Cytosolic GDH1 degradation restricts protein synthesis to sustain tumor cell survival following amino acid deprivation.

The mTORC1 pathway plays key roles in regulating various biological processes, including sensing amino acid deprivation and driving expression of ribosomal protein (RP)-coding genes. In this study, we observed that depletion of glutamate dehydrogenase 1 (GDH1), an enzyme that converts glutamate to α-ketoglutarate (αKG), confers resistance to amino acid deprivation on kidney renal clear cell carcinoma (KIRC) cells. Mechanistically, under conditions of adequate nutrition, GDH1 maintains RP gene expression in a manner dependent on its enzymatic activity. Following amino acid deprivation or mTORC1 inhibition, GDH1 translocates from mitochondria to the cytoplasm, where it becomes ubiquitinated and degraded via the E3 ligase RNF213. GDH1 degradation reduces intracellular αKG levels by more than half and decreases the activity of αKG-dependent lysine demethylases (KDMs). Reduced KDM activity in turn leads to increased histone H3 lysine 9 and 27 methylation, further suppressing RP gene expression and preserving nutrition to support cell survival. In summary, our study exemplifies an economical and efficient strategy of solid tumor cells for coping with amino acid deficiency, which might in the future be targeted to block renal carcinoma progression.

Capturing RNA-protein interaction via CRUIS.

No RNA is completely naked from birth to death. RNAs function with and are regulated by a range of proteins that bind to them. Therefore, the development of innovative methods for studying RNA-protein interactions is very important. Here, we developed a new tool, the CRISPR-based RNA-United Interacting System (CRUIS), which captures RNA-protein interactions in living cells by combining the power of CRISPR and PUP-IT, a novel proximity targeting system. In CRUIS, dCas13a is used as a tracker to target specific RNAs, while proximity enzyme PafA is fused to dCas13a to label the surrounding RNA-binding proteins, which are then identified by mass spectrometry. To identify the efficiency of CRUIS, we employed NORAD (Noncoding RNA activated by DNA damage) as a target, and the results show that a similar interactome profile of NORAD can be obtained as by using CLIP (crosslinking and immunoprecipitation)-based methods. Importantly, several novel NORAD RNA-binding proteins were also identified by CRUIS. The use of CRUIS facilitates the study of RNA-protein interactions in their natural environment, and provides new insights into RNA biology.

Quantitative Phosphoproteomics Reveals System-Wide Phosphorylation Network Altered by Spry in Mouse Mammary Stromal Fibroblasts.

Understanding the fundamental role of the stroma in normal development and cancer progression has been an emerging focus in recent years. The receptor tyrosine kinase (RTK) signaling pathway has been reported playing critical roles in regulating the normal and cancer microenvironment, but the underlying mechanism is still not very clear. By applying the quantitative phosphoproteomic analysis of Sprouty proteins (SPRYs), generic modulators of RTK signaling and deleted mouse mammary fibroblasts, we quantified a total of 11,215 unique phosphorylation sites. By contrast, 554 phosphorylation sites on 425 proteins had SPRY-responsive perturbations. Of these, 554 phosphosites, 362 sites on 277 proteins, were significantly increased, whereas 192 sites on 167 proteins were decreased. Among the regulated proteins, we identified 31 kinases, 7 phosphatases, and one phosphatase inhibitor that were not systematically characterized before. Furthermore, we reconstructed a phosphorylation network centered on RTK signaling regulated by SPRY. Collectively, this study uncovered a system-wide phosphorylation network regulated by SPRY, providing an additional insight into the complicated RTK signaling pathways involved in the mammary gland microenvironment.

Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils.

This study investigated the abilities of pre-processing, feature selection and machine-learning methods for the spectroscopic diagnosis of soil arsenic contamination. The spectral data were pre-processed by using Savitzky-Golay smoothing, first and second derivatives, multiplicative scatter correction, standard normal variate, and mean centering. Principle component analysis (PCA) and the RELIEF algorithm were used to extract spectral features. Machine-learning methods, including random forests (RF), artificial neural network (ANN), radial basis function- and linear function- based support vector machine (RBF- and LF-SVM) were employed for establishing diagnosis models. The model accuracies were evaluated and compared by using overall accuracies (OAs). The statistical significance of the difference between models was evaluated by using McNemar's test (Z value). The results showed that the OAs varied with the different combinations of pre-processing, feature selection, and classification methods. Feature selection methods could improve the modeling efficiencies and diagnosis accuracies, and RELIEF often outperformed PCA. The optimal models established by RF (OA = 86%), ANN (OA = 89%), RBF- (OA = 89%) and LF-SVM (OA = 87%) had no statistical difference in diagnosis accuracies (Z < 1.96, p < 0.05). These results indicated that it was feasible to diagnose soil arsenic contamination using reflectance spectroscopy. The appropriate combination of multivariate methods was important to improve diagnosis accuracies.

Monitoring arsenic contamination in agricultural soils with reflectance spectroscopy of rice plants.

The objective of this study was to explore the feasibility and to investigate the mechanism for rapidly monitoring arsenic (As) contamination in agricultural soils with the reflectance spectra of rice plants. Several data pretreatment methods were applied to improve the prediction accuracy. The prediction of soil As contents was achieved by partial least-squares regression (PLSR) using laboratory and field spectra of rice plants, as well as linear regression employing normalized difference spectral index (NDSI) calculated from fild spectra. For laboratory spectra, the optimal PLSR model for predicting soil As contents was achieved using Savitzky-Golay smoothing (SG), first derivative and mean center (MC) (root-mean-square error of prediction (RMSEP)=14.7 mg kg(-1); r=0.64; residual predictive deviation (RPD)=1.31). For field spectra, the optimal PLSR model was also achieved using SG, first derivative and MC (RMSEP=13.7 mg kg(-1); r=0.71; RPD=1.43). In addition, the NDSI with 812 and 782 nm obtained a prediction accuracy with r=0.68, RMSEP=13.7 mg kg(-1), and RPD=1.36. These results indicated that it was feasible to monitor the As contamination in agricultural soils using the reflectance spectra of rice plants. The prediction mechanism might be the relationship between the As contents in soils and the chlorophyll-a/-b contents and cell structure in leaves or canopies of rice plants.

Elevated nuclear PIGL disrupts the cMyc/BRD4 axis and improves PD-1 blockade therapy by dampening tumor immune evasion.

To improve the efficacy of lenvatinib in combination with programmed death-1 (PD-1) blockade therapy for hepatocellular carcinoma (HCC), we screened the suppressive metabolic enzymes that sensitize HCC to lenvatinib and PD-1 blockade, thus impeding HCC progression. After analysis of the CRISPR‒Cas9 screen, phosphatidylinositol-glycan biosynthesis class L (PIGL) ranked first in the positive selection list. PIGL depletion had no effect on tumor cell growth in vitro but reprogrammed the tumor microenvironment (TME) in vivo to support tumor cell survival. Specifically, nuclear PIGL disrupted the interaction between cMyc/BRD4 on the distant promoter of target genes and thus decreased the expression of CCL2 and CCL20, which are involved in shaping the immunosuppressive TME by recruiting macrophages and regulatory T cells. PIGL phosphorylation at Y81 by FGFR2 abolished the interaction of PIGL with importin α/ß1, thus retaining PIGL in the cytosol and facilitating tumor evasion by releasing CCL2 and CCL20. Clinically, elevated nuclear PIGL predicts a better prognosis for HCC patients and presents a positive correlation with CD8 + T-cell enrichment in tumors. Clinically, our findings highlight that the nuclear PIGL intensity or the change in PIGL-Y81 phosphorylation should be used as a biomarker to guide lenvatinib with PD-1 blockade therapy.

AF9 targets acetyl-modified STAT6 to diminish purine metabolism and accelerate cell apoptosis during metastasis.

Cell migration and invasion are two important steps for tumour metastasis, and involved the behaviors including metabolism remodeling and anti-apoptosis. However, it's still elusive that cancer cells how to antagonize apoptosis during tumour metastasis. In this study, we observed that super elongation complex (SEC) subunit AF9 depletion exacerbated cell migration and invasion but reduced the apoptosis during invasive migration. Mechanically, AF9 targeted acetyl (Ac)-STAT6 at lysine (K) 284 and blocked STAT6 transactivation on the promoter of such genes involved in regulating purine metabolism and metastasis, in turn induced apoptosis of suspended cells. Of note, AcSTAT6-K284 was not induced by IL4 signaling but decreased by limited nutrition which triggered SIRT6 to remove acetyl group at STAT6-K284. The functional experiments proved that AcSTAT6-K284 attenuated cell migration and invasion depending on AF9 expression level. Animal metastatic study further confirmed the AF9/AcSTAT6-K284 axis existed and blocked kidney renal clear cell carcinoma (KIRC) metastasis. In clinical, both AF9 expression and AcSTAT6-K284 were decreased accompanied by the advanced tumour grade and positively correlated with KIRC patients' survival. Conclusively, we explored an inhibitory axis which not only suppressed tumour metastasis but also could be utilized for drug development to hamper KIRC metastasis.

Competition between p53 and YY1 determines PHGDH expression and malignancy in bladder cancer.

PURPOSE: Serine metabolism is frequently dysregulated in many types of cancers and the tumor suppressor p53 is recently emerging as a key regulator of serine metabolism. However, the detailed mechanism remains unknown. Here, we investigate the role and underlying mechanisms of how p53 regulates the serine synthesis pathway (SSP) in bladder cancer (BLCA). METHODS: Two BLCA cell lines RT-4 (WT p53) and RT-112 (p53 R248Q) were manipulated by applying CRISPR/Cas9 to examine metabolic differences under WT and mutant p53 status. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) and non-targeted metabolomics analysis were adopted to identify metabolomes changes between WT and p53 mutant BLCA cells. Bioinformatics analysis using the cancer genome atlas and Gene Expression Omnibus datasets and immunohistochemistry (IHC) staining was used to investigate PHGDH expression. Loss-of-function of PHGDH and subcutaneous xenograft model was adopted to investigate the function of PHGDH in mice BLCA. Chromatin immunoprecipitation (Ch-IP) assay was performed to analyze the relationships between YY1, p53, SIRT1 and PHGDH expression. RESULTS: SSP is one of the most prominent dysregulated metabolic pathways by comparing the metabolomes changes between wild-type (WT) p53 and mutant p53 of BLCA cells. TP53 gene mutation shows a positive correlation with PHGDH expression in TCGA-BLCA database. PHGDH depletion disturbs the reactive oxygen species homeostasis and attenuates the xenograft growth in the mouse model. Further, we demonstrate WT p53 inhibits PHGDH expression by recruiting SIRT1 to the PHGDH promoter. Interestingly, the DNA binding motifs of YY1 and p53 in the PHGDH promoter are partially overlapped which causes competition between the two transcription factors. This competitive regulation of PHGDH is functionally linked to the xenograft growth in mice. CONCLUSION: YY1 drives PHGDH expression in the context of mutant p53 and promotes bladder tumorigenesis, which preliminarily explains the relationship between high-frequency mutations of p53 and dysfunctional serine metabolism in bladder cancer.

Increased SPRY1 expression activates NF-κB signaling and promotes pancreatic cancer progression by recruiting neutrophils and macrophages through CXCL12-CXCR4 axis.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with a high mortality rate, in which about 90% of patients harbor somatic oncogenic point mutations in KRAS. SPRY family genes have been recognized as crucial negative regulators of Ras/Raf/ERK signaling. Here, we investigate the expression and role of SPRY proteins in PDAC. METHODS: Expression of SPRY genes in human and mice PDAC was analyzed using The Cancer Genome Atlas and Gene Expression Omnibus datasets, and by immunohistochemistry analysis. Gain-of-function, loss-of-function of Spry1 and orthotopic xenograft model were adopted to investigate the function of Spry1 in mice PDAC. Bioinformatics analysis, transwell and flowcytometry analysis were used to identify the effects of SPRY1 on immune cells. Co-immunoprecipitation and K-ras4B G12V overexpression were used to identify molecular mechanism. RESULTS: SPRY1 expression was remarkably increased in PDAC tissues and positively associated with poor prognosis of PDAC patients. SPRY1 knockdown suppressed tumor growth in mice. SPRY1 was found to promote CXCL12 expression and facilitate neutrophil and macrophage infiltration via CXCL12-CXCR4 axis. Pharmacological inhibition of CXCL12-CXCR4 largely abrogated the oncogenic functions of SPRY1 by suppressing neutrophil and macrophage infiltration. Mechanistically, SPRY1 interacted with ubiquitin carboxy-terminal hydrolase L1 to induce activation of nuclear factor κB signaling and ultimately increase CXCL12 expression. Moreover, SPRY1 transcription was dependent on KRAS mutation and was mediated by MAPK-ERK signaling. CONCLUSION: High expression of SPRY1 can function as an oncogene in PDAC by promoting cancer-associated inflammation. Targeting SPRY1 might be an important approach for designing new strategy of tumor therapy.

Human Expansion-Induced Biodiversity Crisis over Asia from 2000 to 2020.

Asia stands out as a priority for urgent biodiversity conservation due to its large protected areas (PAs) and threatened species. Since the 21st century, both the highlands and lowlands of Asia have been experiencing the dramatic human expansion. However, the threat degree of human expansion to biodiversity is poorly understood. Here, the threat degree of human expansion to biodiversity over 2000 to 2020 in Asia at the continental (Asia), national (48 Asian countries), and hotspot (6,502 Asian terrestrial PAs established before 2000) scales is investigated by integrating multiple large-scale data. The results show that human expansion poses widespread threat to biodiversity in Asia, especially in Southeast Asia, with Malaysia, Cambodia, and Vietnam having the largest threat degrees (∼1.5 to 1.7 times of the Asian average level). Human expansion in highlands induces higher threats to biodiversity than that in lowlands in one-third Asian countries (most Southeast Asian countries). The regions with threats to biodiversity are present in ∼75% terrestrial PAs (including 4,866 PAs in 26 countries), and human expansion in PAs triggers higher threat degrees to biodiversity than that in non-PAs. Our findings provide novel insight for the Sustainable Development Goal 15 (SDG-15 Life on Land) and suggest that human expansion in Southeast Asian countries and PAs might hinder the realization of SDG-15. To reduce the threat degree, Asian developing countries should accelerate economic transformation, and the developed countries in the world should reduce the demands for commodity trade in Southeast Asian countries (i.e., trade leading to the loss of wildlife habitats) to alleviate human expansion, especially in PAs and highlands.

The MFN1 and MFN2 mitofusins promote clustering between mitochondria and peroxisomes.

Mitochondria and peroxisomes are two types of functionally close-related organelles, and both play essential roles in lipid and ROS metabolism. However, how they physically interact with each other is not well understood. In this study, we apply the proximity labeling method with peroxisomal proteins and report that mitochondrial protein mitofusins (MFNs) are in proximity to peroxisomes. Overexpression of MFNs induces not only the mitochondria clustering but also the co-clustering of peroxisomes. We also report the enrichment of MFNs at the mitochondria-peroxisome interface. Induced mitofusin expression gives rise to more mitochondria-peroxisome contacting sites. Furthermore, the tethering of peroxisomes to mitochondria can be inhibited by the expression of a truncated MFN2, which lacks the transmembrane region. Collectively, our study suggests MFNs as regulators for mitochondria-peroxisome contacts. Our findings are essential for future studies of inter-organelle metabolism regulation and signaling, and may help understand the pathogenesis of mitofusin dysfunction-related disease.

Spatiotemporal dynamics of forest ecosystem carbon budget in Guizhou: customisation and application of the CBM-CFS3 model for China.

BACKGROUND: Countries seeking to mitigate climate change through forests require suitable modelling approaches to predict carbon (C) budget dynamics in forests and their responses to disturbance and management. The Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) is a feasible and comprehensive tool for simulating forest C stock dynamics across broad levels, but discrepancies remain to be addressed in China. Taking Guizhou as the case study, we customised the CBM-CFS3 model according to China's context, including the modification of aboveground biomass C stock algorithm, addition of C budget accounting for bamboo forests, economic forests, and shrub forests, improvement of non-forest land belowground slow dead organic matter (DOM) pool initialisation, and other model settings. RESULTS: The adequate linear relationship between the estimated and measured C densities (R2 = 0.967, P < 0.0001, slope = 0.904) in the model validation demonstrated the high accuracy and reliability of our customised model. We further simulated the spatiotemporal dynamics of forest C stocks and disturbance impacts in Guizhou for the period 1990-2016 using our customised model. Results shows that the total ecosystem C stock and C density, and C stocks in biomass, litter, dead wood, and soil in Guizhou increased continuously and significantly, while the soil C density decreased over the whole period, which could be attributed to deforestation history and climate change. The total ecosystem C stock increased from 1220 Tg C in 1990 to 1684 Tg C in 2016 at a rate of 18 Tg C yr-1, with significant enhancement in most areas, especially in the south and northwest. The total decrease in ecosystem C stock and C expenditure caused by disturbances reached 97.6 Tg C and 120.9 Tg C, respectively, but both represented significant decreasing trends owing to the decline of disturbed forest area during 1990-2016. Regeneration logging, deforestation for agriculture, and harvest logging caused the largest C stock decrease and C expenditure, while afforestation and natural expansion of forest contributed the largest increases in C stock. CONCLUSIONS: The forests in Guizhou were a net carbon sink under large-scale afforestation throughout the study period; Our customised CBM-CFS3 model can serve as a more effective and accurate method for estimating forest C stock and disturbance impacts in China and further enlightens model customisation to other areas.

Leukemia inhibitory factor receptor homodimerization mediated by acetylation of extracellular lysine promotes prostate cancer progression through the PDPK1/AKT/GCN5 axis.

BACKGROUND: Prostate cancer (PCa), an inert tumour, has a long progression period, but valid biomarkers and methods for effectively and sensitively monitoring PCa progression are lacking, prompting us to identify new predictors for diagnosis and prognosis. Posttranslational modifications characterizing receptor activation are considered potentially strong indicators of disease progression. METHODS: The posttranscriptional regulation of leukaemia inhibitory factor receptor (LIFR) and its novel downstream signalling activity in PCa were studied using liquid mass spectrometry, genetically engineered mouse (GEM) models, organoid assays, lentivirus packaging, infection and stable cell line construction. RESULTS: In this study, the level of acetylated K620 on LIFR in its extracellular domain was shown to predict the progression and prognosis of PCa. In PCa cells, LIFR-K620 acetylation is reversibly mediated by GCN5 and SIRT2. GEM experiments and organoid assays confirmed that the loss of LIFR-K620 acetylation inhibits PCa progression. Mechanistically, K620 acetylation facilitates LIFR homodimerization and subsequently promotes LIFR-S1044 phosphorylation and activation, which further recruits PDPK1 to activate AKT signalling and sequentially enhances the GCN5 protein level to sustain the protumour level of LIFR-K620 acetylation by preventing GCN5 degradation via CRL4Cdt2 E3 ligase. CONCLUSIONS: Acetylation of extracellular K620 on LIFR reinforces its homodimerization and integrates the activities of PDPK1, AKT, GSK3ß and GCN5 to form a novel positive feedback loop in PCa; this modification is thus a promising biomarker for monitoring PCa progression.

Human expansion into Asian highlands in the 21st Century and its effects.

Most intensive human activities occur in lowlands. However, sporadic reports indicate that human activities are expanding in some Asian highlands. Here we investigate the expansions of human activities in highlands and their effects over Asia from 2000 to 2020 by combining earth observation data and socioeconomic data. We find that ∼23% of human activity expansions occur in Asian highlands and ∼76% of these expansions in highlands comes from ecological lands, reaching 95% in Southeast Asia. The expansions of human activities in highlands intensify habitat fragmentation and result in large ecological costs in low and lower-middle income countries, and they also support Asian developments. We estimate that cultivated land net growth in the Asian highlands contributed approximately 54% in preventing the net loss of the total cultivated land. Moreover, the growth of highland artificial surfaces may provide living and working spaces for ∼40 million people. Our findings suggest that highland developments hold dual effects and provide new insight for regional sustainable developments.

Recent Advances in Design and Preparation of Polymer-Based Thermal Management Material.

The boosting of consumer electronics and 5G technology cause the continuous increment of the power density of electronic devices and lead to inevitable overheating problems, which reduces the operation efficiency and shortens the service life of electronic devices. Therefore, it is the primary task and a prerequisite to explore innovative material for meeting the requirement of high heat dissipation performance. In comparison with traditional thermal management material (e.g., ceramics and metals), the polymer-based thermal management material exhibit excellent mechanical, electrical insulation, chemical resistance and processing properties, and therefore is considered to be the most promising candidate to solve the heat dissipation problem. In this review, we summarized the recent advances of two typical polymer-based thermal management material including thermal-conduction thermal management material and thermal-storage thermal management material. Furtherly, the structural design, processing strategies and typical applications for two polymer-based thermal management materials were discussed. Finally, we proposed the challenges and prospects of the polymer-based thermal management material. This work presents new perspectives to develop advanced processing approaches and construction high-performance polymer-based thermal management material.

Digital mapping of zinc in urban topsoil using multisource geospatial data and random forest.

This study aimed to map the spatial patterns of Zn in urban topsoil by using multisource geospatial data and machine learning method. Geological map, digital elevation models, and Landsat images were used to extract data related to geology, relief, and land use types and a vegetation index. Urban functional types were derived from the fusion of Systeme Probatoire d'Observation de la Terre 5 images, points of interest, and real-time Tencent user data. A geodetector was adopted to select key environmental covariates. Random forest (RF) and geographically weighted regression (GWR) were employed to model and map Zn concentrations in urban topsoil. The results showed that urban functional type, geology, NDVI, elevation, slope, and aspect were key environmental covariates. Compared with land use types, urban functional types could better reflect the spatial variation in Zn. The RF and GWR models were established using the key environmental covariates, with leave-one-out cross-validated R values of 0.68 and 0.58 and root mean square errors of 0.51 and 0.57, respectively. The results indicated that digital mapping of Zn in urban topsoil by using multisource geospatial data and RF was feasible. RF might be more suitable to fit the stochastic characteristics of Zn in urban topsoils than GWR, which considers deterministic trends in modeling.

Mapping lead concentrations in urban topsoil using proximal and remote sensing data and hybrid statistical approaches.

Due to rapid urbanization in China, lead (Pb) continues to accumulate in urban topsoil, resulting in soil degradation and increased public exposure. Mapping Pb concentrations in urban topsoil is therefore vital for the evaluation and control of this exposure risk. This study developed spatial models to map Pb concentrations in urban topsoil using proximal and remote sensing data. Proximal sensing reflectance spectra (350-2500 nm) of soils were pre-processed and used to calculate the principal components as landscape factors to represent the soil properties. Other landscape factors, including vegetation and land-use factors, were extracted from time-sequential Landsat images. Two hybrid statistical approaches, regression kriging (RK) and geographically weighted regression (GWR), were adopted to establish prediction models using the landscape factors. The results indicated that the use of landscape factors derived from combined remote and proximal sensing data improved the prediction of Pb concentrations compared with useing these data individually. GWR obtained better results than RK for predicting soil Pb concentration. Thus, joint proximal and remote sensing provides timely, easily accessible, and suitable data for extracting landscape factors.

Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case study in an arid oasis, NW China.

In arid and semi-arid regions, water-quality problems are crucial to local social demand and human well-being. However, the conventional remote sensing-based direct detection of water quality parameters, especially using spectral reflectance of water, must satisfy certain preconditions (e.g., flat water surface and ideal radiation geometry). In this study, we hypothesized that drone-borne hyperspectral imagery of emergent plants could be better applied to retrieval total nitrogen (TN) concentration in water regardless of preconditions possibly due to the spectral responses of emergent plants on nitrogen removal and water purification. To test this hypothesis, a total of 200 groups of bootstrap samples were used to examine the relationship between the extracted TN concentrations from the drone-borne hyperspectral imagery of emergent plants and the experimentally measured TN concentrations in Ebinur Lake Oasis using four machine learning (ML) models (Partial Least Squares (PLS), Random Forest (RF), Extreme Learning Machine (ELM), and Gaussian Process (GP)). Through the introduction of the fractional order derivative (FOD), we build a decision-level fusion (DLF) model to minimize the regression results' biases of individual ML models. For individual ML model, GP performed the best. Still, the amount of uncertainty in individual ML models renders their performance to be subpar. The introduction of the DLF model greatly minimizes the regression results' biases. The DLF model allows to reduce potential uncertainties without sacrificing accuracy. In conclusion, the spectral response caused by nitrogen removal and water purification on emergent plants could be used to retrieve TN concentration in water with a DLF model framework. Our study offers a new perspective and a basic scientific support for water quality monitoring in arid regions.

Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI.

Accurate assessment of soil salinization is considered as one of the most important steps in combating global climate change, especially in arid and semi-arid regions. Multi-spectral remote sensing (RS) data including Landsat series provides the potential for frequent surveys for soil salinization at various scales and resolutions. Additionally, the recently launched Sentinel-2 satellite constellation has temporal revisiting frequency of 5 days, which has been proven to be an ideal approach to assess soil salinity. Yet, studies on detailed comparison in soil salinity tracking between Landsat-8 OLI and Sentinel-2 MSI remain limited. For this purpose, we collected a total of 64 topsoil samples in an arid desert region, the Ebinur Lake Wetland National Nature Reserve (ELWNNR) to compare the monitoring accuracy between Landsat-8 OLI and Sentinel-2 MSI. In this study, the Cubist model was trained using RS-derived covariates (spectral bands, Tasseled Cap transformation-derived wetness (TCW), and satellite salinity indices) and laboratory measured electrical conductivity of 1:5 soil:water extract (EC). The results showed that the measured soil salinity had a significant correlation with surface soil moisture (Pearson's r = 0.75). The introduction of TCW generated satisfactory estimating performance. Compared with OLI dataset, the combination of MSI dataset and Cubist model yielded overall better model performance and accuracy measures (R2 = 0.912, RMSE = 6.462 dS m-1, NRMSE = 9.226%, RPD = 3.400 and RPIQ = 6.824, respectively). The differences between Landsat-8 OLI and Sentinel-2 MSI were distinguishable. In conclusion, MSI image with finer spatial resolution performed better than OLI. Combining RS data sets and their derived TCW within a Cubist framework yielded accurate regional salinity map. The increased temporal revisiting frequency and spectral resolution of MSI data are expected to be positive enhancements to the acquisition of high-quality soil salinity information of desert soils.