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[Display omitted] • Soil fertility degradation was classified into severe, moderate and slight clusters. • Soil fertility drastically decreased in grassland induced by mining disturbance. • While it was slightly degraded in cropland due to conservation and naturally recovering. • TN and SOM are main factors affecting the classification of soil fertility degradation. The degradation of soil fertility in mining areas poses great risks to agricultural production and the ecological environment, and has increasingly become a worldwide concern. In this study, soil assessments were conducted to evaluate the spatial and temporal variations of soil fertility indicators and characteristics of soil fertility degradation under different types of land use and mining disturbances in a coalfield on the Loess Plateau of China, where soil fertility degradation caused by mining activities has become a serious environmental issue. Soil samples (depth: 0–20 cm) were collected twice from the same 50 sampling points in 2017 and 2019. The sampling points covered three land use types (cropland, shrubland, and grassland) and three years of mining disturbance (2011, 2013, and 2016). Soil organic matter (SOM), total nitrogen (TN), soil-available phosphorus, soil-available potassium, and the fine soil particles in topsoils were measured for each sample. The spatial distributions of the properties and degradation of soil fertility were analysed using kriging interpolation, and the degree to which fertility degraded was analysed via density-based spatial clustering of applications with noise (DBSCAN) and validated using SoftMax and random forest algorithms. The study revealed that the intensity of the degradation of soil fertility could be classified into three clusters (i.e. severely degraded, moderately degraded, and slightly degraded), as indicated by the DBSCAN results, and based on the variation in soil fertility indicators. Validation using random forest and SoftMax suggested that the accuracy of clustering was over 95%. Land use types and coal mining years significantly affected the degree of degradation, and total nitrogen and soil organic matter had the most noticeable impacts on the classification of soil fertility degradation. [ABSTRACT FROM AUTHOR]

Non-alcoholic fatty liver disease (NAFLD) affects obesity-associated metabolic syndrome, which exhibits hepatic steatosis, insulin insensitivity and glucose intolerance. Previous studies indicated that hepatic microRNAs (miRs) play critical roles in the development of NAFLD. In this study, we aim to explore the pathophysiological role of miR-194 in obesity-mediated metabolic dysfunction. Our findings show that the high fat diet or palmitic acid treatment significantly increase hepatic miR-194 levels in vivo and in vitro. Silence of miR-194 protects palmitic acid-induced inflammatory response in cultured hepatocytes, and attenuates structural disorders, lipid deposits and inflammatory response in fatty liver. MiR-194 inhibitor also improves glucose and insulin intolerance in obese mice. Through dual luciferase assay, we demonstrate that miR-194 directly binds to FXR/ Nr1h4 3′-UTR, and inhibits gene expression of FXR/ Nr1h4 . Furthermore, overexpression of miR-194 downregulates FXR/ Nr1h4 in cultured hepatocytes, but miR-194 inhibitor reversely increases FXR/ Nr1h4 expression in obese mouse liver tissues. On the contrast, silence of FXR/ Nr1h4 abolishes the hepatic benefits in obese mice treated with miR-194 inhibitor. Present study provides a novel finding that suppression of miR-194 attenuates dietary-induced NAFLD via upregulation of FXR/ Nr1h4 . The findings suggest miR-194/FXR are potential diagnostic markers and therapeutic targets for NAFLD. [ABSTRACT FROM AUTHOR]

• Spatial distribution of total nitrogen (TN) showed moderate spatial dependence. • TN content is significantly different among each type of land use. • TN is generally more spatially affected by land use types than soil erosion intensity. • Available K, soil organic matter, NDVI, and elevation mainly affect soil TN content. Soil total nitrogen (TN) is one of the most important nutrients for plant and crop growth. It is essential to estimate the spatial distribution of TN to evaluate soil productivity, land reclamation efficiency, and agricultural management. This study was performed to investigate the spatial distribution of TN and its influencing factors in a coalfield in the Loess Plateau, China. A total of 143 topsoil samples were collected from five land use types and different slope positions. Classical statistical and geostatistical methods were used to quantify the spatial variation in TN. Redundancy analysis (RDA) was used to detect correlations between environmental factors and TN, whereas the random forest (RF) algorithm and LightGBM were applied to simulate the correlations. The results showed that the degree of spatial dependence ranged from 31.7%–45.2%, indicating moderate spatial variability in TN. Land use type significantly affected TN. The highest TN contents were found in the conservation tillage lands, followed by conventional tillage land, shrub land, grassland, and coal mining areas. The spatial distribution of the TN was also influenced by the slope position. The sites located downslope had higher TN than those located on the upper and middle slopes. RDA indicated that available potassium (AK) and soil organic matter (SOM) significantly affected the spatial distribution of TN. It was also demonstrated by LightGBM and RF that the TN content could be simulated by AK, SOM, normalised vegetation index, elevation, and available phosphorus. These results are helpful in understanding the effects of anthropogenic and natural factors on soil TN in coalfield areas. [ABSTRACT FROM AUTHOR]

Precise delineation of river networks is important for accurate hydrological and flood modelling. Whilst remote sensing (RS) has showed great potential in monitoring hydrological changes over space and time, the existing RS-based methods extract river networks based on local morphologies and seldom take into account the overall hydrological connectivity of the rivers. The existing methods also commonly neglect the effect of seasonal variation of water surfaces and the existence of temporary water bodies, which deteriorate the precision of positioning river networks. To address these challenges, a new two-stage method is developed to E xtract spatiotemporal variation of water surfaces based on M ulti-temporal remote sensing I magery and D elineate connected river networks with improved accuracy (EMID method for short) using a path tracking technique. The EMID method delineates connected river networks using (a) multi-temporal imagery and a Random Forest model to synoptically map the location and extent of water surfaces under different hydrological conditions, and (b) an optimization algorithm to find the best river paths based on water-occurrence frequency. Four drainage basins with various river morphologies are considered to validate EMID. Comparing with alternative methods, the EMID method consistently produces river network results with improved accuracy in terms of stream location, river coverage and network connectivity. Unlabelled Image • A new two-stage RS-based method is proposed to extract connected rivers. • The new method performs well in maintaining river positional accuracy. • The method effectively reduces temporary water's interference. [ABSTRACT FROM AUTHOR]

• LSTM can well simulate the forecast capacity of a complicated water quality model. • LSTM is expected to be promising in the future water quality forecast. • LSTM trained by outputs of a complicated water quality model is mechanism-guided. The utilization of complicated water quality models is the primary approach used to forecast water quality. These models, however, are not easy to employ because of constraints, such as data limitation, extensive computations, and future boundary conditions. Long short-term memory (LSTM) can overcome such constraints; however, its applications in water quality forecast have rarely been explored. In this study, the ability of LSTM to simulate the forecast capacity of a complicated water quality model (i.e., environmental fluid dynamics code, EFDC) is investigated. First, the EFDC is run to produce a long-term (12 years) time series of six water quality variables. These variables are intrinsically associated with equations embedded in the EFDC that represent the dynamics of the simulated system. The LSTM is developed to forecast the concentration of Chlorophyll a 1–31 d ahead of time using six water quality variables. The generated data are thereafter employed to train a number of LSTMs with different model structures (combinations of input variables, numbers of hidden layers, and lag times). The LSTM performances are evaluated by the Nash–Sutcliffe efficiency coefficient, and random forest is applied to identify the key drivers of LSTM performance. The results show that many LSTMs could achieve an acceptable performance level. Chlorophyll a , water temperature, and total phosphorus are identified as the key drivers of LSTM performances, which are consistent with limnological theories. The number of hidden layers and lag time practically have no impact on the LSTM performance. It is thereby confirmed that an LSTM with a simple structure could simulate the forecast capacity of EFDC. The results also reveal that the mechanism-guided LSTM in our study may capture certain mechanism features. The LSTM is thus expected to be a promising approach for water quality forecast. [ABSTRACT FROM AUTHOR]