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Lake clarity, usually measured by Secchi disc depth (SDD), is a reliable proxy of lakes trophic status due to its close link with total suspended matter, chlorophyll-a, and nutrients. Trained with in-situ measured SDD and match-up Landsat images, we established various regression models to estimate SDD for global lakes. We selected a unified model which demonstrated good spatiotemporal transferability, and has potential to map SDD in different years with good quality of Landsat top-of-atmosphere (TOA) images embedded in Google Earth Engine (GEE). The unified model was successfully calibrated (n = 3586 data points, R 2  = 0.84, MAPE = 29.8%) against SDD measured in 2235 lakes across the world, and the validation (n = 1779, R 2  = 0.76, MAPE = 38.8%) also exhibited stable performance. The unified model was tuned to historical SDD measurements coincident with different Landsat sensors (L5-TM, L7-ETM+, L8-OLI) launched over the past four decades (1984-2020), thus confirming its temporal stability. Global SDD was mapped using GEE with OLI TOA products mainly acquired in 2019 to examine the spatial variation of lake water clarity (lake surface area ≥ 1 ha) all over the world. Worldwide, lake water clarity averaged 3.13 ± 1.71 m in 2019, but exhibited remarkable spatial variability due to catchment hydrological and landscape settings, lake morphology, elevation and anthropogenic impact. Inland waters in Europe (4.18 ± 1.82 m) and North America (3.84 ± 1.77 m) had the highest clarity due to greater water depth combined with less human disturbance in the high latitude regions. Lakes in South America (2.50 ± 2.33 m), Asia (2.44 ± 1.63 m) and Africa (2.36 ± 0.72 m) displayed intermediate clarity. Lakes in Oceania (1.97 ± 1.48 m) exhibited the lowest clarity for all continents except Antarctica. Further, we used the mapped SDD to evaluate water trophic status using the Carlson trophic state index. Our results indicate that, in 2019, about 63.6% of the lake areas and 47.8% of total lake numbers (2,219,627/4,646,056) were oligotrophic for global lakes, while about 23.6% areal percent and 37.1% of lake numbers are eutrophic mostly as a result of their being located in agricultural and urban-dominated drainage basins. This study, for the first time, provides water clarity information for lakes with area ≥ 1 ha all over the world with 30-m resolution and facilitates the understanding of the water clarity relevant to TSM (r = 0.95), Chl-a (r = 0.73), total phosphorus (r = 0.75), total nitrogen (r = 0.60), which could further provide water clarity data and technical support for trophic level evaluations as well. This unified model could serve as a powerful research tool for long-term monitoring of aquatic ecosystems and assessing their resilience to anthropogenic disturbance and climate change-related stressors., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

In this study, we empirically developed a robust model (the Root Mean Square Error (RMSE), bias, NSE and RE were 26.63 mg/L, -4.86 mg/L, 0.47 and 16.47%, respectively) for estimating the total suspended solids (TSS) concentrations in lakes and reservoirs (Hereinafter referred to as lakes) across the Eastern Plain Lake (EPL) Zone. The model was based on 700 in-situ TSS samples collected during 2007-2020 and logarithmic transformed red band reflectance of Landsat data. Based on the Google Earth Engine (GEE), the TSS concentrations in 16,804 lakes were mapped from 1984 to 2019. The results demonstrated a decreasing tendency of TSS in 82.2% of the examined lakes (72.5% of the basins) indicating that the pollutants carried by TSS flowing into the lakes were decreasing. Statistically significant variation (p < 0.05) was found in half of these lakes (28.6% of the basins). High TSS level (>100 mg/L) was observed in 0.31% of lakes (1.1% of the basins). The changing rates of TSS in 47.8% of the lakes (52.7% of the basins) ranged between -50 mg/L/yr and 0. We found high and significantly increased relative spatial heterogeneity of TSS in 4.6% and 6.5% of lakes, respectively. Likewise, the environmental factors, i.e., fertilizer usage, domestic wastewater, industrial wastewater, precipitation, wind speed and Normalized Difference Vegetation Index (NDVI) exhibited a significant correlation with interannual TSS in 38, 21, 20, 11, 17 and 15 of the 91 basins, respectively. This analysis indicated that only precipitation and fertilizer usage were significantly (p < 0.05) related to the spatial distribution of TSS. The relative contributions of the six factors to the interannual TSS changes were varied in different basins. Overall, the NDVI (the representation of vegetation cover) had a high mean contribution to the interannual TSS changes with an average contribution of 7.2%, and contributions of fertilizer were varied greatly among the basins (0.01%-68%). Human activities (fertilizer usage, domestic wastewater, industrial wastewater) and natural factors (precipitation, wind speed and NDVI) played relatively important roles to TSS changes in 14 and 15 of the 91 basins, respectively. Beyond the six factors in this study, other unanalyzed factors (such as lake depth and soil texture) also had some impacts on the distribution of TSS in the study area., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Water clarity, denoted by the Secchi disk depth (SDD), is one of the most important indicators for monitoring water quality. In the Songhua River basin (SHRB), few studies have used Landsat to monitor long-term (3-4 decades) changes in lake SDD and explore the impact of natural and human factors on SDD interannual variation at the watershed scale. Lakes in the SHRB are of great significance to local populations. Understanding the spatiotemporal dynamics of SDD could help policymakers manage, protect, and predict lake water quality. We utilized the Landsat red/blue band ratio in the Google Earth Engine to estimate the SDD of 77 lakes and generated annual mean SDD maps from 1990 to 2018. The results of the SDD interannual changes showed that the water quality in the SHRB has improved since 2005. Specifically, the SDD in the SHRB displayed a significant increasing trend (p < 0.05) from 0.29 m in 2005 to 0.37 m in 2018. Moreover, the number of lakes displaying a significant increasing trend for SDD increased from 18 between 1990 and 2005 to 31 between 2005 and 2018. We also found that use of chemical fertilizer significantly impacted lakes, followed by wastewater discharge and normalized difference vegetation index. Improvements in the quantity and ability of wastewater discharge treatment and increased vegetation cover have alleviated water pollution; however, the non-point pollution of agriculture still poses a threat to some lakes in the SHRB. Therefore, more efforts should be made to further improve the aquatic ecological environment of SHRBs., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Lake eutrophication has attracted the attention of the government and general public. Chlorophyll-a (Chl-a) is a key indicator of algal biomass and eutrophication. Many efforts have been devoted to establishing accurate algorithms for estimating Chl-a concentrations. In this study, a total of 273 samples were collected from 45 typical lakes across China during 2017-2019. Here, we proposed applicable machine learning algorithms (i.e., linear regression model (LR), support vector machine model (SVM) and Catboost model (CB)), which integrate a broad scale dataset of lake biogeochemical characteristics using Multispectral Imager (MSI) product to seamlessly retrieve the Chl-a concentration. A K-means clustering approach was used to cluster the 273 normalized water leaving reflectance spectra [Rrs (λ)] extracted from MSI imagery with Case 2 Regional Coast Colour (CR2CC) processor into three groups. The pH, electrical conductivity (EC), total suspended matter (TSM) and dissolved organic carbon (DOC) from three clustering groups had significant differences (p < 0.05**), indicating that water quality parameters have an integrated impact on Rrs(λ)-spectra. The results of machine learning algorithms integrating demonstrated that SVM obtained a better degree of measured- and derived- fitting (calibration: slope = 0.81, R 2  = 0.91; validation: slope = 1.21, R 2  = 0.88). On the contrary, the documented nine Chl-a algorithms gave poor results (fitting 1:1 linear slope < 0.4 and R 2  < 0.70) with synchronous train and test datasets. It demonstrated that machine learning provides a robust model for quantifying Chl-a concentration. Further, considering three Rrs(λ) clustering groups by k-means, Chl-a SVM model indicated that cluster 1 group gave a better retrieving performance (slope = 0.71, R 2  = 0.78), followed by cluster 3 group (slope = 0.77, R 2  = 0.64) and cluster 2 group (slope = 0.67, R 2  = 0.50). These are related to the low TSM and high DOC levels for cluster-1 and cluster-3 Rrs(λ) spectra, which reduce the influence of particle in red bands for Rrs(λ) signal. Our results highlighted the quantification of lake Chl-a concentrations using MSI imagery and SVM, which can realize the large-scale monitoring and more appropriate for medium/low Chl-a level. The remote estimation of Chl-a based on artificial intelligence can provide an effective and robust way to monitor the lake eutrophication on a macro-scale; and offer a better approach to elucidate the response of lake ecosystems to global change., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

It is high incidence of tubulointerstitial lesion (TIL) in lupus nephritis (LN) and TIL can affect the prognosis of patients with LN. Signal transducer and activator of transcription (STAT) 3 was activated in LN and STAT3 inhibition could delay the onset of LN. Here, we evaluated the role of a well-known STAT3 inhibitor, S3I-201, on TIL in lupus nephritis. STAT3 was activated in MRL/lpr mice (a mouse model of lupus nephritis), and treatment with S3I-201 inhibited the activation of it. The level of 24-h urine protein and nitrogen urea increased in MRL/lpr mice and adminstration of S3I-201 reduced the level of urinary protein. In addition, S3I-201 attenuated the expression of α-smooth muscle actin (α-SMA), Fibronectin (FN) proteins, as well as the expression of monocyte chemotactic factor-1 (MCP-1) and intercellular adhesion molecule (ICAM-1). However, the expression of E-cadherin improved when treatment with S3I-201. These results revealed that the activation of STAT3 mediates tubulointerstitial lesion in mice with LN. S3I-201, by suppressing STAT3 activity, has therapeutic effect in lupus nephritis., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Autophagy is an important homoeostatic mechanism for the lysosomal degradation of protein aggregates and damaged cytoplasmic components. Recent studies suggest that autophagy which is induced by TGF-β1 suppresses kidney fibrosis in renal tubular epithelial cells (RTECs) of obstructed kidneys. Sphingosine kinase 1(SK1), converting sphingosine into endogenous sphingosine-1-phosphate (S1P), was shown to modulate autophagy and involved in the processes of fibrotic diseases. Since SK1 activity is also up-regulated by TGF-β1, we explored its effect on the induction of autophagy and development of renal fibrosis in this study. In vitro, SK1 expression and activity were markedly increased by TGF-β1 stimulation in a time and concentration dependent manner, and concomitant changes in autophagic response were observed in HK-2 cells. Further, knockdown of SK-1 led to a decrease of autophagy whereas overexpression of SK1 caused a greater induction of autophagy. In addition, overexpression of SK1 resulted in decreased of mature TGF-β levels through autophagic degradation. In vivo, SK1 enzymatic activity and autophagic response were both up-regulated in a mouse model of kidney fibrosis induced by unilateral ureteral obstruction (UUO); meanwhile, increased of mature TGF-β1 and deposition of extracellular matrix (ECM) were observed in tubulointerstitial areas compared with sham-operated mice. However, aggravation of renal fibrosis was detected when SK1 inhibitor PF-543 was applied to suppress SK1 enzymatic activity in UUO mice. At the same time, autophagy was also inhibited by PF-543. Thus, our findings suggest that SK1 activation is renoprotective via induction of autophagy in the fibrotic process., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Abnormal lipid metabolism contributes to the renal lipid accumulation, which is associated with diabetic kidney disease, but its precise mechanism remains unclear. The growing evidence demonstrates that thioredoxin-interacting protein is involved in regulating cellular glucose and lipid metabolism. Here, we investigated the effects of thioredoxin-interacting protein on lipid accumulation in diabetic kidney disease. In contrast to the diabetic wild-type mice, the physical and biochemical parameters were improved in the diabetic thioredoxin-interacting protein knockout mice. The increased renal lipid accumulation, expression of acetyl-CoA carboxylase, fatty acid synthase and sterol regulatory element binding protein-1, and phosphorylated Akt and mTOR associated with diabetes in wild-type mice was attenuated in diabetic thioredoxin-interacting protein knockout mice. Furthermore, thioredoxin-interacting protein knockout significantly increased the expression of peroxisome proliferator-activated receptor-α, acyl-coenzyme A oxidase 1 and carnitine palmitoyltransferaser 1 in diabetic kidneys. In vitro experiments, using HK-2 cells, revealed that knockdown of thioredoxin-interacting protein inhibited high glucose-mediated lipid accumulation, expression of acetyl-CoA carboxylase, fatty acid synthase and sterol regulatory element binding protein-1, as well as activation of Akt and mTOR. Moreover, knockdown of thioredoxin-interacting protein reversed high glucose-induced reduction of peroxisome proliferator-activated receptor-α, acyl-coenzyme A oxidase 1 and carnitine palmitoyltransferaser 1 expression in HK-2 cells. Importantly, blockade of Akt/mTOR signaling pathway with LY294002, a specific PI3K inhibitor, replicated these effects of thioredoxin-interacting protein silencing. Taken together, these data suggest that thioredoxin-interacting protein deficiency alleviates diabetic renal lipid accumulation through regulation of Akt/mTOR pathway, thioredoxin-interacting protein may be a potential therapeutic target for diabetic kidney disease., (Copyright © 2016 Elsevier Ltd. All rights reserved.)