Re-estimating China's lake CO2 flux considering spatiotemporal variability

The spatiotemporal variability of lake partial carbon dioxide pressure (pCO2) introduces uncertainty into CO2 flux estimates at the lake water-air interface. Knowing the variation pattern of pCO2 is important for obtaining accurate global estimation. Here we examine seasonal and trophic variations in lake pCO2 based on 13 field campaigns conducted in Chinese lakes from 2017 to 2021. We found significant seasonal fluctuations in pCO2, with decreasing values as trophic states intensify within the same region. Saline lakes exhibit lower pCO2 levels than freshwater lakes. These pCO2 dynamics result in variable areal CO2 emissions, with lakes exhibiting different trophic states (oligotrophication > mesotrophication > eutrophication) and saline lakes differing from freshwater lakes (-23.1 ± 17.4 vs. 19.3 ± 18.3 mmol m-2 d-1). These spatiotemporal pCO2 variations complicate total CO2 emission estimations. Using area proportions of lakes with varying trophic states and salinity in China, we estimate China's lake CO2 flux at 8.07 Tg C yr-1. In future studies, the importance of accounting for lake salinity, seasonal dynamics, and trophic states must be noticed to enhance the accuracy of large-scale carbon emission estimates from lake ecosystems in the context of climate change.

Analysis of taiga and tundra lake browning trends from 2002 to 2021 using MODIS data

Lakes in taiga and tundra regions may be silently undergoing changes due to global warming. One of those changes is browning in lake color. The browning interacts with the carbon cycle, ecosystem dynamics, and water quality in freshwater systems. However, spatiotemporal variabilities of browning in these regions have not been well documented. Using MODIS remote sensing reflectance at near ultraviolet wavelengths from 2002 to 2021 on the Google Earth Engine platform, we quantified long-term browning trends across 7616 lakes (larger than 10 km2) in taiga and tundra biomes. These lakes showed an overall decreased trend in browning (Theil-Sen Slope = 0.00015), with ∼36% of these lakes showing browning trends, and ∼1% of these lakes showing statistically significant (p-value <0.05) browning trends. The browning trends more likely occurred in small lakes in high latitude, low ground ice content regions, where air temperature increased and precipitation decreased. While temperature is projected to increase in response to climate change, our results provide one means to understand how biogeochemical cycles and ecological dynamics respond to climate change.

Spatial variations of DOM in a diverse range of lakes across various frozen ground zones in China: Insights into molecular composition

Dissolved organic matter (DOM) plays a significant role in aquatic biogeochemical processes and the carbon cycle. As global climate warming continues, it is anticipated that the composition of DOM in lakes will be altered. This could have significant ecological and environmental implications, particularly in frozen ground zones. However, there is limited knowledge regarding the spatial variations and molecular composition of DOM in lakes within various frozen ground zones. In this study, we examined the spatial variations of in-lake DOM both quantitatively, focusing on dissolved organic carbon (DOC), and qualitatively, by evaluating optical properties and conducting molecular characterization using Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). Lakes in cold regions retained more organic carbon compared to those in warmer regions, the comparison of the mean value of DOC concentration of all sampling sites in the same frozen ground zone showed that the highest mean lake DOC concentration found in the permafrost zone at 21.4 ± 19.3 mg/L. We observed decreasing trends in E2:E3 and MLBL, along with increasing trends in SUVA254 and AImod, along the gradually warming ground. These trends suggest lower molecular weight, reduced aromaticity, and increased molecular lability of in-lake DOM in the permafrost zone compared to other frozen ground zones. Further FT-ICR MS characterization revealed significant molecular-level heterogeneity of DOM, with the lowest abundance of assigned DOM molecular formulas found in lakes within permafrost zones. In all studied zones, the predominant molecular formulas in-lake DOM were compounds consisted by CHO elements, accounting for 40.1 % to 63.1 % of the total. Interestingly, the percentage of CHO exhibited a gradual decline along the warming ground, while there was an increasing trend in nitrogen-containing compounds (CHON%). Meanwhile, a substantial number of polyphenols were identified, likely due to the higher rates of DOM mineralization and the transport of terrestrial DOM derived from vascular plants under the elevated temperature and precipitation conditions in the warming region. In addition, sulfur-containing compounds (CHOS and CHNOS) associated with synthetic surfactants and agal derivatives were consistently detected, and their relative abundances exhibited higher values in seasonal and short-frozen ground zones. This aligns with the increased anthropogenic disturbances to the lake's ecological environment in these two zones. This study reported the first description of in-lake DOM at the molecular level in different frozen ground zones. These findings underline that lakes in the permafrost zone serve as significant hubs for carbon processing. Investigating them may expand our understanding of carbon cycling in inland waters.

[Prevention and treatment of endometrial cancer]

Endometrial cancer is rising in incidence, especially in young women. This rise in incidence has implications for both primary prevention and screening in high-risk population. In the past several years, our understanding of the integration of clinically related genomic and pathologic data optimized the management of endometrial cancer. The updated 2023 FIGO staging includes the histological and molecular classification to better reflect the improved understanding of the heterogenous nature of endometrial carcinoma. Standard primary treatment is quite essential, however, selection of patients for adjuvant radiation or chemotherapy remains in controversy. Molecular characterization of endometrial cancer is becoming critical in directing treatment for advanced and recurrent disease, and the addition of immunotherapy to frontline chemotherapy is becoming the standard of care. More attention should be given to increase awareness of survivorship issues and improve patient quality-of-life.

Extraction of eutrophic and green ponds from segmentation of high-resolution imagery based on the EAF-Unet algorithm

Inland ponds exhibit remarkable ubiquity across the globe, playing a vital role in the sustainability of global continental freshwater resources and contributing significantly to their biodiversity. Numerous ponds are eutrophic and experience recurrent seasonal or year-round algal blooms or persistent duckweed cover, conferring a characteristic green hue. Here, we denote these eutrophic and green ponds as EGPs. The excessive proliferation of algal blooms and duckweed within these EGPs poses a significant threat to the ecological functioning of these aquatic systems, which can lead to hypoxia or the release of microcystins. To identify these EGPs automatically, we constructed an Efficient Attention Fusion Unet (EAF-Unet) algorithm using Gaofen-2 (GF2) panchromatic and multispectral imagery. The attention mechanism was incorporated in Unet to help better detect EGPs. Using the first EGP labeled dataset, we determined the best input feature combination (RGB, NIR, NDVI, and Bright) and the most effective encoding (Rasnet50) for EAF-Unet for distinguishing EGPs from other ground cover types. The evaluation indices - Precision (0.81), Recall (0.79), F1-Score (0.80), and Intersection over Union (IoU, 0.67) - indicate that EAF-Unet can accurately and robustly extract EGPs from GF2 images without relying on pond water masks. Remote-sensing EGP products can assist in identifying ponds with severe eutrophication. Moreover, these products can serve as references for identifying high-risk areas prone to improper sewage discharge or inadequate sewer construction.

A novel chlorophyll-a retrieval model based on suspended particulate matter classification and different machine learning

Chlorophyll-a (Chla) in inland waters is one of the most significant optical parameters of aquatic ecosystem assessment, and long-term and daily Chla concentration monitoring has the potential to facilitate in early warning of algal blooms. MOD09 products have multiple observation advantages (higher temporal, spatial resolution and signal-to-noise ratio), and play an extremely important role in the remote sensing of water color. For developing a high accuracy machine learning model of remotely estimating Chla concentration in inland waters based on MOD09 products, this study proposed an assumption that the accuracy of Chla concentration retrieval will be improved after classifying water bodies into three groups by suspended particulate matter (SPM) concentration. A total of 10 commonly used machine learning models were compared and evaluated in this study, including random forest regressor (RFR), deep neural networks (DNN), extreme gradient boosting (XGBoost), and convolutional neural network (CNN). Altogether, 41 basic bands and 820 band ratios between the 41 bands were filtered by measuring their correlation with Ln(Chla) and several bands brought into different machine learning models. Results demonstrated that the construction of Chla concentration remote estimation model based on SPM classification could significantly improve the correlation between Ln(Chla) and 41 basic spectral band combinations, the correlation between Ln(Chla) and 820 band ratios, and the model verification R2 from 0.41 to 0.83. Furthermore, B3, B20, and B32 were finally selected based on correlation with SPM to classify SPM and the classification accuracy could reach 0.9. Finally, we concluded that RFR model performed best via comparing the R2, RMSE, and MAPE. By comparing the relative contribution of input bands in different groups, B3 contributed most to three groups. The model constructed in this study has promising prospects for promotion and application in other inland waters, and could provide systematic research reference for subsequent research.

A novel total phosphorus concentration retrieval method based on two-line classification in lakes and reservoirs across China

Phosphorus is widely recognized as a nutrient that restricts growth and is the primary contributor to eutrophication in 80 % of water bodies. Consequently, the Chinese government has consistently prioritized monitoring and controlling total phosphorus (TP) levels. The remote estimation of TP in lakes and reservoirs at a national scale is a challenging task due to TP being a non-optically active parameter. Currently, there is a lack of developed TP inversion models specifically designed for lakes and reservoirs in China. For solving this problem, a novel two-line classification method drawn on scatter plots based on the natural logarithm of TP (Ln(TP)) and B33/B9 was proposed and used to classify 1211 measured samples obtained from field cruises in 105 lakes and reservoirs across China from 2012 to 2022 into three categories, Class 1, Class 2, and Class 3. Results demonstrate that the proposed classification method has the ability to enhance the correlation between Ln(TP) and 43 basic potential single band and band combinations. Specifically, the correlation range improved from (-0.31,0.15) to (-0.77,0.24) in Class 1, (-0.81, 0.36) in Class 2, and (-0.74, 0.52) in Class 3. Additionally, the classification method also improved the correlation range between Ln(TP) and 820 band ratios, from (-0.32, 0.32) to (-0.83, 0.82) in Class 1, (-0.86, 0.86) in Class 2, and (-0.86, 0.86) in Class 3. These datasets were subsequently utilized as input for eXtreme Gradient Boosting (XGBoost) models. Finally, well performing XGBoost models in Class 1 (R2 = 0.76, RMSE = 0.3, MAPE = 12 %), Class 2 (R2 = 0.84, RMSE = 0.49, MAPE = 38 %), and Class 3 (R2 = 0.74, RMSE = 0.46, MAPE = 14 %) were used to map TP of 563 large lakes and reservoirs (≥20 km2) across China using MODIS images from 2005, 2010, 2015, and 2020. This study presents a novel approach for estimating non-optically active parameters through remote sensing on a national scale.

Choline induced cardiac dysfunction by inhibiting the production of endogenous hydrogen sulfide in spontaneously hypertensive rats

To investigate the exact effects of dietary choline on hypertensive heart disease (HHD) and explore the potential mechanisms, male spontaneously hypertensive rats (SHR) and Wistar Kyoto rats (WKY) were randomly divided into five groups as follows: WKY group, WKY + Choline group, SHR group, SHR + Choline group, and SHR + Choline + NaHS group. In choline treatment groups, rats were fed with 1.3% (w/v) choline in the drinking water for 3 months. The rats in the SHR + Choline + NaHS group were intraperitoneally injected with NaHS (100 micromol/kg/day, a hydrogen sulfide (H2S) donor) for 3 months. After 3 months, left ventricular ejection fraction (LVEF) and fractional shortening (LVFS), the indicators of cardiac function measured by echocardiography, were increased significantly in SHR as compared to WKY, although there was no significant difference in collagen volumes and Bax/Bcl-2 ratio between the two groups, indicating the early stage of cardiac hypertrophy. There was a significant decrease in LVEF and LVFS and an increase in collagen volumes and Bax/Bcl-2 ratio in SHR fed with choline, meanwhile, plasma H2S levels were significantly decreased significantly in SHR fed with choline accompanying by the decrease of cystathionine-gamma-lyase (CSE) activity. Three months of NaHS significantly increased plasma H2S levels, ameliorated cardiac dysfunction and inhibited cardiac fibrosis and apoptosis in SHR fed with choline. In conclusion, choline aggravated cardiac dysfunction in HHD through inhibiting the production of endogenous H2S, which was reversed by supplementation of exogenous H2S donor.

Real world data on outcomes of anti-CD38 antibody treated, including triple class refractory, patients with multiple myeloma: a multi-institutional report from the Canadian Myeloma Research Group (CMRG) Database

Multiple myeloma (MM) remains incurable despite the availability of novel agents. This multi-center retrospective cohort study used the Canadian Myeloma Research Group Database to describe real-world outcomes of patients withanti-CD38 monoclonal antibody (mAb) refractory MM subsequently treated with standard of care (SoC) regimens. Patients with triple class refractory (TCR) disease (refractory to a proteasome inhibitor, immunomodulatory drug, and anti-CD38 mAb) were examined as a distinct cohort. Overall, 663 patients had disease progression on anti-CD38 mAb therapy, 466 received further treatment (346 with SoC regimens were included, 120 with investigational agents on clinical trial and were excluded). The median age at initiation of subsequent SoC therapy of 67.9 (range 39.6-89.6) years with a median of 3 prior lines (range 1-9). The median PFS and OS from the start of subsequent therapy was 4.6 (95% CI 4.1-5.6) months and 13.3 (95% CI 10.6-16.6) months, respectively. The median PFS and OS of patients with TCR disease (n = 199) was 4.4 (95% CI 3.6-5.3) months and 10.5 (95% CI 8.5-13.8) months. Our results reinforce that real-world patients with relapsed MM, particularly those with TCR disease, have dismal outcomes. There remains an urgent unmet need for the development of and access to effective therapeutics for these patients.

Remote estimation of phycocyanin concentration in inland waters based on optical classification

In recent years, under the dual pressure of climate change and human activities, the cyanobacteria blooms in inland waters have become a threat to global aquatic ecosystems and the environment. Phycocyanin (PC), a diagnostic pigment of cyanobacteria, plays an essential role in the detection and early warning of cyanobacterial blooms. In this context, accurate estimation of PC concentration in turbid waters by remote sensing is challenging due to optical complexity and weak optical signal. In this study, we collected a comprehensive dataset of 640 pairs of in situ measured pigment concentration and the Ocean and Land Color Instrument (OLCI) reflectance from 25 lakes and reservoirs in China during 2020-2022. We then developed a framework consisting of the water optical classification algorithm and three candidate algorithms: baseline height, band ratio, and three-band algorithm. The optical classification method used remote sensing reflectance (Rrs) baseline height in three bands: Rrs(560), Rrs(647) and Rrs(709) to classify the samples into five types, each with a specific spectral shape and water quality character. The improvement of PC estimation accuracy for optically classified waters was shown by comparison with unclassified waters with RMSE = 72.6 μg L-1, MAPE = 80.4 %, especially for the samples with low PC concentration. The results show that the band ratio algorithm has a strong universality, which is suitable for medium turbid and clean water. In addition, the three-band algorithm is only suitable for medium turbid water, and the line height algorithm is only suitable for high PC content water. Furthermore, the five distinguished types with significant differences in the value of the PC/Chla ratio well indicated the risk rank assessment of cyanobacteria. In conclusion, the proposed framework in this paper solved the problem of PC estimation accuracy problem in optically complex waters and provided a new strategy for water quality inversion in inland waters.

China's wetland soil organic carbon pool: New estimation on pool size, change, and trajectory

Robust estimates of wetland soil organic carbon (SOC) pools are critical to understanding wetland carbon dynamics in the global carbon cycle. However, previous estimates were highly variable and uncertain, due likely to the data sources and method used. Here we used machine learning method to estimate SOC storage and their changes over time in China's wetlands based on wetland SOC density database, associated geospatial environmental data, and recently published wetland maps. We built a database of wetland SOC density in China that contains 809 samples from 181 published studies collected over the last 20 years as presented in the published literature. All samples were extended and standardized to a 1-m depth, on the basis of the relationship between SOC density data from soil profiles of different depths. We used three different machine learning methods to evaluate their robustness in estimating wetland SOC storage and changes in China. The results indicated that random forest model achieved accurate wetland SOC estimation with R2 being .65. The results showed that average SOC density of top 1 m in China's wetlands was 25.03 ± 3.11 kg C m-2 in 2000 and 26.57 ± 3.73 kg C m-2 in 2020, an increase of 6.15%. SOC storage change from 4.73 ± 0.58 Pg in 2000 to 4.35 ± 0.61 Pg in 2020, a decrease of 8.03%, due to 13.6% decreased in wetland area from 189.12 × 103 to 162.8 × 103  km2 in 2020, despite the increase in SOC density during the same time period. The carbon accumulation rate was 107.5 ± 12.4 g C m-2  year-1 since 2000 in wetlands with no area changes. Climate change caused variations in wetland SOC density, and a future warming and drying climate would lead to decreases in wetland SOC storage. Estimates under Shared Socioeconomic Pathway 1-2.6 (low-carbon emissions) suggested that wetland SOC storage in China would not change significantly by 2100, but under Shared Socioeconomic Pathway 5-8.5 (high-carbon emissions), it would decrease significantly by approximately 5.77%. In this study, estimates of wetland SOC storage were optimized from three aspects, including sample database, wetland extent, and estimation method. Our study indicates the importance of using consistent SOC density and extent data in estimating and projecting wetland SOC storage.

Microcystins risk assessment in lakes from space: Implications for SDG 6.1 evaluation

Cyanobacterial blooms release a large number of algal toxins (e.g., Microcystins, MCs) and seriously threaten the safety of drinking water sources what the SDG 6.1 pursues (to provide universal access to safe drinking water by 2030, United Nations Sustainable Development Goal). Nevertheless, algal toxins in lake water have not been routinely monitored and evaluated well and frequently so far. In this study, a total of 100 large lakes (>25 km2) in densely populated eastern China were studied, and a remote sensing scheme of human health risks from MCs based on Sentinel-3 OLCI data was developed. The spatial and temporal dynamics of MCs risk in eastern China lakes since OLCI satellite observation data (2016-2021) were first mapped. The results showed that most of the large lakes in eastern China (80 out of 100) were detected with the occurrence of a high risk of more than 1 pixel (300×300 m) at least once. Fortunately, in terms of lake areas, the frequency of high human health risks in most waters (70.93% of total lake areas) was as less as 1%. This indicates that drinking water intakes can be set in most waters from the perspective of MCs, yet the management departments are required to reduce cyanobacterial blooms. This study highlights the potential of satellite in monitoring and assessing the risk of algal toxins and ensuring drinking water safety. It is also an important reference for SDG 6.1 reporting for lakes that lack routine monitoring.

Effects of cropland reclamation on soil organic carbon in China's black soil region over the past 35 years

The long-term use of cropland and cropland reclamation from natural ecosystems led to soil degradation. This study investigated the effect of the long-term use of cropland and cropland reclamation from natural ecosystems on soil organic carbon (SOC) content and density over the past 35 years. Altogether, 2140 topsoil samples (0-20 cm) were collected across Northeast China. Landsat images were acquired from 1985 to 2020 through Google Earth Engine, and the reflectance of each soil sample was extracted from the Landsat image that its time was consistent with sampling. The hybrid model that included two individual SOC prediction models for two clustering regions was built for accurate estimation after k-means clustering. The probability hybrid model, a combination between the hybrid model and classification probabilities of pixels, was introduced to enhance the accuracy of SOC mapping. Cropland reclamation results were extracted from the land cover time-series dataset at a 5-year interval. Our study indicated that: (1) Long-term use of cropland led to a 3.07 g kg-1 and 6.71 Mg C ha-1 decrease in SOC content and density, respectively, and the decrease of SOC stock was 0.32 Pg over the past 35 years; (2) nearly 64% of cropland had a negative change in terms of SOC content from 1985 to 2020; (3) cropland reclamation track changed from high to low SOC content, and almost no cropland was reclaimed on the "Black soils" after 2005; (4) cropland reclamation from wetlands resulted in the highest decrease, and reclamation period of years 31-35 decreased when SOC density and SOC stock were 16.05 Mg C ha-1 and 0.005 Pg, respectively, while reclamation period of years 26-30 from forest witnessed SOC density and stock decreases of 8.33 Mg C ha-1 and 0.01 Pg, respectively. Our research results provide a reference for SOC change in the black soil region of Northeast China and can attract more attention to the area of the protection of "Black soils" and natural ecosystems.

Reliability of synthetic diffusion-weighted imaging with a high b-value for paediatric abdominal MRI

AIM

To evaluate the reliability of synthetic diffusion-weighted imaging (DWI) using a high b-value in comparison to conventional DWI for paediatric abdominal MRI.

MATERIALS AND METHODS

Paediatric patients (<19 years old) who underwent liver or pancreatobiliary MRI with DWI using 10 b-values (b = 0, 25, 50, 75, 100, 200, 400, 600, 800, 1,500 s/mm2) from March to October 2021 were included in this retrospective study. Using the software, synthetic DWI using b = 1,500 s/mm2 was generated automatically by selecting the b-value required as output. Conventional and synthetic DWI values for b = 1,500 s/mm2 were measured at the liver, spleen, paraspinal muscle, and mass lesions, if present, and apparent diffusion coefficient (ADC) values were calculated using the mono-exponential model. Intraclass correlation coefficients (ICCs) were calculated to assess the reliability of conventional and synthetic DWI and ADC values with b = 1,500 s/mm2.

RESULTS

Thirty paediatric patients (M:F = 22:8, mean 10.8 ± 3.1 years old) were included and four had tumours on abdominal MRI. ICC values were 0.906-0.995 between conventional and synthetic DWI and ADC with b = 1,500 s/mm2 in the liver, spleen and muscle. For mass lesions, ICC values were 0.997-0.999 for both synthetic DWI and ADC images.

CONCLUSIONS

Synthetic DWI and ADC values obtained using a high b-value showed excellent agreement with conventional DWI for the liver, spleen, muscle, and mass in paediatric MRI.

Unbalanced impacts of nature and nurture factors on the phenology, area and intensity of algal blooms in global large lakes: MODIS observations

Under the influence of climate warming and human activities, many large lakes have experienced an increase in eutrophication and algal blooms. Although these trends have been identified using low temporal resolution (~16 days) satellites such as those of the Landsat missions, the opportunity to compare high-frequency spatiotemporal variations of algal bloom characteristics between lakes has not been explored. In the present study, we explore daily satellite observations by developing a universal, practical, and robust algorithm to identify the spatiotemporal distribution of algal bloom dynamics in large lakes (>500 km²) across the globe. Data from 161 lakes, taken from 2000 to 2020 showed an average accuracy of 79.9 %. Algal blooms were detected in 44 % of all lakes, with a higher incidence in temperate lakes (67 % of all temperate lakes), followed by tropical lakes (59 %) compared to lakes in arid climates (23 %). We found positive trends in bloom area and frequency (p < 0.05), as well as an earlier bloom time (p < 0.05). Climate factors were found to be linked to changes in annual initial bloom time (44 %); while an increase in human activities was associated to bloom duration (49 %), area (max percent: 53 %, mean percent: 45 %), and frequency (46 %). The study shows the evolution of daily algal blooms and their phenology in global large lakes for the first time. Such information enhances our understanding of algal bloom dynamics and their drivers, with important considerations to improve the management of large lake ecosystems.

Response of total suspended matter to natural and anthropogenic factors since 1990 in China's large lakes

Total suspended matter (TSM) as a critical water quality parameter is closely linked with nutrients, micropollutants, and heavy metals threatening the ecological health of aquatic ecosystems. However, the long-term spatiotemporal dynamics of lake TSM in China and their response to natural and anthropogenic factors are rarely explored. In this study, based on Landsat top-of-atmosphere (TOA) reflectance embedded in GEE and in-situ TSM data collecting in the periods 2014-2020, we developed a unified empirical model (R² = 0.87, RMSE = 10.16 mg/L, and MAPE = 38.37 %) to retrieve the autumn TSM of lakes at national scale. This model exhibited stable and reliable performances through transferability validation and comparative analysis with published TSM models, and was implemented to generate autumn TSM maps for large lakes (≥50 km²) across China during 1990-2020.We found that 78.03 % of large lakes with TSM < 20 mg/L were dominant in 2020 across China, and these lakes were mainly located in the plateau and mountain regions. In the first gradient terrain (FGT) and second gradient terrain (SGT), the number of lakes showing significant (p < 0.05) decreasing TSM trends increased from 1990-2004 to 2004-2020, while those with opposite directions in TSM decreased. Lakes in the third gradient terrain (TGT) exhibited the inverse quantitative change in these two TSM trends compared with the FGT and SGT. A relative contribution analysis at the watershed level indicated that the first two leading factors that control TSM significant change in the FGT were lake area and wind speed, in the SGT were lake area and NDVI, and in the TGT were population and NDVI, respectively. The impacts of anthropogenic factors on lakes are continuing, particularly in eastern China, and more efforts are needed to improve and protect the water environment in the future. Our findings might help water resource managers better grasp the current state of water quality.

Mapping global distribution of mangrove forests at 10-m resolution

Mangrove forests deliver incredible ecosystem goods and services and are enormously relevant to sustainable living. An accurate assessment of the global status of mangrove forests warrants the necessity of datasets with sufficient information on spatial distributions and patch patterns. However, existing datasets were mostly derived from ∼30 m resolution satellite imagery and used pixel-based image classification methods, which lacked spatial details and reasonable geo-information. Here, based on Sentinel-2 imagery, we created a global mangrove forest dataset at 10-m resolution, namely, High-resolution Global Mangrove Forests (HGMF_2020), using object-based image analysis and random forest classification. We then analyzed the status of global mangrove forests from the perspectives of conservation, threats, and resistance to ocean disasters. We concluded the following: (1) globally, there were 145,068 km² mangrove forests in 2020, among which Asia contained the largest coverage (39.2%); at the country level, Indonesia had the largest amount of mangrove forests, followed by Brazil and Australia. (2) Mangrove forests in South Asia were estimated to be in the better status due to the higher proportion of conservation and larger individual patch size; in contrast, mangrove forests in East and Southeast Asia were facing intensive threats. (3) Nearly, 99% of mangrove forest areas had a patch width greater than 100 m, suggesting that nearly all mangrove forests were efficient in reducing coastal wave energy and impacts. This study reports an innovative and up-to-date dataset and comprehensive information on mangrove forests status to contribute to related research and policy implementation, especially for supporting sustainable development.

[Mechanism study of platelet derived growth factor receptor alpha on the bidirectional differentiation regulation of glioma-associated oncogene homolog 1-positive mesenchymal stem cells in mice]

Objective: To investigate the role of platelet derived growth factor receptor alpha (PDGFRα) on bidirectional differentiation of glioma-associated oncogene homolog 1-positive mesenchymal stem cells (Gli1+-MSC). Methods: Breeding double reporter transgenic mice ROSAmT/mG/Gli1-Cre^ERt2/PDGFRα^fl (Experimental group) and ROSA^mT/mG/Gli1-Cre^ERt2 (Control group), 20 mice in each of the two groups at four weeks of age were selected, MSC were isolated from the mouse aortic epithelium. After tamoxifen inducement, the two groups of Gli1⁺-MSC were screened by green fluorescent protein (GFP) labeling and flow cytometry sorting. PDGFRα was conditionally knocked out in the experimental group, and the control group Gli1⁺-MSC expressed PDGFRα normally. The two groups of Gli1⁺-MSC were subjected to adipogenic induction and fibrogenic induction, the Western blotting was performed to detect PDGFRα, adipocyte markers [perilipin and CCAAT/enhancer binding protein alpha (C/EBPα)] and fibrogenic markers [alpha smooth muscle actin (α-SMA) and fibroblast-specific protein 1 (FSP-1)] and semi-quantitative analysis was performed. The degree of cellular adipose differentiation after bidirectional induction of Gli1+-MSC in both groups was observed by oil red O staining and analyzed semi-quantitatively. Results: After tamoxifen induction, Gli1⁺-MSC could be accurately isolated from flow cytometry by GFP labeling. Via adipogenic differentiation, the expression of PDGFRα in the experimental group (0.017±0.002) was significantly lower than that in the control group (0.184±0.012) (t=25.48，P=0.002). The protein expressions of perilipin (3.138±0.414) and C/EBPα (3.565±0.289) were significantly higher than those in the control group (2.312±0.218 and 2.179±0.103, respectively) (t=6.21，P=0.025；t=6.69，P=0.022). Thus, the knock-out of PDGFRα enhanced the adipogenic differentiation ability of Gli1⁺-MSC. After fibrogenesis induction, the protein expressions of PDGFRα, α-SMA and FSP-1 in the experimental group (0.030±0.001, 0.932±0.177 and 0.276±0.020, respectively) were significantly lower than those in the control group (0.439±0.006, 1.352±0.170 and 0.835±0.097, respectively) (t=149.40, P<0.001; t=66.38,P<0.001; t=11.41,P<0.08). This suggested that the knock-out of PDGFRα significantly inhibited Gli1⁺-MSC differentiation toward fibroblasts. After bidirectional induction, significantly less adipocyte formation was seen in the control group and more in the experimental group. Quantitative analysis showed that the amount of oil red O staining in the experimental group (0.461±0.042) was significantly higher than that in the control group (0.017±0.007) after bidirectional induction (t=23.20, P<0.01). Conclusions: PDGFRα plays an important role in the regulation of bidirectional differentiation of vascular adventitial Gli1+-MSC.

Remote sensing of fluorescent humification levels and its potential environmental linkages in lakes across China

The pollution or eutrophication affected by dissolved organic matter (DOM) composition and sources of inland waters had attracted concerns from the public and government in China. Combined with remote sensing techniques, the fluorescent DOM (FDOM) parameters accounted for the important part of optical constituent as chromophoric dissolved organic matter (CDOM) was a useful tool to trace relative DOM sources and assess the trophic states for large-scale regions comprehensively and timely. Here, the objective of this research is to calibrate and validate a general model based on Landsat 8 OLI product embedded in Google Earth Engine (GEE) for deriving humification index (HIX) based on EEMs in lakes across China. The Landsat surface reflectance was matched with 1150 pairs fieldtrip samples and the nine sensitive spectral variables with good correlation with HIX were selected as the inputs in machine learning methods. The calibration of XGBoost model (R² = 0.86, RMSE = 0.29) outperformed other models. Our results indicated that the entire dataset of HIX has a strong association with Landsat reflectance, yielding low root mean square error between measured and predicted HIX (R² = 0.81, RMSE = 0.42) for lakes in China. Finally, the optimal XGBoost model was used to calculate the spatial distribution of HIX of 2015 and 2020 in typical lakes selected from the Report on the State of the Ecology and Environment in China. The significant decreasing of HIX from 2015 to 2020 with trophic states showed positive control of humification level of lakes based on the published document of Action plan for prevention and control of water pollution in 2015 of China. The calibrated model would greatly facilitate FDOM monitoring in lakes, and provide indicators for relative DOM sources to evaluate the impact of water protection measures or human disturbance effect from Covid-19 lockdown, and offer the government supervision to improve the water quality management for lake ecosystems.

Analysis of the Safety and Pregnancy Outcomes of Fertility-sparing Surgery in Ovarian Malignant Sex Cord-stromal Tumours: A Multicentre Retrospective Study

AIMS

To assess the difference in survival between fertility-sparing surgery (FSS) and radical surgery and explore pregnancy outcomes after FSS in stage I malignant sex cord-stromal tumours (MSCSTs).

MATERIALS AND METHODS

We carried out a multicentre retrospective cohort study on patients who were diagnosed with MSCSTs and the tumour was confined to one ovary. The patients were divided into FSS and radical surgery groups. Inverse probability of treatment weighting (IPTW) was used to balance variables between the two groups. Kaplan-Meier analysis was used to compare the difference in disease-free survival (DFS). Univariate and multivariate Cox regression analysis was used to find risk factors of DFS. Univariate logistic regression analysis was used to assess risk factors of pregnancy.

RESULTS

In total, 107 patients were included, of whom 54 (50.5%) women underwent FSS and 53 (49.5%) received radical surgery. After IPTW, a pseudo-population of 208 was determined and all of the covariates were well balanced. After a median follow-up time of 50 months (range 7-156 months), 10 patients experienced recurrence and two died. There was no significant difference in DFS between the two groups, both in unweighted (P = 0.969) or weighted cohorts (P = 0.792). In the weighted cohort, stage IC (P = 0.014), tumour diameter >8 cm (P = 0.003), incomplete staging surgery (P = 0.003) and no adjuvant chemotherapy (P < 0.001) were the four high-risk factors associated with a shorter DFS. Among 14 patients who had pregnancy desire, 11 (78.6%) women conceived successfully; the live birth rate was 76.9%. In univariate analysis, only adjuvant chemotherapy (P = 0.009) was associated with infertility.

CONCLUSIONS

On the premise of complete staging surgery, FSS is safe and feasible in early stage MSCSTs with satisfactory reproductive outcomes.

Relationship between Social Support, Sarcopenia, and Cognitive Impairment in Chinese Community-Dwelling Older Adults

BACKGROUND

Cognitive impairment and sarcopenia have become important challenges for the growing aging population. Social support has been shown to protect against cognitive impairment, but its impact on sarcopenia remains unknown. The purpose of this study was to explore the correlation between social support, sarcopenia, and cognitive impairment in Chinese older adults.

METHOD

A multi-stage whole group sampling method was used to conduct a cross-sectional survey of 720 community-dwelling older people in Shanghai. The definition of sarcopenia was in accordance with the criteria of the Asian Working Group for Sarcopenia (AWGS) 2019. Cognitive impairment was evaluated using a computerized neuropsychological assessment device that had been previously validated. Social support was assessed using the Social Support Rate Scale. Logistic regression analyses were conducted to explore the relationship between social support cognitive impairment and sarcopenia, fully adjusting for all potential confounding factors.

RESULTS

Our study found that 230 (31.94%) of the participants had cognitive impairment and 97 (13.47%) of the participants had sarcopenia. The mean social support score was 35.10 ± 7.54. Besides, the results showed that cognitive impairment was associated with sarcopenia (OR:1.650, 95% CI: 1.048, 2.596, P=0.030) after adjusting for confounding factors. Older adults with high level social support had the lowest risk of cognitive impairment (OR: 0.297, 95% CI: 0.115, 0.680, P=0.021) and sarcopenia (OR: 0.113, 95% CI: 0.031, 0.407, P=0.001), respectively.

CONCLUSION

Our analysis revealed that high level social support was negatively associated with sarcopenia and cognitive impairment. These findings provide strong support for the health promotion effect of social networks against sarcopenia and cognitive impairment in Chinese community-dwelling older adults, with important implications for healthcare policy makers.

Variation of satellite-derived total suspended matter in large lakes with four types of water storage across the Tibetan Plateau, China

Total suspended matter (TSM), as an indicator of the concentration of fine materials in the water column including particulate nutrients, pollutants, and heavy metals, is widely used to monitor aquatic ecosystems. However, the long-term spatiotemporal variations of TSM in lakes across the Tibetan Plateau (TP) and their response to environmental factors are rarely explored. Accordingly, taking advantage of the Landsat top-of-atmosphere reflectance and in-situ data, an empirical model (R² = 0.83, RMSE = 1.08 mg/L, and MAPE = 19.49 %) was developed to estimate the average autumnal TSM in large TP lakes (≥50 km²) during the 1990-2020 period. For analyzing the spatiotemporal variability in TP lakes TSM, the examined lakes were classified into four types (Type A-D) based on their water storage changing in different periods. The results showed that the lakes in the southern and some northeastern parts of the TP exhibited lower TSM values than those situated in other regions. The assessment of TSM in each of these four lake types showed that more than half of them had a TSM value of <20 mg/L. Apart from Type D, the lakes with the TSM showing significantly decreasing trends were dominantly Types A-C. A relative contribution analysis involving five driving factors indicated that they contributed by >50 % to lake TSM interannual variation in 73 out of 114 watersheds, and the lakes area change demonstrated the greatest contribution (82.2 %), followed by wind speed (11.0 %). Further comparison between the entire lake and the non-expansive regions suggested that the expansive region played an indispensable role in determining the TSM value of the whole lake. This study can help to better understand the water quality condition and provide valuable information for policy-makers to maintain sustainable development in the TP region.

Injectable carbon nanotube-functionalized hydrogel as a tool for cardiac tissue engineering

Background/Introduction

Heart failure (HF) is an expensive major public health problem in the United States and around the world (1). The current treatments for HF are aimed at reducing symptoms, slowing disease progression, and reducing mortality and not aimed at repairing heart muscle or restoring function. Furthermore, even with these treatments, approximately half of patients with HF will die within 5 years of diagnosis (2). Cardiac transplantation remains the only definitive treatment for those affected with end-stage HF, but availability of donor hearts remains a major limitation (3).

Purpose

The ability of the adult heart to regenerate cardiomyocytes (CMs) lost after injury is limited, generating interest in developing tissue engineering therapies to avoid progression towards HF. Rigid carbon nanotubes (CNTs) scaffolds have been used to improve CMs viability, proliferation, and maturation (4), but require undesirable invasive surgeries for implantation. To overcome this limitation, we engineered an injectable reverse thermal gel (RTG) functionalized with CNTs (RTG-CNT) that transitions from a liquid-solution to a gel-based matrix shortly after reaching body temperature allowing for a liquid-based delivery rapidly followed by a stable-gel localization (5).

Methods and results

Here we show experimental evidences the RTG-CNT hydrogel, used as a three-dimensional (3D) niche to culture human induced pluripotent stem cells (hiPSC)-CMs, promotes hiPSC-CMs alignment and elongation with increased Cx43 localization and improved contraction function when compared with traditional two-dimensional (2D) fibronectin controls and plain 3D RTG system without CNTs. Moreover, the short-term (4-week) biocompatibility of the RTG-CNT hydrogel was also assessed in a mouse model (intracardial injection). The results confirmed that the RTG-CNT hydrogel is well tolerated by the cardiac tissue.

Conclusion

Our results indicated that the injectable RTG-CNT hydrogel has the potential to be used as a minimally invasive tool for cardiac tissue engineering efforts.

Funding Acknowledgement

Type of funding sources: Other. Main funding source(s): NATIONAL HEART, LUNG, AND BLOOD (NHLBI) INSTITUTE

Composition of dissolved organic matter (DOM) in lakes responds to the trophic state and phytoplankton community succession

Dissolved organic matter (DOM), a heterogeneous mixture of diverse compounds with different molecular weights, is crucial for the lake carbon cycle. The properties and concentration of DOM in lakes are closely related to anthropogenic activities, terrigenous input, and phytoplankton growth. Thus, the lake's trophic state, along with the above factors, has an important effect on DOM. We determined the DOM sources and molecular composition in six lakes along a trophic gradient during and after phytoplankton bloom by combining optical techniques and the Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). CDOM pools in eutrophic lakes may be more biologically refractory than in oligotrophic and mesotrophic lakes. Molecular formulas of DOM were positively correlated with the TSI (trophic state index) value (R² = 0.73), with the nitrogen-containing compounds (CHON) being the most abundant formulas in all studied lakes. Eutrophication modified the molecular formulas of DOM to have less CHO% and more heteroatom S-containing compounds (CHOS% and CHNOS%), and this was the synactic result of the anthropogenic perturbation and phytoplankton proliferation. In eutrophic lakes, summer DOM showed higher molecular lability than in autumn, which was related to the seasonal phytoplankton community succession. Although the phytoplankton-derived DOM is highly bioavailable, we detected a simpler and more fragile phytoplankton community ecosystem in autumn, which may be accompanied by a lower phytoplankton production and metabolic activity. Therefore, we concluded that the lake eutrophication increased the allochthonous DOM accumulation along with sewage and nutrient input, and subsequently increased its release with phytoplankton bloom. Eutrophication and phytoplankton growth are accompanied by more highly unsaturated compounds, O₃S+O₅S compounds, and carboxylic-rich alicyclic compounds (CRAMs), which are the biotransformation product of phytoplankton-derived DOM. Eutrophication may be a potential source of refractory DOM compounds for biodegradation and photodegradation. Our results can clarify the potential role of water organic matter in the future global carbon cycle processes, considering the increasing worldwide eutrophication of inland waters.

Natural versus anthropogenic controls on the dissolved organic matter chemistry in lakes across China: Insights from optical and molecular level analyses

Dissolved organic matter (DOM) plays an essential role in the global carbon biogeochemical cycle for aquatic ecosystems. The complexity of DOM compounds contributes to the accurate monitoring of its sources and compositions from large-scale patterns to microscopic molecular groups. Here, this study demonstrates the diverse sources and compositions for humic-rich lakes and protein-rich lakes for large-scale regions across China with the linkage to optical components and molecular high-resolution mass spectrometry properties. The total fluorescence intensity of colored DOM (CDOM) for humic-rich lake regions (0.176 Raman unit; R.U.) is significantly (p<0.05) higher than that of the protein-rich lake region (0.084 R.U.). The combined percentages of CDOM absorption variance explained by the anthropogenic and climatic variables across the five lake regions of Northeastern lake region (NLR), Yungui Plateau lake region (YGR), Inner Mongolia-Xinjiang lake region (MXR), Eastern lake region (ELR), and Tibetan-Qinghai Plateau lake region (TQR) were 86.25%, 82.57%, 80.23%, 88.55%, and 87.72% respectively. The averaged relative intensity percentages of CHOS and CHONS formulas from humic-rich lakes (90.831‰, 10.561‰) were significantly higher than that from the protein-like lakes (47.484‰, 5.638‰), respectively. The more complex molecular composition with higher aromaticity occurred in the humic-rich lakes than in the protein-rich lakes. The increasing anthropogenic effects would significantly enhance the sources, transformation, and biodegradation of terrestrial DOM and link to the greenhouse gas emission and the carbon cycle in inland waters.

[Prognosis and fertility outcomes of patients with borderline ovarian tumors after fertility-sparing surgery]

Objective: To explore the clinical prognosis and fertility outcomes in patients with borderline ovarian tumors (BOT) who underwent fertility-sparing surgery, and evaluate the related risk factors. Methods: The study examined the clinicopathological characteristics of 280 patients diagnosed with BOT from Qilu Hospital of Shandong University between January 2009 and December 2019. According to the surgery plan, the patients were divided into the fertility-sparing group (167 cases) and the radical surgery group (113 cases). The information of the patients' age, preoperative serum CA-125 level, surgery method, pathological type, FIGO stage (2014), tumor location, and whether focal canceration combined were collected. The Kaplan-Meier method was used to compare disease-free survival (DFS) between the fertility-sparing surgery group and the radical surgery group. The univariate and multivariate Cox proportional hazard regression analysis was used to explore high-risk factors associated with DFS. Results: A total of 280 BOT patients were identified in the study, with a median age of 35.0 (26.0, 51.0) years old. The median follow-up time was 55.2 (34.7, 79.3)months. 25 patients (15.0%) developed recurrence in the fertility-sparing surgery group, 11 patients (8.7%) developed recurrence in the radical surgery group. There was no significant difference in 5-year DFS rate between the two groups (84.4% vs 90.1%, P=0.223). Only FIGO stage was found to be related to DFS through the univariable and multivariable Cox proportional hazard regression analysis, and patients with FIGO Ⅱ/Ⅲ had higher risk of recurrence [HR (95%CI) 2.872(1.283-6.431)] (P=0.010); Fertility-sparing surgery does not increase the recurrence risk of BOT patients (P=0.116). Pregnancies were reported in 39 patients (54.2%), among whom 37 patients gave birth successfully, and 2 patients selected to terminate pregnancy. Conclusions: The fertility-sparing surgery does not increase the risk of recurrence in BOT patients, and patients who underwent the fertility-sparing surgery have a favorable outcome. FIGO stage is the independent risk factor of DFS in BOT patients.

Global divergent trends of algal blooms detected by satellite during 1982-2018

Algal blooms (ABs) in inland lakes have caused adverse ecological effects, and health impairment of animals and humans. We used archived Landsat images to examine ABs in lakes (>1 km² ) around the globe over a 37-year time span (1982-2018). Out of the 176032 lakes with area >1 km² detected globally, 863 were impacted by ABs, 708 had sufficiently long records to define a trend, and 66% exhibited increasing trends in frequency ratio (FRQR, ratio of the number of ABs events observed in a year in a given lake to the number of available Landsat images for that lake) or area ratio (AR, ratio of annual maximum area covered by ABs observed in a lake to the surface area of that lake), while 34% showed a decreasing trend. Across North America, an intensification of ABs severity was observed for FRQR (p < .01) and AR (p < .01) before 1999, followed by a decrease in ABs FRQR (p < .01) and AR (p < .05) after the 2000s. The strongest intensification of ABs was observed in Asia, followed by South America, Africa, and Europe. No clear trend was detected for the Oceania. Across climatic zones, the contributions of anthropogenic factors to ABs intensification (16.5% for fertilizer, 19.4% for gross domestic product, and 18.7% for population) were slightly stronger than climatic drivers (10.1% for temperature, 11.7% for wind speed, 16.8% for pressure, and for 11.6% for rainfall). Collectively, these divergent trends indicate that consideration of anthropogenic factors as well as climate change should be at the forefront of management policies aimed at reducing the severity and frequency of ABs in inland waters.

Estimation of the lake trophic state index (TSI) using hyperspectral remote sensing in Northeast China

The Trophic state index (TSI) is a vital parameter for aquatic ecosystem assessment. Estimating TSI by remote sensing is still a challenge due to the multivariate complexity of the eutrophication process. A comprehensive in situ spectral-biogeochemical dataset for 7 lakes in Northeast China was collected in October 2020. The dataset covers trophic states from oligotrophic to eutrophic, with a wide range of total phosphorus (TP, 0.07-0.2 mg L^-1), Secchi disk depth (SDD, 0.1-0.78 m), and chlorophyll a (Chla, 0.11-20.41 μg L^-1). Here, we propose an empirical method to estimate TSI from remote sensing data. First, TP, SDD, and Chla were estimated by band ratio/band combination models. Then TSI was estimated using the Carlson model with a high R² (0.88), a low RMSE (3.87), and a low MRE (6.83%). Synergistic effects between TP, SDD, and Chla dominated the trophic state, changed the distribution of light in the water column, affected the spectral characteristics. Furthermore, the contribution of each parameter for eutrophication were different among the studied lakes from ternary plot. High Chla concentration was the main reason for eutrophication in HMT Lake with 45.4% of contribution more than the other two parameters, However, in XXK Lake, high TP concentrations were the main reason for eutrophication with 66.8% of contribution rather than Chla and SDD. Overall, the trophic state was dominated by TP, and SDD accounted for 85.6% of contribution in all sampled lakes. Additionally, we found using one-parameter index to evaluate the lake trophic state will lead to a great deviation, even with two levels of difference. Therefore, multi-parameter TSI is strongly recommended for the lake trophic state assessment. Summarily, our findings provide a theoretical and methodological basis for future large-scale estimations of lake TSI using satellite image data, help with water quality monitoring and management.

A unified model for high resolution mapping of global lake (>1 ha) clarity using Landsat imagery data

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, R² = 0.84, MAPE = 29.8%) against SDD measured in 2235 lakes across the world, and the validation (n = 1779, R² = 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.

Total suspended solids characterization and management implications for lakes in East China

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.

Climate-driven variations in suspended particulate matter dominate water clarity in shallow lakes

Secchi disk depth (SDD) has long been considered as a reliable proxy for lake clarity, and an important indicator of the aquatic ecosystems. Meteorological and anthropogenic factors can affect SDD, but the mechanism of these effects and the potential control of climate change are poorly understood. Preliminary research at Lake Khanka (international shallow lake on the China-Russia border) had led to the hypothesis that climatic factors, through their impact on suspended particulate matter (SPM) concentration, are key drivers of SDD variability. To verify the hypothesis, Landsat and MODIS images were used to examine temporal trend in these parameters. For that analysis, the novel SPM index (SPMI) was developed, through incorporation of SPM concentration effect on spectral radiance, and was satisfactorily applied to both Landsat (R²= 0.70, p < 0.001) and MODIS (R²= 0.78, p < 0.001) images to obtain remote estimates of SPM concentration. Further, the SPMI algorithm was successfully applied to the shallow lakes Hulun, Chao and Hongze, demonstrating its portability. Through analysis of the temporal trend (1984-2019) in SDD and SPM, this study demonstrated that variation in SPM concentration was the dominant driver (explaining 63% of the variation as opposed to 2% due to solar radiation) of SDD in Lake Khanka, thus supporting the study hypothesis. Furthermore, we speculated that variation in wind speed, probably impacted by difference in temperature between lake surface and surrounding landscapes (greater difference between 1984-2009 than after 2010), may have caused varying degree of sediment resuspension, ultimately controlling SPM and SDD variation in Lake Khanka.

Development of total suspended matter prediction in waters using fractional-order derivative spectra

More and more hyper-spectral satellites will be used to estimate total suspended matter (TSM) in waters instead of multi-spectral satellites, such as China's Gaofen-5 and Zhuhai-1. Although they have not been widely used because of the consistency of sampling and image time. Hence, the study based on measured hyper-spectroscopy is important for applying to hyper-spectral satellites. Fractional-order derivatives (FODs) considers more detailed spectral information, and it is a better spectral preprocessing method than conventional integer-order derivatives. The application and analysis of FODs for spectra in waters is rare. If FOD is successfully applied to estimate TSM, the TSM mapping with FOD using hyper-spectral satellites will be meaningful. Based on these points, this study aimed to apply FOD to predict TSM and to prove the prediction feasibility of FOD in waters. Different prediction models and eight FOD transformation processes with increment of 0.25 per step for 392 spectral reflectance data from China were used and compared. The prediction models include the optimum models of the single wavelength, ratio index, difference index and TSM index at each FOD order, and the random forest (RF) model with all wavelengths was also used. Discrete wavelet transform (DWT) was used to reduce noise and improve the model accuracy after using FOD. Our results achieved the followings First, FOD enhanced spectral characteristics at 500-600 nm and 800 nm that were affected by TSM. Second, the correlation between TSM and FOD spectra was enhanced (e.g., the correlation coefficients of 19 wavelengths (789-807 nm) of 0.75-order were higher than 0.8 but the original spectra were not). Third, FOD improved the performance of different prediction models, and the RF model from 0.5-order to 1.25-order derivative spectra all led good results (). Fourth, DWT can reduce the noise and improve the performance, and FOD-DWT model of 1.25-order led the R² of 0.84, RMSE of 16.30 and MAPE of 78.62 in validation. Overall, our results suggest that FOD can improve the prediction performance for most models, and the optimum order of some models is not integer. Our results also provide a reference for predicting other water quality parameters and mapping these parameters using hyper-spectral satellites. The accurate estimation of TSM is helpful for protecting ecological and social environments.

Natural and anthropogenic impacts on the DOC characteristics in the Yellow River continuum

The Yellow River is the second largest river in China. Carbon transport by the Yellow River has significant influence on riverine carbon cycles in Asia. During the wet season, the riverine carbon was mainly found in dissolved form, i.e., dissolved organic carbon (DOC), along the entire course of the river. The distinct spatial variations of DOC concentration were observed at different reaches of the mainstream (p < 0.01), while the highest mean DOC concentration was generally observed at midstream (4.13 ± 0.91 mg/L). Carbon stable isotope analysis δ¹³C and C: N ratio of DOC, evidenced the sources of DOC in headwater and upstream were primarily the terrestrial plants (94% and 61%), but it was changed to soil organic matter (SOM) in mid- and downstream (36% and 37%), and the contribution of sewage to DOC were also increased to 17% and 18%. In the whole mainstream of the Yellow River, water temperature (WT) had a significant impact on DOC concentration, and it could explain 67% of the DOC variance. However, in a large catchment, the driving mechanisms on the DOC variations in headwaters will not necessarily be those controlling DOC trends in downstream. The study firstly quantified, in headwater and upstream, the natural factors explained as much as 65% and 73% of the DOC variations, respectively. In mid- and downstream areas, DOC was significantly influenced by the amount of wastewater discharged by the industry and the use of chemical fertilizers (p < 0.05). These findings may facilitate a better assessment of global riverine carbon cycling and may help to reveal the importance of the balance between development and environmental sustainability with the changing DOC transport features in the Yellow River due to human disturbances.

Factors influencing the effective management of diabetes during humanitarian crises in low- and middle-income countries: a systematic review

OBJECTIVES

The management of non-communicable chronic diseases such as diabetes mellitus is often poor in humanitarian crisis settings. Poor disease management can have serious long-term consequences. This review sought to identify studies that explored the effectiveness of interventions and the determinants that may improve the management of diabetes in the humanitarian context.

STUDY DESIGN

A systematic review was conducted of all types of studies that implemented diabetes programmes in humanitarian settings.

METHODS

MEDLINE, Embase, CINAHL and Web of Science were searched for relevant articles using multiple search terms for relevant studies published in English between 2000 and June 2020. Quality assessments using valid tools, were conducted. A narrative synthesis of the findings was then performed.

RESULTS

The search yielded 550 citations. After de-duplicating and screening, 19 studies were eligible for inclusion. Most studies were conducted in the Middle East (74%), Asia (16%) and Africa (10%). The interventions reported to improved diabetes care included the mHealth tool, intensive lifestyle intervention, task-shifting, psychosocial support, distribution of glucometers and comprehensive care. Insufficient drug supply, out-of-pocket cost, the complexity of insulin therapy and low adherence to guidelines were identified as barriers.

CONCLUSION

There is a paucity of evidence on optimal diabetes care in acute crisis and natural disaster settings. The review was constrained by the low quality of the studies included. Nevertheless, it is likely that mHealth can be feasibly utilised, as educational SMS messages are cost-effective and electronic medical records can enhance disease monitoring. In addition, a multidisciplinary approach to care improves glycaemic control and is desirable due to the multifaceted nature of the disease and management required. Several key barriers have been identified that need to be tackled. Overall, the evidence base for diabetes care in humanitarian settings remains sparse, and further research is needed.

Variations in the light absorption coefficients of phytoplankton, non-algal particles and dissolved organic matter in reservoirs across China

Reservoirs were critical sources of drinking water for many large cities around the world, but progress in the development of large-scale monitoring protocols to obtain timely information about water quality had been hampered by the complex nature of inland waters and the various optical conditions exhibited by these aquatic ecosystems. In this study, we systematically investigated the absorption coefficient of different optically-active constituents (OACs) in 120 reservoirs of different trophic states across five eco-regions in China. The relationships were found between phytoplankton absorption coefficient at 675 nm (a_ph (675)) and Chlorophyll a (Chla) concentration in different regions (R²:0.60-0.82). The non-algal particle (NAP) absorption coefficient (a_NAP) showed an increasing trend for reservoirs with trophic states. Significant correlation (p < 0.05) was observed between chromophoric dissolved organic matter (CDOM) absorption and water chemical parameters. The influencing factors for contributing the relative proportion of OACs absorption including the hydrological factors and water quality factors were analyzed. The non-water absorption budget from our data showed the variations of the dominant absorption types which underscored the need to develop and parameterize region-specific bio-optical models for large-scale assessment in water reservoirs.

Fluorescence spectroscopy of CDOM in urbanized waters across gradients of development/industrialization of China

The optical signature of chromophoric dissolved organic matter (CDOM) has been related to sources and composition of dissolved organic matter (DOM) in surface waters, but the spatial scope of previous research has been limited to single cities with no studies exploring patterns across gradients of development/industrialization or latitude. Using EEM (excitation emission matrix) techniques, a study was conducted to examine optical properties of CDOM in urban waters along a gradient of urban development (developed and undeveloped cities) and industries (primary, secondary, tertiary). The optical properties of CDOM were measured in 436 water samples collected from urbanized waterbodies spanning 93 cities across China. Results showed marked differences of DOM composition for different level of urban development and for different types of dominant industries. The mean a_CDOM(254) for developed cities (14.31 m^-1) was significantly lower (p < 0.05) than that of undeveloped cities (18.01 m^-1). The intensity of the tryptophan-like component (Q₂) of CDOM was significantly higher for developed cities (0.98 ×10¹⁰ nm) than for undeveloped cities (4.6 ×10⁹ nm), whereas the humic-like component (Q₅) intensity was significantly lower for undeveloped cities (19.80 ×10¹⁰ nm) than for developed cities (16.26 ×10¹⁰ nm). Regression analysis showed that the Q₅ component was mainly (and positively) influenced by secondary industries, while Q₂ was mainly affected by both tertiary and secondary industries. The proportion of allochthonous CDOM increased significantly with latitude from south to north with the minimum increased percentage of 67% for humification index (HIX) within different urban development degrees. These findings indicate that changes in urban development, human activities and industrial structure could alter DOM sources and composition in urbanized waterbodies. These findings are relevant to the management of urban water resources in regions experiencing rapid urban and industrial expansion, and add to our understanding of carbon cycling in urbanized freshwater ecosystems.

Songhua River basin's improving water quality since 2005 based on Landsat observation of water clarity

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.

Characterization of dissolved organic matter (DOM) in an urbanized watershed using spectroscopic analysis

Landscape urbanization broadly alter watersheds ecosystems, yet the impact of nonpoint source urban inputs on dissolved organic matter (DOM) amount, composition and source is poorly understood. To systematically examine how DOM optical index and composition varied with urbanization, a unique long term observation dataset (4 years) of fluorescence excitation emission matrices (EEMs) was collected from two types of waters: urban waters and non-urban waters. Two humic-like DOM fluorescent components (C1 and C2) and one protein-like component (C3) were identified by parallel factor analysis (PARAFAC), and the results indicated that urbanization had an important influence on DOM concentration and composition, with urban waters having a high degree of DOM variation due to different land use surrounding each body of water. Urban waters presented higher DOM content, CDOM absorption and DOM fluorescence intensity (FI), a greater proportion of protein-like (26% > 21.3%), and less proportion of humic-like (51.9% < 57.6%) than non-urban waters, were dominated by allochthonous inputs. Moreover, the long-term observation of the urbanized DOM's dynamics was conducted on monthly, seasonal and yearly timescales. The results reflected the response of DOM to regional climate. Higher DOM amount and FI appeared in the summer due to autochthonous production comes from algae growth and allochthonous input comes from rainfall. It also revealed that continuous increase in impervious artificial surfaces caused by urban expansion, contributed to the increase in DOM quantity and drove DOM composition to be more protein-like. Consequently, these findings filled the knowledge gap of the mechanism of land-water interaction on DOM properties in freshwater ecosystems.

Quantification of chlorophyll-a in typical lakes across China using Sentinel-2 MSI imagery with machine learning algorithm

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, R² = 0.91; validation: slope = 1.21, R² = 0.88). On the contrary, the documented nine Chl-a algorithms gave poor results (fitting 1:1 linear slope < 0.4 and R² < 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, R² = 0.78), followed by cluster 3 group (slope = 0.77, R² = 0.64) and cluster 2 group (slope = 0.67, R² = 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.