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Lead (Pb(II)) ions in water pose a significant threat to both human health and aquatic ecosystems. Various approaches have been employed for wastewater treatment, but adsorption is often preferred due to its effectiveness. However, its practical application is limited by the large quantities of adsorbent required, which consequently increases operational costs. In this study, orange-modified iron-oxide nanoparticles (O-Fe 3 O 4 ) are synthesized from agro-waste mass (orange peel), and adsorption experiments were conducted for the removal of Pb(II) from aqueous solution. Characterization studies confirm that O-Fe 3 O 4 nanoparticles possess a mesoporous hexagonal nanocrystalline structure, with diameters measuring less than 100 nm. The adsorption process was optimized using a central composite design framework combined with response surface methodology. The analysis of interaction effects demonstrated that they significantly influenced the effectiveness of adsorption removal. The study revealed that an initial concentration of 25 mg/L, a dosage of 0.2 g/L, a contact period of 90 min, and a pH of 5.5 were the optimum conditions to achieve above 95% of Pb(II) removal. The green synthesized O-Fe 3 O 4 nanoparticles, which presented high efficacy, makes it a promising option for implementing the sustainable water purification.

Soil moisture (SM) plays an essential role in the hydrological cycle, drought monitoring, and water resources management. However, passive microwave remote sensing products offer coarse spatial resolutions (approximately 25–40 km), greatly limiting their applications. In this study, we considered vegetation memory and increased the amount of vegetation data by spatiotemporal fusion, integrated lagged vegetation index and fusion data as model inputs, and downscaled the spatial resolution of SMAP SM using the Random Forest (RF) model as well as multi-source remote sensing data. The downscaled SM was validated with 30 in situ SM measurements and daily precipitation data and explored the contributions of NDVIlagged and NDVIfused in downscaling. The results show that the downscaled SM is highly consistent with the in situ SM measurements. The correlation ranges from 0.628 to 0.849 with an average of 0.689. The root mean square error (RMSE) ranges from 0.024 to 0.094 m3/m3 with an average of 0.057 m3/m3. The proposed method leverages the strengths of the RF model, fusion vegetation data, and lagged vegetation data to improve high-resolution SM accuracy.

Most studies on building height estimation using remote sensing imagery mainly focus on urban buildings with relatively regular shape and relatively flat terrain, and pay little attention to large buildings with complex terrain like dams. A new dam height measurement method was proposed in this paper, which used shadow measurement data and metadata from multiple image sources. The method not only considers the geometric relationship between the sun, the satellite and the dam, but also considers the influence of the satellite zenith and introduces the correction factor, which brings the higher precision. Two dams with a maximum height of more than 200 m were studied, and the dam height is estimated by considering the topography around the dam and the shape of the dam. The experimental results show that the Mean Relative Error (MRE) of the estimated dam height using our proposed method are 3.1% and 4.7% for our two study areas, while the MRE of the traditional models is more than 13%. By doing so, we are able to calculate dam height and obtain information on temporal changes in dam height during the construction process, even in situations without a Digital Surface Model (DSM). Therefore, the proposed method will be propitious to the dynamic supervision of the construction process of the dams effectively.

As crucial water conservancy projects, ship locks play a key role in flood control, shipping, water resource allocation, and promoting regional economic development, making them an indispensable part of the modern water transportation system. Utilizing satellite remote sensing for lock extraction can significantly reduce manual workload and costs, assist in the daily dynamic maintenance of lock hubs, and provide more comprehensive data support for the construction and management of water transport infrastructure. In this context, this paper proposes a new method for ship lock object extraction. Leveraging fuzzy theory and prior knowledge of locks, the extraction of lock objects is achieved from Gaofen-1 (GF-1) high-resolution remote sensing images. The experimental results demonstrate that the proposed algorithm can effectively extract small lock objects in remote sensing images, achieving an average extraction accuracy of 80.9% in the study area.

Peracetic acid (PAA) has attracted increasing attention in wastewater decontamination for yielding reactive oxygen species (ROS). In this study, mackinawite (FeS) was synthesized and applied as the heterogeneous catalyst for PAA activation. Using sulfamethoxazole (SMX) as the target pollutant, the influences of FeS dosage, PAA dosage and initial pH values on SMX degradation by the FeS/PAA process were investigated. Under optimal conditions, SMX could be completely removed within 5 min with the apparent first-order rate constant (kobs) of 7.71 × 10−4 s−1. Electron spin resonance spectroscopy (ESR) analysis and radical scavenging experiments were modulated to detect the active species. During the activation process of PAA, various active components were evidenced to be hydroxyl radicals (HO•), singlet oxygen (1O2), superoxide radicals (O2•−) and organic radicals (R-O•, e.g., CH3C(O)O• and CH3C(O)OO•), whereas HO• and R-O• were demonstrated as the dominant active species responsible for SMX abatement. Both dissolved Fe(II) ions and active sites on the surface of FeS were proven to be activators for PAA, and SMX abatement was highly promoted as a result. Furthermore, the acidic pH displayed superior efficiency in SMX decomposition compared with alkaline and neutral conditions. Five degradation pathways of SMX were put forward and the toxicity of byproducts was further evaluated. Overall, this study provided an efficient and environmentally friendly PAA activation approach using FeS, which might broaden its applicability in the remediation of micropollutants.

Traditional lettuce counting relies heavily on manual labor, which is laborious and time-consuming. In this study, a simple and efficient method for localization and counting lettuce is proposed, based only on lettuce field images acquired by an unmanned aerial vehicle (UAV) equipped with an RGB camera. In this method, a new lettuce counting model based on the weak supervised deep learning (DL) approach is developed, called LettuceNet. The LettuceNet network adopts a more lightweight design that relies only on point-level labeled images to train and accurately predict the number and location information of high-density lettuce (i.e., clusters of lettuce with small planting spacing, high leaf overlap, and unclear boundaries between adjacent plants). The proposed LettuceNet is thoroughly assessed in terms of localization and counting accuracy, model efficiency, and generalizability using the Shanghai Academy of Agricultural Sciences-Lettuce (SAAS-L) and the Global Wheat Head Detection (GWHD) datasets. The results demonstrate that LettuceNet achieves superior counting accuracy, localization, and efficiency when employing the enhanced MobileNetV2 as the backbone network. Specifically, the counting accuracy metrics, including mean absolute error (MAE), root mean square error (RMSE), normalized root mean square error (nRMSE), and coefficient of determination (R2), reach 2.4486, 4.0247, 0.0276, and 0.9933, respectively, and the F-Score for localization accuracy is an impressive 0.9791. Moreover, the LettuceNet is compared with other existing widely used plant counting methods including Multi-Column Convolutional Neural Network (MCNN), Dilated Convolutional Neural Networks (CSRNets), Scale Aggregation Network (SANet), TasselNet Version 2 (TasselNetV2), and Focal Inverse Distance Transform Maps (FIDTM). The results indicate that our proposed LettuceNet performs the best among all evaluated merits, with 13.27% higher R2 and 72.83% lower nRMSE compared to the second most accurate SANet in terms of counting accuracy. In summary, the proposed LettuceNet has demonstrated great performance in the tasks of localization and counting of high-density lettuce, showing great potential for field application.

Low temperature is a type of abiotic stress affecting the tomato (Solanum lycopersicum) growth. Understanding the mechanisms and utilization of exogenous substances underlying plant tolerance to cold stress would lay the foundation for improving temperature resilience in this important crop. Our study is aiming to investigate the effect of exogenous glycine betaine (GB) on tomato seedlings to increase tolerance to low temperatures. By treating tomato seedlings with exogenous GB under low temperature stress, we found that 30 mmol/L exogenous GB can significantly improve the cold tolerance of tomato seedlings. Exogenous GB can influence the enzyme activity of antioxidant defense system and ROS levels in tomato leaves. The seedlings with GB treatment presented higher Fv/Fm value and photochemical activity under cold stress compared with the control. Moreover, analysis of high-throughput plant phenotyping of tomato seedlings also supported that exogenous GB can protect the photosynthetic system of tomato seedlings under cold stress. In addition, we proved that exogenous GB significantly increased the content of endogenous abscisic acid (ABA) and decreased endogenous gibberellin (GA) levels, which protected tomatoes from low temperatures. Meanwhile, transcriptional analysis showed that GB regulated the expression of genes involved in antioxidant capacity, calcium signaling, photosynthesis activity, energy metabolism-related and low temperature pathway-related genes in tomato plants. In conclusion, our findings indicated that exogenous GB, as a cryoprotectant, can enhance plant tolerance to low temperature by improving the antioxidant system, photosynthetic system, hormone signaling, and cold response pathway and so on.

Currently, there is an absence of an exhaustive elucidation regarding the long-term spatiotemporal characteristics of meteorological droughts in China. Previous studies have predominantly fixated on the lower-dimensional (temporal or spatial) facets of drought events, overlooking critical spatiotemporal details. This paper proposes an integrated processing framework that employs a three-dimensional clustering algorithm to discern drought events and quantifies the spatiotemporal patterns and dynamic attributes of China’s meteorological droughts during 1950–2021 from a three-dimensional perspective encompassing longitude, latitude, and time, capturing comprehensive spatiotemporal information. Additionally, the cross wavelet transform is harnessed to elucidate the impact of teleconnection factors on meteorological drought. The findings underscore that prolonged and extensive meteorological drought events, characterized by heightened severity, primarily manifest during the 1950 s, 1960 s, and 2010 s in temporal, predominantly within the central and western China spatially. From 1950 onwards, the severity, duration, and affected area of meteorological droughts in China exhibit a discernible pattern of initial decline followed by escalation. Into the 2010 s, meteorological droughts became more severe and active. China's meteorological drought displays two relatively prevailing spatial migration patterns: an overarching eastward and westward migration. In the former, the most frequent migration occurs to the east and northeast during its monthly evolution, while the latter involves migration primarily towards the west, southwest, and northwest. The impact of teleconnection factors on meteorological drought shows a spatiotemporal heterogeneity, with ENSO exhibiting a relatively substantial and intricate influencer. This research unearths the latest long-term spatiotemporal dynamics of meteorological drought in China, which contributes to an improved comprehension of drought patterns and behaviors.

The study of the spatio-temporal distribution and evolution trends of water resources in large regions plays an important role in the study of regional water resource planning, regional economic and social development, and water disasters. In this study, a Landsat multi-index relationship and water probability thresholding method is developed based on the Google Earth Engine (GEE) platform, which can support the integration of multiple Landsat satellites. The algorithm jointly combines multiple remote sensing metrics along with the calculation of water probability to produce an interannual water body product for the Jianghan Plain on a 20-year time series. The results indicate that the Landsat multi-index relationship algorithm used in this study has high accuracy in extracting long-term water bodies in extensive, flat terrain areas such as the Jianghan Plain, with an overall accuracy (OA) of 97.23%. By analyzing the water body products and landscape patterns, we have identified the following features: (1) From 2002 to 2021, the changes in river water bodies in the Jianghan Plain were relatively small, and some lakes experienced a shrinkage in area. Overall, there is a strong correlation between water distribution and precipitation. (2) The complexity index of water bodies shows a strong negative correlation with effective irrigation area and population, indicating a strong mutual influence between water bodies and socio-economic activities. (3) Through the study of the distribution characteristics of built-up areas and the water system, it was found that for large rivers, the larger the size of the river, the more built-up areas are nearby. Most extensive built-up areas are located near large rivers. This study contributes to providing methods and data support for urban planning, water resource management, and disaster research in the Jianghan Plain.

Abstract Background Homozygous familial hypercholesterolemia (HoFH) is an orphan metabolic disease characterized by extremely elevated low-density lipoprotein cholesterol (LDL-C), xanthomas, aortic stenosis, and premature atherosclerotic cardiovascular disease (ASCVD). In addition to LDL-C, studies in experimental models and small clinical populations have suggested that other types of metabolic molecules might also be risk factors responsible for cardiovascular complications in HoFH, but definitive evidence from large-scale human studies is still lacking. Herein, we aimed to comprehensively characterize the metabolic features and risk factors of human HoFH by using metabolic systems strategies. Methods Two independent multi-center cohorts with a total of 868 individuals were included in the cross-sectional study. First, comprehensive serum metabolome/lipidome-wide analyses were employed to identify the metabolomic patterns for differentiating HoFH patients (n = 184) from heterozygous FH (HeFH, n = 376) and non-FH (n = 100) subjects in the discovery cohort. Then, the metabolomic patterns were verified in the validation cohort with 48 HoFH patients, 110 HeFH patients, and 50 non-FH individuals. Subsequently, correlation/regression analyses were performed to investigate the associations of clinical/metabolic alterations with typical phenotypes of HoFH. In the prospective study, a total of 84 HoFH patients with available follow-up were enrolled from the discovery cohort. Targeted metabolomics, deep proteomics, and random forest approaches were performed to investigate the ASCVD-associated biomarkers in HoFH patients. Results Beyond LDL-C, various bioactive metabolites in multiple pathways were discovered and validated for differentiating HoFH from HoFH and non-FH. Our results demonstrated that the inflammation and oxidative stress-related metabolites in the pathways of arachidonic acid and lipoprotein(a) metabolism were independently associated with the prevalence of corneal arcus, xanthomas, and supravalvular/valvular aortic stenosis in HoFH patients. Our results also identified a small marker panel consisting of high-density lipoprotein cholesterol, lipoprotein(a), apolipoprotein A1, and eight proinflammatory and proatherogenic metabolites in the pathways of arachidonic acid, phospholipid, carnitine, and sphingolipid metabolism that exhibited significant performances on predicting first ASCVD events in HoFH patients. Conclusions Our findings demonstrate that human HoFH is associated with a variety of metabolic abnormalities and is more complex than previously known. Furthermore, this study provides additional metabolic alterations that hold promise as residual risk factors in HoFH population.

IntroductionThe cold stress is one of the most important factors for affecting production throughout year, so effectively evaluating frost damage is great significant to the determination of the frost tolerance in lettuce.MethodsWe proposed a high-throughput method to estimate lettuce FDI based on remote sensing. Red-Green-Blue (RGB) and multispectral images of open-field lettuce suffered from frost damage were captured by Unmanned Aerial Vehicle platform. Pearson correlation analysis was employed to select FDI-sensitive features from RGB and multispectral images. Then the models were established for different FDI-sensitive features based on sensor types and different groups according to lettuce colors using multiple linear regression, support vector machine and neural network algorithms, respectively.Results and discussionDigital number of blue and red channels, spectral reflectance at blue, red and near-infrared bands as well as six vegetation indexes (VIs) were found to be significantly related to the FDI of all lettuce groups. The high sensitivity of four modified VIs to frost damage of all lettuce groups was confirmed. The average accuracy of models were improved by 3% to 14% through a combination of multisource features. Color of lettuce had a certain impact on the monitoring of frost damage by FDI prediction models, because the accuracy of models based on green lettuce group were generally higher. The MULTISURCE-GREEN-NN model with R2 of 0.715 and RMSE of 0.014 had the best performance, providing a high-throughput and efficient technical tool for frost damage investigation which will assist the identification of cold-resistant green lettuce germplasm and related breeding.

BackgroundMachine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. ObjectiveIn a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. MethodsPubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. ResultsIn total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia. ConclusionsStatistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. Trial RegistrationPROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250

In recent years, the invert anomalies of operating railway tunnels in water-rich areas occur frequently, which greatly affect the transportation capacity of the railway lines. Tunnel drainage system is a crucial factor to ensure the invert stability by regulating the external water pressure (EWP). By means of a three-dimensional (3D) printing model, this paper experimentally investigates the deformation behavior of the invert for the tunnels with the traditional drainage system (TDS) widely used in China and its optimized drainage system (ODS) with bottom drainage function. Six test groups with a total of 110 test conditions were designed to consider the design factors and environmental factors in engineering practice, including layout of the drainage system, blockage of the drainage system and groundwater level fluctuation. It was found that there are significant differences in the water discharge, EWP and invert stability for the tunnels with the two drainage systems. Even with a dense arrangement of the external blind tubes, TDS was still difficult to eliminate the excessive EWP below the invert, which is the main cause for the invert instability. Blockage of drainage system further increased the invert uplift and aggravated the track irregularity, especially when the blockage degree is more than 50%. However, ODS can prevent these invert anomalies by reasonably controlling the EWP at tunnel bottom. Even when the groundwater level reached 60 m and the blind tubes were fully blocked, the invert stability can still be maintained and the railway track experienced a settlement of only 1.8 mm. Meanwhile, the on-site monitoring under several rainstorms further showed that the average EWP of the invert was controlled within 84 kPa, while the maximum settlement of the track slab was only 0.92 mm, which also was in good agreement with the results of model test.

In recent years, the tunnels operating in water rich areas of China occasionally suffer from disasters, such as lining cracking, leakage, and track deformation. To develop reasonable and effective treatment measures for tunnel water hazards, this paper investigates the water hazards in tunnels operating in water rich areas of China, and identifies the causes of these water hazards. Considering the on-site situation of water hazards (e.g., track bulging and lining cracking), FLAC3D simulation was implemented to compute the actual water head on the tunnel during the water hazards, that is, the ultimate water pressure of the tunnel. Based on ultimate water pressure and water hazard investigation, the authors put forward treatment measures for tunnel water hazards, and evaluated whether the treated tunnel can withstand the attack of high-water pressure in future. The research provides theoretical and data references for treating water hazards in tunnels operating in water rich areas.

Large-scale and dynamic surface water mapping is crucial for understanding the impact of global climate change and human activities on the distribution of surface water resources. Remote sensing imagery has become the primary data source for surface water mapping due to its high spatiotemporal resolution and wide coverage. However, the reliability of current water products during flood seasons is limited due to the influence of clouds on optical remote sensing images. Moreover, annual and seasonal surface water mapping cannot capture intra-month variations of water bodies. To address these challenges, we proposed a high spatiotemporal surface water mapping framework on Google Earth Engine that combines multi-source remote sensing data. Our framework can generate 10 m spatial resolution surface water maps at a 15-day time step. We classified water bodies using Sentinel-2 images and a classification tree algorithm, and then used Sentinel-1 data to compensate for cloudy and missing data areas in Sentinel-2 images, resulting in seamless cloud-unaffected surface water maps. We evaluated the effectiveness of our proposed framework in six floodplains around the world, and experimental results demonstrate that the water maps generated by our framework outperform existing public datasets and our framework has great potential for hydrological applications. Our proposed framework can capture the details of surface water dynamics with higher spatial and temporal resolution and is free from cloud influence, which is necessary for water resources management, flood monitoring, and disaster response.

Exploring carbon dioxide (CO2) emissions from human activities is essential for urban energy conservation and resource management. Remotely sensed nighttime lights from the Suomi NPP-VIIRS provide spatial consistency in and a low-cost way of revealing CO2 emissions. Although many researches have documented the feasibility of the Suomi NPP-VIIRS data for assessing CO2 emissions, few have systematically revealed the ability of nighttime lights for evaluating CO2 emissions from different industries, such as service industry CO2 emissions (SC), traffic CO2 emissions (TC), and secondary industry CO2 emissions (IC). Here, China was selected as the experimental subject, and we comprehensively explored the relationships between the nighttime lights and SC, TC, and IC, and investigated the factors mediating these relationships. We found that without considering other factors, the nighttime lights only revealed up to 51.2% of TC, followed by 41.7% of IC and 22.7% of SC. When controlling for city characteristic variables, the models showed that there were positive correlations between the Suomi NPP-VIIRS data and SC, IC, and TC, and that nighttime lights have an Inverted-U relationship with SC. The Suomi NPP-VIIRS data are more suitable for revealing SC, TC, and IC in medium-sized and large-sized cities than in small-sized cities and megacities.

IntroductionAbnormal lipoprotein metabolism is associated with a variety of diseases, cardiovascular disease in particular. Free fatty acids (FAs) and triglycerides (TGs) are the principal lipid species in adipocytes and are the major components of lipoproteins. However, in routine clinical laboratory testing, only the total plasma concentrations of FAs and TGs are typically measured.MethodsWe collected 965 individuals with hyperlipidemia plasma and clinical characteristics; high-throughput metabolomics permits the accurate qualitative and quantitative assessment of a variety of specific FAs and TGs and their association with lipoproteins; through regression analysis, the correlation between multiple metabolites and routine measured lipid parameters was found. Mice were fed a diet containing AA, and the concentrations of TC and TG in the plasma of mice were detected by enzyme method, western blot and qRT-PCR detected the protein and mRNA levels of cholesterol synthesis and metabolism in mice.ResultUsing LC-MS/MS identified eight free FA and 27 TG species in plasma samples, the plasma concentrations of free arachidonic acid (AA) and AA-enriched TG species were significantly associated with the plasma low-density lipoprotein-cholesterol, apolipoprotein B (ApoB), and total cholesterol (TC) concentrations after adjustment for age, sex, the use of lipid-lowering therapy, and body mass index. AA-rich diet significantly increased the plasma concentrations of TC and ApoB and the liver expression of ApoB protein and reduced the protein expression of ATP binding cassette subfamily G members 5 and 8 in mice.DiscussionIn this study, it was clarified that the plasma concentrations of free AA- and AA-enriched TG species were significantly associated with the plasma low-density lipoprotein-cholesterol, ApoB, and TC concentrations in individuals with hyperlipidemia, and it was verified that AA could increase the plasma TC level in mice. Taken together, these findings suggest a potential role of AA in the regulation of plasma cholesterol and lipoprotein concentrations.

Summary: Homozygous familial hypercholesterolemia (HoFH) is an extremely rare metabolism disorder usually caused by low-density lipoprotein receptor (LDLR) mutations. LDLR genotype is commonly known to determine blood concentrations of LDL cholesterol. However, effects of LDLR genotype on holistic metabolome remain unclear. Herein, we present metabolomic, genetic, and clinical datasets from a large multi-center panel of 142 patients with LDLR-mutated HoFH. We found that true homozygotes and compound heterozygotes showed few differences in clinical and metabolomic phenotypes. Compared with defective/defective mutation carriers, patients carrying one or two null mutation showed profound alterations in clinical laboratory lipids and serum cholesterol esters, lysophosphocholines, bile acids, and amino acids. Importantly, these altered metabolites are implicated in multiple biochemical reactions and associated with LDL cholesterol. This study extends the first map of different LDLR genotypes influencing the metabolome and suggests that the small-molecule metabolites serve as potential targets to mitigate the deleterious impact of LDLR mutations on HoFH.

Background The aim of this prospective study was to determine the impact of elevated ANGPTL3 (angiopoietin‐like protein 3) on cardiovascular events (CVEs) following acute coronary syndrome (ACS) in patients with or without obstructive sleep apnea (OSA). Methods and Results A total of 1174 patients with ACS underwent successful percutaneous coronary intervention were included in this prospective cohort study (NCT03362385). Patients were categorized according to the apnea–hypopnea index (≥15 events/h, OSA) and further classified by ANGPTL3 levels. We analyzed the incidence of CVEs in patients with ACS according to the status of OSA and ANGPTL3. During a median of 3.1 years of follow‐up, 217 (18.48%) CVEs occurred. The patients with ACS with OSA had higher ANGPTL3 levels than those without OSA (30.4 [20.9–43.2] versus 27.80 [19.1–41.5] ng/mL; P

The method to determine the active earth pressure and critical width for finite soil behind the retaining wall in mountainous areas is one of the concerns of geotechnical engineering. In order to study the active earth pressure distribution of the finite soil against the retaining wall and determine the critical width of the boundary between the finite soil and the semi-infinite soil, this study focuses on investigating a retaining wall with finite cohesionless backfill. The shape of the failure surface is assumed to be a cycloid passing through the heel of the wall in the limit equilibrium state. Considering the deflection of soil principal stress induced by wall–soil friction effect, a calculation method of active earth pressure for finite soil is proposed by using an arc-shaped small principal stress trajectory, and the rationality of this method is verified. On this basis, a calculation formula of the critical width for finite soil is proposed. The influence of the internal friction angle and the wall–soil friction angle on the critical width of finite soil is examined. The results indicate that the active earth pressure of finite soil presents a nonlinear drum distribution along the height of the retaining wall under the failure mode of the cycloidal surface. The maximum value of active earth pressure is close to the bottom of the wall. The critical width of finite soil decreases with the increase of the internal friction angle, and its variation rate decreases gradually. The critical width of finite soil increases with the increase of the wall–soil friction angle, and its variation rate also increases gradually. Under different internal friction angles and wall–soil friction angles, the critical width values of finite soil calculated by the assumption of the cycloidal failure surface are smaller than those calculated by the Coulomb earth pressure calculation method.

Solar generation has become increasingly important in grid applications. In order to improve the energy efficiency of the photovoltaic array (PV), factors such as temperature, nonlinear characteristics, and partial shadow conditions (PSCs) of the PV must be fully considered. An excellent maximum power point tracking (MPPT) control strategy can effectively improve the energy utilization efficiency of photovoltaic cells and provide strong support for the construction of smart campuses in terms of environmental protection and energy saving. A traditional method such as Perturb & Observe (P&O) and incremental conductance (INC) will fall into the local maximum power point (LMPP). In the past decade, researchers have proposed many MPPT methods to solve the difficulties of the PV system. However, they have failed to fully consider dynamic changes in irradiance conditions. Changes in the irradiance of photovoltaic arrays can lead to an extension of the convergence time and an increase in the oscillation amplitude. Many current MPPT methods have shortcomings such as requiring a long convergence time, large oscillation amplitude, and being prone to falling into LMPP. In order to reduce the oscillation amplitude and improve the convergence speed, a novel Multi-strategy Improved Tuna Swarm Optimization hybrid INC (ITSO-INC) method is introduced in this article. This strategy involves improving the Tuna Swarm Optimization (TSO) through Levy Flight and a linear weight coefficient. In addition, the INC method is added in the later stage to improve the accuracy of MPPT tracking. The proposed algorithm can extract the global maximum power point under different partial shading. In order to verify the effectiveness of the proposed method, the proposed method was compared with other metaheuristic algorithms such as Cuckoo Search (CS) and TSO. The proposed ITSO-INC technique was tested over four different patterns of partial shading conditions. Modulation was performed by tracking the sudden change in the shadow pattern of the MPP. These simulation results confirm that the proposed method has fast convergence, high accuracy, zero steady state oscillation, and a rapid response to dynamic change.

When using HBase to store tiles of remote sensing images, the spatial position of a tile is often used as the first part of the tile's rowkey so that tiles with high spatial correlations are stored close together to improve query efficiency. We refer to this storage method as the Geo-First model. However, Geo-First models have two problems: the load between nodes is unbalanced, and the accumulation of time-series remote sensing images has a negative impact on storage and query efficiency. Considering these two problems, we proposed a method for storing remote sensing images based on Google S2 and HBase. In our method, two strategies are adopted to eliminate these problems: the balanced placement strategy (BPS) and the periodic storage strategy (PSS). We evaluated our method by focusing on the effectiveness of BPS and PSS. The results show that our method achieves higher tile storage and query efficiency than three Geo-First models based on latitude and longitude, Geohash code, and Google S2 code. BPS effectively balances the load between nodes, while PSS alleviates the negative impact of the accumulation of time-series remote sensing images. Both BPS and PSS greatly improve tile storage and query efficiency.

Homozygous familial hypercholesterolemia (HoFH) is a rare inborn-errors-of-metabolism disorder characterized by devastatingly elevated low-density lipoprotein cholesterol (LDL-C) and premature cardiovascular disease. The gold standard for screening and diagnosing HoFH is genetic testing. In China, it is expensive and is always recommended for the most likely HoFH subjects with aggressive LDL-C phenotype. However, the LDL-C levels of HoFH patients and a substantial proportion of heterozygous FH (HeFH) patients overlapped considerably. Here, we performed a cost-effective metabolomic profiling on genetically diagnosed HoFH (n = 69) and HeFH patients (n = 101) with overlapping LDL-C levels, aiming to discovery a unique metabolic pattern for screening homozygotes in patients with severe FH. We demonstrated a differential serum metabolome profile in HoFH patients compared to HeFH patients. Twenty-one metabolomic alterations showed independent capability in differentiating HoFH from severe HeFH. The combined model based on seven identified metabolites yielded a corrected diagnosis in 91.3% of HoFH cases with an area under the curve value of 0.939. Collectively, this study demonstrated that metabolomic profiling serves as a useful and economical approach to preselecting homozygotes in FH patients with severe hypercholesterolemia and may help clinicians to conduct selective genetic confirmation testing and familial cascade screening.

Estimation of crop biophysical and biochemical characteristics is the key element for crop growth monitoring with remote sensing. With the application of unmanned aerial vehicles (UAV) as a remote sensing platform worldwide, it has become important to develop general estimation models, which can interpret remote sensing data of crops by different sensors and in different agroclimatic regions into comprehensible agronomy parameters. Leaf chlorophyll content (LCC), which can be measured as a soil plant analysis development (SPAD) value using a SPAD-502 Chlorophyll Meter, is one of the important parameters that are closely related to plant production. This study compared the estimation of rice (Oryza sativa L.) LCC in two different regions (Ningxia and Shanghai) using UAV-based spectral images. For Ningxia, images of rice plots with different nitrogen and biochar application rates were acquired by a 125-band hyperspectral camera from 2016 to 2017, and a total of 180 samples of rice LCC were recorded. For Shanghai, images of rice plots with different nitrogen application rates, straw returning, and crop rotation systems were acquired by a 5-band multispectral camera from 2017 to 2018, and a total of 228 samples of rice LCC were recorded. The spectral features of LCC in each study area were analyzed and the results showed that the rice LCC in both regions had significant correlations with the reflectance at the green, red, and red-edge bands and 8 vegetation indices such as the normalized difference vegetation index (NDVI). The estimation models of LCC were built using the partial least squares regression (PLSR), support vector regression (SVR), and artificial neural network (ANN) methods. The PLSR models tended to be more stable and accurate than the SVR and ANN models when applied in different regions with R2 values higher than 0.7 through different validations. The results demonstrated that the rice canopy LCC in different regions, cultivars, and different types of sensor-based data shared similar spectral features and could be estimated by general models. The general models can be implied to a wider geographic extent to accurately quantify rice LCC, which is helpful for growth assessment and production forecasts.

Background: Endothelial barrier dysfunction plays a key role in atherosclerosis progression. The primary pathology of obstructive sleep apnea-hypopnea syndrome is chronic intermittent hypoxia (IH), which induces reactive oxygen species (ROS) overproduction, endothelial barrier injury, and atherosclerosis. Salidroside, a typical pharmacological constituent of Rhodiola genus, has documented antioxidative, and cardiovascular protective effects. However, whether salidroside can improve IH-aggravated endothelial barrier dysfunction and atherosclerosis has not been elucidated.Methods and results: In normal chow diet-fed ApoE−/− mice, salidroside (100 mg/kg/d, p. o.) significantly ameliorated the formation of atherosclerotic lesions and barrier injury aggravated by 7-weeks IH (21%–5%–21%, 120 s/cycle). In human umbilical vein endothelial cells (HUVECs), exposure to IH (21%–5%–21%, 40 min/cycle, 72 cycles) decreased transendothelial electrical resistance and protein expression of vascular endothelial cadherin (VE-cadherin) and zonula occludens 1. In addition, IH promoted ROS production and activated ras homolog gene family member A (RhoA)/Rho-associated protein kinase (ROCK) pathway. All of these effects of IH were reversed by salidroside. Similar to salidroside, ROCK-selective inhibitors Y26732, and Fasudil protected HUVECs from IH-induced ROS overproduction and endothelial barrier disruption. Furthermore, salidroside increased intracellular cAMP levels, while the PKA-selective inhibitor H-89 attenuated the effects of salidroside on IH-induced RhoA/ROCK suppression, ROS scavenging, and barrier protection.Conclusion: Our findings demonstrate that salidroside effectively ameliorated IH-aggravated endothelial barrier injury and atherosclerosis, largely through the cAMP/PKA/RhoA signaling pathway.

The identification of cucumber fruit is an essential procedure in automated harvesting in greenhouses. In order to enhance the identification ability of object detection models for cucumber fruit harvesting, an extended RGB image dataset (n = 801) with 3943 positive and negative labels was constructed. Firstly, twelve channels in four color spaces (RGB, YCbCr, HIS, La*b*) were compared through the ReliefF method to choose the channel with the highest weight. Secondly, the RGB image dataset was converted to the pseudo-color dataset of the chosen channel (Cr channel) to pre-train the YOLOv5s model before formal training using the RGB image dataset. Based on this method, the YOLOv5s model was enhanced by the Cr channel. The experimental results show that the cucumber fruit recognition precision of the enhanced YOLOv5s model was increased from 83.7% to 85.19%. Compared with the original YOLOv5s model, the average values of AP, F1, recall rate, and mAP were increased by 8.03%, 7%, 8.7%, and 8%, respectively. In order to verify the applicability of the pre-training method, ablation experiments were conducted on SSD, Faster R-CNN, and four YOLOv5 versions (s, l, m, x), resulting in the accuracy increasing by 1.51%, 3.09%, 1.49%, 0.63%, 3.15%, and 2.43%, respectively. The results of this study indicate that the Cr channel pre-training method is promising in enhancing cucumber fruit detection in a near-color background.

Obstructive sleep apnea (OSA) can aggravate blood pressure and increase the risk of cardiovascular diseases in hypertensive individuals, yet the underlying pathophysiological process is still incompletely understood. More importantly, OSA remains a significantly undiagnosed condition. In this study, a total of 559 hypertensive patients with and without OSA were included. Metabolome and lipidome-wide analyses were performed to explore the pathophysiological processes of hypertension comorbid OSA and derive potential biomarkers for diagnosing OSA in hypertensive subjects. Compared to non-OSA hypertensive patients (discovery set = 120; validation set = 116), patients with OSA (discovery set = 165; validation set = 158) demonstrated a unique sera metabolic phenotype dominated by abnormalities in biological processes of oxidative stress and inflammation. By integrating three machine learning algorithms, six discriminatory metabolites (including 5-hydroxyeicosatetraenoic acid, taurine, histidine, lysophosphatidic acid 16:0, lysophosphatidylcholine 18:0, and dihydrosphingosine) were selected for constructing diagnostic and classified model. Notably, the established multivariate-model could accurately identify OSA subjects. The corresponding area under the curve values and the correct classification rates were 0.995 and 96.8% for discovery sets, 0.997 and 99.1% for validation sets. This work updates the molecular insights of hypertension comorbid OSA and paves the way for the use of metabolomics for the diagnosis of OSA in hypertensive individuals.

Herein, the effect of the shear flow on the growth of columnar crystals in an undercooled melt is studied. The asymptotic method is used to solve the dynamic model for the growth of a columnar crystal. The resulting asymptotic solution shows that the shear flow significantly changes the interface morphology of the columnar crystal. With the shear effect of the forced flow, the growth rate of the columnar interface increases in the shear direction of the shear flow. As the shear rate of the shear flow further increases, the interface of the columnar crystal is seriously deformed and distorted. The shear flow causes the columnar crystal in the undercooled melt to tend to evolve into smaller crystals in the initial stage of crystal growth. The analytical result provides a prediction of the formation of interface microstructures during solidification through the change of processing parameters.

There is no consensus on the effect of China’s rapid urban expansion on the fine particulate matter (PM2.5) emissions from fossil fuel combustion (PC). Previous studies usually focused on the environmental effect of urban expansion from a single mode (e.g., spatial expansion). However, studies that simultaneously considered and compared the effect from spatial and socioeconomic modes are still lacking. Thus, we combined multiple data sources (e.g., nighttime light data, urban land datasets, and PC) and econometric methods to evaluate the effect of urban expansion on PC within different regions from spatial and socioeconomic modes. The results show that China’s urban socioeconomic expansion (UE) and urban spatial expansion (US) increased from 68.50% and 11.81 × 10−4, respectively, in 1992 to 72.23% and 66.86 × 10−4, respectively in 2012. The UE is the Granger cause of the increased PC in China. Through variance decomposition analysis, we also found that the UE contributed much more to the PC in China than the US. When comparing the different regions, we recognized that the UE was the key factor in explaining the increase in the PC in the eastern and northeastern regions, and the US could effectively explain the changes in the PC in the central region. The study provides a novel perspective for quantifying the effect of urban intensive and extensive development on haze pollution.

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and a promising indicator of drought monitoring, but the ability of high-resolution satellite-derived SIF for drought monitoring has not been widely investigated due to a lack of data. The lack of high spatiotemporal resolution satellite SIF hinders the resolution enhancement of SIF derived by downscaling or reconstruction algorithms. The TROPOspheric Monitoring Instrument (TROPOMI) SIF provides an alternative with finer spatiotemporal resolution. We present an operational downscaling method to generate 500 m 16-day SIF (TSIF) using Neural Networks over a local spatiotemporal window. The results showed that our method is very robust against overfitting, and TSIF has a strong spatiotemporal consistency with TROPOMI SIF (TROPOSIF) with R2=0.956 and RMSE=0.054 mWm−2sr−1nm−1. Comparison with another SIF product (CASIF) showed a spatiotemporal consistency with TSIF. Comparison with tower gross primary productivity (GPP) from AmeriFlux in California showed a strong correlation with R2 for multiple ecosystems ranging from 0.58 to 0.88. We explored the capacity of TSIF for monitoring a drought event in Henan, China, showing that TSIF is more sensitive to drought and precipitation compared to the Enhanced Vegetation Index. Our TSIF is a very promising indicator for regional drought monitoring.

Plant photosynthesis and biomass production are associated with the amount of intercepted light, especially the light distribution inside the canopy. Three virtual canopies (n = 80, 3.25 plants/m2) were constructed based on average leaf size of the digitized plant structures: ‘small leaf’ (98.1 cm2), ‘medium leaf’ (163.0 cm2) and ‘big leaf’ (241.6 cm2). The ratios of diffuse light were set in three gradients (27.8%, 48.7%, 89.6%). The simulations of light interception were conducted under different ratios of diffuse light, before and after the normalization of incident radiation. With 226.1% more diffuse light, the result of light interception could increase by 34.4%. However, the 56.8% of reduced radiation caused by the increased proportion of diffuse light inhibited the advantage of diffuse light in terms of a 26.8% reduction in light interception. The big-leaf canopy had more mutual shading effects, but its larger leaf area intercepted 56.2% more light than the small-leaf canopy under the same light conditions. The small-leaf canopy showed higher efficiency in light penetration and higher light interception per unit of leaf area. The study implied the 3D structural model, an effective tool for quantitative analysis of the interaction between light and plant canopy structure.

ObjectiveMelatonin is the major hormone produced and secreted at night by the pineal gland into the cerebrospinal fluid (CSF) and circulation. The relationship between plasma melatonin levels and Parkinson’s disease is not clear. The aim of the current study was to assess plasma melatonin levels in Parkinson’s disease (PD) patients and to analysis the relationship between plasma melatonin levels and non-motor symptoms.Participants and MethodsIn this cross-sectional study, we evaluated 61 patients with idiopathic PD [males n = 30 (49.2%), average age 62.4 years (range: 46–73 years)] and a total of 58 healthy volunteers [males n = 30 (51.7%), average age 64.3 years (range: 45–70 years)] who participated in the study. Plasma melatonin levels were measured using an enzyme-linked immunosorbent assay. The severity of disease in PD patients was scored by the Unified Parkinson’s Disease Rating Scale and the Hoehn and Yahr Staging scale. The quality of life in PD patients was assessed by the 39-item Parkinson’s Disease Questionnaire. The non-motor symptoms were assessed by the 14-item Hamilton Anxiety Rating Scale, the 24-item Hamilton Depression Rating Scale, the Parkinson Disease Sleep Scale, the Epworth Sleepiness Scale and the Non-Motor Symptoms Scale for PD.ResultsCompared with the healthy controls, the plasma melatonin levels were significantly higher in PD patients (12.82 ± 4.85 vs. 19.40 ± 4.23, P < 0.001). Plasma melatonin levels were significantly associated with the levodopa equivalent daily dose (r = −0.262, P < 0.05, n = 61). Higher plasma melatonin concentrations were detected in the negative cardiovascular symptom group than in the cardiovascular symptom group (20.13 ± 3.74 vs. 16.93 ± 3.74, P < 0.05). Higher plasma melatonin concentrations were detected in the non-sleep-disorders group than in the sleep disorders group (22.12 ± 5.93 vs. 18.86 ± 3.66, P < 0.05). In addition, the plasma melatonin concentration was higher in the group without gastrointestinal dysfunction than in the gastrointestinal dysfunction group (21.71 ± 4.44 vs. 18.35 ± 3.74, P < 0.05).ConclusionThis study revealed that the plasma melatonin levels in PD patients were significantly higher than those in healthy controls. Non-motor symptoms that were significantly negatively correlated with plasma melatonin levels were cardiovascular symptoms, sleep disorders, and gastrointestinal dysfunction. Plasma melatonin levels have the closest relationship with sleep disorders. There was a correlation between plasma melatonin levels and sleep quality in patients with PD. The remaining non-motor symptoms were not related to plasma melatonin levels.

Deep learning technology has achieved great success in the field of remote sensing processing. However, the lack of tools for making deep learning samples with remote sensing images is a problem, so researchers have to rely on a small amount of existing public data sets that may influence the learning effect. Therefore, we developed an add-in (LabelRS) based on ArcGIS to help researchers make their own deep learning samples in a simple way. In this work, we proposed a feature merging strategy that enables LabelRS to automatically adapt to both sparsely distributed and densely distributed scenarios. LabelRS solves the problem of size diversity of the targets in remote sensing images through sliding windows. We have designed and built in multiple band stretching, image resampling, and gray level transformation algorithms for LabelRS to deal with the high spectral remote sensing images. In addition, the attached geographic information helps to achieve seamless conversion between natural samples, and geographic samples. To evaluate the reliability of LabelRS, we used its three sub-tools to make semantic segmentation, object detection and image classification samples, respectively. The experimental results show that LabelRS can produce deep learning samples with remote sensing images automatically and efficiently.

Urban flooding is one of the most costly and destructive natural hazards worldwide. Remote-sensing images with high temporal resolutions have been extensively applied to timely inundation monitoring, assessing and mapping, but are limited by their low spatial resolution. Sub-pixel mapping has drawn great attention among researchers worldwide and has demonstrated a promising potential of high-accuracy mapping of inundation. Aimed to boost sub-pixel urban inundation mapping (SUIM) from remote-sensing imagery, a new algorithm based on spatial attraction models and Elman neural networks (SAMENN) was developed and examined in this paper. The Elman neural networks (ENN)-based SUIM module was developed firstly. Then a normalized edge intensity index of mixed pixels was generated. Finally the algorithm of SAMENN-SUIM was constructed and implemented. Landsat 8 images of two cities of China, which experienced heavy floods, were used in the experiments. Compared to three traditional SUIM methods, SAMENN-SUIM attained higher mapping accuracy according not only to visual evaluations but also quantitative assessments. The effects of normalized edge intensity index threshold and neuron number of the hidden layer on accuracy of the SAMENN-SUIM algorithm were analyzed and discussed. The newly developed algorithm in this study made a positive contribution to advancing urban inundation mapping from remote-sensing images with medium-low spatial resolutions, and hence can favor urban flood monitoring and risk assessment.

Diabetes is caused by the lack of release or action of insulin. Some foods and supplements can compensate for this deficiency; thus, they can aid in the prevention or treatment of diabetes. The aim of this study was to investigate the effects of Cyclocarya paliurus extract (CPE) on insulin signaling and its capacity to correct hyperglycemia in the absence of insulin. To investigate the hypoglycemic effects of CPE, C2C12 cells were exposed to CPE (50 and 100 μg/mL). CPE promoted 2-(N-(7-Nitrobenz-2-oxa-1,3-diazol-4-yl)Amino)-2-Deoxyglucose (2NBDG) uptake into the cells via translocation of glucose transporter 4 (Glut4) to the plasma membrane. In addition, CPE enhanced tyrosine phosphorylation of insulin receptor substrate and activated phosphatidylinositol 3-kinase and protein kinase B (Akt) via sirtuin1 in C2C12 cells. Moreover, we found that oral administration of CPE (1 g/kg) to streptozotocin-induced hyperglycemic mice produced a progressive decrease in plasma glucose levels at 1 h after single dosing. At that point, CPE significantly increased the expression of skeletal muscle membrane Glut4 and enhanced the phosphorylation of Akt. These results suggest that CPE exerts antidiabetic effects similar to those of insulin, and may be an oral therapeutic alternative for the management of diabetes.

Periodic inundation of floodplains and wetlands is critical for the well being of ecosystems. This study proposes a simple but efficient model that integrates time series daily flow data and the Landsat-derived Water Observation from Space (WOfS) product to model the spatio-temporal flood inundation dynamics of the Murray-Darling Basin. A zone-gauge framework is adopted in order to reduce the hydrologic complexity of the large river basin. Under this framework, flood frequency analysis was conducted at each gauge station to identify historical peak flows and their annual exceedance probabilities. The results were then linked with the WOfS dataset through date to model the inundation probability in each zone. Inundation frequency was derived by simply overlaying the yearly inundation extent from 1988 to 2015 and counting the inundation times. Both the resultant inundation frequency map and inundation probability map are of ecological significance for the survival and prosperity of riparian ecosystems. The assumptions of the model were validated carefully to enhance its theoretical basis. The WOfS dataset was also compared with another independent water observation dataset to cross-validate its reliability. It is hoped that with the development of more and more global high-resolution surface water datasets, this study could inspire more studies that integrate surface water datasets with hydrological observations for flood inundation modeling.

Wetland flooding is significant for the flora and fauna of wetlands. High temporal resolution remote sensing images are widely used for the timely mapping of wetland flooding but have a limitation of their relatively low spatial resolutions. In this study, a novel method based on random forests and spatial attraction models (RFSAM) was proposed to improve the accuracy of sub-pixel mapping of wetland flooding (SMWF) using remote sensing images. A random forests-based SMWF algorithm (RM-SMWF) was developed firstly, and a comprehensive complexity index of a mixed pixel was formulated. Then the RFSAM-SMWF method was developed. Landsat 8 Operational Land Imager (OLI) images of two wetlands of international importance included in the Ramsar List were used to evaluate RFSAM-SMWF against three other SMWF methods, and it consistently achieved more accurate sub-pixel mapping results in terms of visual and quantitative assessments in the two wetlands. The effects of the number of trees in random forests and the complexity threshold on the mapping accuracy of RFSAM-SMWF were also discussed. The results of this study improve the mapping accuracy of wetland flooding from medium-low spatial resolution remote sensing images and therefore benefit the environmental studies of wetlands.

The sodium/glucose cotransporter 2 (SGLT2) is responsible for the majority of glucose reabsorption in the kidney, and currently, SGLT2 inhibitors are considered as promising hypoglycemic agents for the treatment of type 2 diabetes mellitus. By constructing CHO cell lines that stably express the human SGLT2 transmembrane protein, along with a fluorescent glucose transporter assay that uses 2-[N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl)amino]2-deoxyglucose (2-NBDG) as a glucose analog, we have developed a nonradioactive, cell-based assay for the discovery and characterization of SGLT2 inhibitors.

The vegetation supply water index (VSWI = NDVI/LST) is an effective metric estimating soil moisture in areas with moderate to dense vegetation cover. However, the normalized difference vegetation index (NDVI) exhibits a long water stress lag and the land surface temperature (LST), sensitive to water stress, does not contribute considerably to surface soil moisture monitoring due to the constraints of the mathematical characteristics of VSWI: LST influences VSWI less when LST value is sufficiently high. This paper mathematically analyzes the characteristics of VSWI and proposes a new operational dryness index (surface water content temperature index, SWCTI) for the assessment of surface soil moisture status. SWCTI uses the surface water content index (SWCI), which provides a more accurate estimation of surface soil moisture than that of NDVI, as the numerator and the modified surface temperature, which has a greater influence on SWCTI than that of LST, as the denominator. The validation work includes comparison of SWCTI with in situ soil moisture and other remote sensing indices. The results show SWCTI demonstrates the highest correlation with in situ soil moisture; the highest correlation R = 0.801 is found between SWCTI and the 0–5 cm soil moisture in a sandy loam. SWCTI is a functional and effective method that has a great potential in surface soil moisture monitoring.

Increased energy intake and reduced physical activity can lead to obesity, diabetes and metabolic syndrome. Transcriptional modulation of metabolic networks has become a focus of current drug discovery research into the prevention and treatment of metabolic disorders associated with energy surplus and obesity. Tang-Nai-Kang (TNK), a mixture of five herbal plant extracts, has been shown to improve abnormal glucose metabolism in patients with pre-diabetes. Here, we report the metabolic phenotype of SHR.Cg-Leprcp/NDmcr (SHR/cp) rats treated with TNK. Pre-diabetic SHR/cp rats were randomly divided into control, TNK low-dose (1.67 g/kg) and TNK high-dose (3.24 g/kg) groups. After high-dose treatment for 2 weeks, the serum triglycerides and free fatty acids in SHR/cp rats were markedly reduced compared to controls. After 3 weeks of administration, the high dose of TNK significantly reduced the body weight and fat mass of SHR/cp rats without affecting food consumption. Serum fasting glucose and insulin levels in the TNK-treated groups decreased after 6 weeks of treatment. Furthermore, TNK-treated rats exhibited obvious improvements in glucose intolerance and insulin resistance. The improved glucose metabolism may be caused by the substantial reduction in serum lipids and body weight observed in SHR/cp rats starting at 3 weeks of TNK treatment. The mRNA expression of NAD+-dependent deacetylase sirtuin 1 (SIRT1) and genes related to fatty acid oxidation was markedly up-regulated in the muscle, liver and adipose tissue after TNK treatment. Furthermore, TNK promoted the deacetylation of two well-established SIRT1 targets, PPARγ coactivator 1α (PGC1α) and forkhead transcription factor 1 (FOXO1), and induced the phosphorylation of AMP-activated protein kinase (AMPK) and acetyl-CoA carboxylase (ACC) in different tissues. These observations suggested that TNK may be an alternative treatment for pre-diabetes and metabolic syndrome by inducing a gene expression switch toward fat oxidation through the activation of SIRT1 and AMPK signaling.

Capturing the dynamics of a lake-water area using remotely sensed images has always been an essential task. Most of the fine spatial resolution data are unsuitable for this purpose because of their low temporal resolution and limited scene coverage. A Visible Infrared Imaging Radiometer Suite on board the Suomi National Polar-orbiting Partnership (Suomi NPP–VIIRS) is a newly-available and appropriate sensor for monitoring large lakes due to its frequent revisits and wide swath (more than 3000 km). However, it provides visible and infrared images at relatively coarse spatial resolutions, which would sometimes hamper the accurate mapping of lake shorelines. This study, therefore, proposes a two-step downscaling method that combines spectral unmixing and subpixel mapping to produce a finer resolution lake map from NPP–VIIRS imagery, which is then applied to delineate the shorelines of five plateau lakes in Yunnan Province, as well as the shoreline dynamics of Poyang Lake at three separate times. A newly published global water dynamic dataset is employed in this study to improve the downscaling method. Results suggest that the proposed method can generate a finer resolution lake map that exhibits more details of the shoreline than hard classification. The downscaling results of the Suomi NPP–VIIRS generally achieve higher than 75% accuracy, while the downscaling results of a Landsat-simulated fraction map could have accuracy higher than 85%. This reveals that errors and uncertainties exist in both procedures, but mainly come from the spectral unmixing procedure which retrieves water fractions from NPP–VIIRS data.

Monitoring the dynamics of surface water using remotely sensed data generally requires both high spatial and high temporal resolutions. One effective and popular approach for achieving this is image fusion. This study adopts a widely accepted fusion model, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), for blending the newly available coarse-resolution Suomi NPP-VIIRS data with Landsat data in order to derive water maps at 30 m resolution. The Pan-sharpening technique was applied to preprocessing NPP-VIIRS data to achieve a higher-resolution before blending. The modified Normalized Difference Water Index (mNDWI) was employed for mapping surface water area. Two fusion alternatives, blend-then-index (BI) or index-then-blend (IB), were comparatively analyzed against a Landsat derived water map. A case study of mapping Poyang Lake in China, where water distribution pattern is complex and the water body changes frequently and drastically, was conducted. It has been revealed that the IB method derives more accurate results with less computation time than the BI method. The BI method generally underestimates water distribution, especially when the water area expands radically. The study has demonstrated the feasibility of blending NPP-VIIRS with Landsat for achieving surface water mapping at both high spatial and high temporal resolutions. It suggests that IB is superior to BI for water mapping in terms of efficiency and accuracy. The finding of this study also has important reference values for other blending works, such as image blending for vegetation cover monitoring.