOM, and also provides valuable information for making more reasonable and environmentally friendly farmland management measures.
Moving bed biofilm reactor (MBBR) is considered as a promising technology for wastewater treatment owing to the high biomass retention and low cost. In this study, the performance of using MBBR for partial denitrification (PD) was investigated. Denitrifying biofilm was successfully formed after 40 days with the biomass and nitrite reduction rate of 40.83 mg VSS/g carriers and 51.52 mg N/(gVSS·h), respectively. Morphology analysis by scanning electron microscope (SEM) showed that the biofilm surface was dominant by cocci, filamentous bacteria, and extracellular polymeric substances (EPS). Investigation about the influencing factors of PD found that the optimal COD/NO3−-N and pH for efficient nitrite production (nitrate to nitrite ratio: 96.49%) was 3 and 9, respectively. Moreover, Saccharimonadales was proved to be dominant functional microbes in the constructed PD systems with different influent conditions because its relative abundance exhibited good correlation with the nitrite accumulation. By analyzing the biofilm characteristics under different conditions, PD was observed to mainly occur in the range of 300–700 μm inside the biofilm, where most of the dissolved oxygen was consumed. This study confirmed the feasibility and superior performance of PD-MBBR system.
Abstract Soil function degradation threatens the sustainable management of soil resources and soil organic matter (SOM) is a vital and important factor. Powerful measuring tools will become very important, especially in areas where data are poor or absent. The archive: China Soil Visible and Near Infrared (vis–NIR) Spectroscopy Library (CSSL) could help providea solution for less costly and fast measuring of SOM. The aim of this article was to compare SOM prediction performance according to three strategies: i) general global partial least squares regression (PLSR) using CSSL with and without spiking samples; ii) memory‐based learning (MBL) using CSSL with and without spiking samples; and iii) general PLSR using only spiking samples to predict soil organic matter in the target area. When using spiked subsets, we also investigated the prediction performance of the extra‐weighted (several copies) subsets. A series of spiking subsets were randomly selected from the total spiking samples, which were selected by conditioned Latin hypercube sampling (cLHS) from the target sites. We calculated only the mean squared Euclidean distance (msd) between the estimates density function (pds) of the principal components (PCs) of vis–NIR spectroscopy from the validation dataset and spiking subsets and statistically inferred the optimal sampling set size to be 30. Our study showed that global PLSR using CSSL spiked with the statistically optimal local samples can achieve higher predicted performance [with a mean root mean square error (RMSE) of 5.75]. MBL spiked with five extra‐weighted optimal spiking samples achieved the best accuracy with an RMSE of 3.98, an R 2 of 0.70, a bias of 0.04, and an LCCC of 0.81. The msd is a simple and effective method to determine an adequate spiking set size using only vis–NIR data. These accurate predictions demonstrated the usefulness of statistically representative spiking and MBL for advanced large soil spectral libraries for SOM determination, which is currently lacking at large soil spectral libraries in use.
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