Search

Abstract The Qinghai-Tibet Plateau (QTP) holds significance for investigating Earth’s surface processes. However, due to rugged terrain, forest canopy, and snow accumulation, open-access Digital Elevation Models (DEMs) exhibit considerable noise, resulting in low accuracy and pronounced data inconsistency. Furthermore, the glacier regions within the QTP undergo substantial changes, necessitating updates. This study employs a fusion of open-access DEMs and high-accuracy photons from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2). Additionally, snow cover and canopy heights are considered, and an ensemble learning fusion model is presented to harness the complementary information in the multi-sensor elevation observations. This innovative approach results in the creation of HQTP30, the most accurate representation of the 2021 QTP terrain. Comparative analysis with high-resolution imagery, UAV-derived DEMs, control points, and ICESat-2 highlights the advantages of HQTP30. Notably, in non-glacier regions, HQTP30 achieved a Mean Absolute Error (MAE) of 0.71 m, while in glacier regions, it reduced the MAE by 4.35 m compared to the state-of-the-art Copernicus DEM (COPDEM), demonstrating its versatile applicability.

Abstract Large‐scale photovoltaic (PV) plants are growing rapidly in drylands because of the rich solar radiation and vast unutilized land. The transformation of landscapes in dryland has threatened local fragile vegetation. Existing studies have investigated the issue by field observations and satellite data, yet the spatial differences in vegetation changes due to dryland PV plants deployment and underlying driving mechanisms remain poorly understood. In this study, Landsat Normalized Difference Vegetation Index data were used to assess the vegetation changes disturbed by PV plants in China's drylands. We further identified environmental factors affecting vegetation changes based on random forest regression model. Our findings reveal the spatial heterogeneity in the impact of PV plants on vegetation dynamics—PV plant deployment promoted the growth of vegetation in the vast majority of arid and hyper‐arid drylands, while it tends to cause vegetation decline in the sub‐humid and semi‐arid drylands. The impact of PV plants on vegetation dynamics depends on local environmental conditions. We found that deploying PV plants in areas with sparse vegetation, low humidity, and long sunshine duration is more likely to promote vegetation restoration. The findings and data maps with highly detailed information can help guide solar energy operators in siting and ecological restoration to enhance techno–ecological synergies in the future.

Many leading countries are boosting renewables, especially solar energy, as a major way to mitigate future energy crises and climate change. Particularly, in China, the number and scale of photovoltaic (PV) power stations have grown unprecedentedly in the last decade. There is an urgent need to monitor the PV power development in order to accurately estimate national renewable potentials and understand the ecological impacts. However, there are few efforts towards providing spatially explicit and time-series datasets of PV development at the regional and national scales. To fill the gap, this study proposes an integrated remote sensing approach for PV power stations mapping by combining image segmentation and object-based classification (ISOC) techniques. We took five northwestern provinces of China as an illustration and produced 30-m medium-resolution PV power station distribution maps from 2007 to 2019. Our analysis shows that the total area of PV power stations in the five provinces increased to 722 km 2 in 2019, with producer, user and overall accuracies of 86%, 100% and 93%. Of the 309 PV station clusters (hereafter, PV parks), the top 7% largest ones account for 61% of the total area of PV power stations, indicating that PV power stations in the Northwest tend to be developed in the form of large-scale centralized PV parks. The land used for PV power stations was mainly converted from four land cover types: Gobi Desert, sandy land, sparse grassland, and moderate grassland. The central government policy on facilitating clean energy played a major role in driving the rapid expansion of PV parks across the country. The methodology and results of this study will help policymakers, researchers, and practitioners to develop corresponding industrial standards and environmental regulations to mitigate the potential environmental impacts, and promote more sustainable development of solar energy in China and beyond.

Globally, the loss of forest vegetation is a significant concern due to the crucial roles that forests play in the Earth’s system, including the provision of ecosystem services, participation in biogeochemical cycles, and support for human well-being. Forests are especially critical in mountains environments, where deforestation can lead to accelerated biodiversity loss, soil erosion, flooding, and reduced agricultural productivity, as well as increased poverty rates. In response to these problems, China has implemented a series of ecological restoration programs aimed at restoring forests. However, there is a lack of knowledge as to whether the forest cover is increasing or decreasing, as well as the relative roles played by natural and human factors in forest change. Here, we aim to address these issues by analyzing the pattern and process of the forest changes in Guizhou province, a typical mountainous karst area with a fragile environment in southwestern China, between 1980 and 2018, and evaluating the extent to which these forest changes were influenced by natural and anthropogenic driving forces. Using a temporal sequence of satellite images and a Markov model, we found that the forest cover increased by 468 km2, and that over 33% of the cropland in Guizhou province was converted into forest between 1980 and 2018, with the most significant increases in the forest cover occurring in Qiandongnan. Through correlation analyses and generalized linear model (GLM) regression, we demonstrate that management factors exerted a more significant positive impact on the forest cover than climate change. While the mean annual precipitation and temperature were mostly stable during the period studied, the effects of population and gross domestic product (GDP) on the forest changes weakened, and the influence of land-use change markedly increased. These findings provide valuable information for resource managers engaging in forest protection, deforestation prevention, and ecological restoration in similar regions.

Renewable energy is crucial to address climate change and achieve carbon neutrality. Within the existing technologies, photovoltaics is one of the most promising renewable energies. However, large-scale development of photovoltaics always needs a large amount of space, competing for land with other usages. Installing photovoltaics on water surfaces such as lakes and reservoirs is one of the major solutions to solve the contradiction between photovoltaic development and land shortage. In recent years, many countries have been actively promoting the development of water photovoltaic (WPV) to reduce carbon emission and meet increased energy demand, of which China is far ahead. Mapping the spatial distribution of WPV with satellite image time series will help reveal the spatial distribution, assess the scale and future potential of national WPV development, and understand its environmental impact. However, studies and reliable spatial data on the extent and distribution of WPV are still lacking. To address this gap, we proposed a classification algorithm based on Random Forest to extract WPV features and determine the types of the WPV from the Sentinel time series. The results show that the area of WPV in China reached 165 km2 in 2019, with an average annual growth of 43.8 km2 from 2016 to 2019. Among them, Stationary Photovoltaic (SPV) accounts for 95% of the total WPV, while Floating Photovoltaic (FPV) only accounts for 5%. Based on our WPV mapping results, we estimated the installed capacity and power generation of WPV would be approximately 6911 MW and 7.8 TWh yr−1, accounting for 3.4% and 3.5% of the total cumulative installed PV capacity and total PV power generation, respectively. This study provides an efficient and robust approach for extracting WPV at a large scale. The WPV data can be a valuable supplement to official tabulate data because it contains much finer spatial information that can assist stakeholders with future WPV planning and environmental impact assessment.

High-altitude environments are among the most sensitive to climate change, where impacts are amplified. To clarify alpine pasture dynamics in three typical mountains (i.e., Altay, Tianshan, and Kunlun) in central Asia, the change trends in the long-term summer peak Normalized Difference Vegetation Index (NDVI) were examined by applying three satellite sources: Landsat ETM+/OLI (1999–2020), Global Inventory Modeling and Mapping Studies (1981–2013), and Moderate Resolution Imaging Spectroradiometer (2000–2020). Satellite-derived NDVIs are reliable because similar variations can be observed during overlapping periods. The results show that the alpine pastures experienced overall greening. The most significant greening was observed in Kunlun Mountains and the high-altitude areas of Tianshan and Altay Mountains, where the annual average temperature is lower. Relationships between NDVI and potential driving factors, such as average precipitation (AP), surface soil water content (SSWC), total water storage (TWS), snow cover frequency, growing degree days (GDD), and average temperature (AT), were further analyzed. Over the last two decades, the optimal habitat of alpine pastures became warmer and wetter, as the AT, GDD, SSWC, TWS all showed increased trends. However, AP decreased at a rate of –0.625 mm/year, indicating that melting ice materials under current warming helped preserve moisture and soil water availability for pasture growth. Additionally, the primary factor regulating greenness patterns shifted from AP during 2002–2011 to GDD during 2012, 2014, and 2016–2020, indicating that the alpine pastures in central Asia have been increasingly dependent on climate warming. Our study highlights that the response of alpine pastures to climatic factors might change under ongoing climate change.

Research on the Himalayan cryosphere has increasingly focused on climate change, considering aspects such as snow cover dynamics, precipitation and temperature trends. The Uttarakhand Himalaya is hydrologically very significant and a more comprehensive understanding of its response to climate change is needed. This paper analyses of elevation-dependent distribution and trends in snow-covered area (SCA), precipitation and temperature for the period 2000–2020 employing MODIS-Terra data, together with reanalysis (CHIRPS) and Landsat-8 data products within the Google Earth Engine (GEE) platform. The study reveals a significant increasing trend in annual, seasonal and monthly precipitation (except November) at different elevations, while SCA and temperatures exhibited more variable trends during the period. Digital elevation and spectral reflectance models reveal the effects of topography and surface attributes on snow cover. Statistical analyses reveal a significant relationship between precipitation and SCA (R2 = 0.78), while seasonal changes in temperature are apparent in an emerging pattern of warmer winters and a cooler pre-monsoon. SCA trends vary markedly between elevation zones in response to precipitation and temperature fluctuations. At higher altitudes, SCA decreased from 2000 to 2020 since, although precipitation increased slightly, marginally higher temperatures led to more snowmelt. Reduced snow cover and increased temperature are also associated with a decline in downstream fluvial during the period for which such data are available (2000 to 2005). The analysis of snow cover dynamics across different elevations improves our understanding of the overall hydrological conditions in the region and enable more reliable flood forecasting and management of water resources.

This study explores the spatio-temporal distribution and trends on monthly, seasonal, and annual scales of rainfall in the central Punjab districts of Punjab province in Pakistan by using observation and satellite data products. The daily observed data was acquired from the Pakistan Metrological Department (PMD) between 1983 and 2020, along with one reanalysis, namely the Climate Hazard Infrared Group Precipitation Station (CHIRPS) and one satellite-based daily Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks climate data record (PERSIANN-CDR) using the Google Earth Engine (GEE) web-based API platform to investigate the spatio-temporal fluctuations and inter-annual variability of rainfall in the study domain. Several statistical indices were employed to check the data similarity between observed and remotely sensed data products and applied to each district. Moreover, non-parametric techniques, i.e., Mann–Kendall (MK) and Sen’s slope estimator were applied to measure the long-term spatio-temporal trends. Remotely sensed data products reveal 422.50 mm (CHIRPS) and 571.08 mm (PERSIANN-CDR) mean annual rainfall in central Punjab. Maximum mean rainfall was witnessed during the monsoon season (70.5%), followed by pre-monsoon (15.2%) and winter (10.2%). Monthly exploration divulges that maximum mean rainfall was noticed in July (26.5%), and the minimum was in November (0.84%). The district-wise rainfall estimation shows maximum rainfall in Sialkot (931.4 mm) and minimum in Pakpattan (289.2 mm). Phase-wise analysis of annual, seasonal, and monthly trends demonstrated a sharp decreasing trend in Phase-1, averaging 3.4 mm/decade and an increasing tendency in Phase-2, averaging 9.1 mm/decade. Maximum seasonal rainfall decreased in phase-1 and increased Phase-2 during monsoon season, averaging 2.1 and 4.7 mm/decade, whereas monthly investigation showed similar phase-wise tendencies in July (1.1 mm/decade) and August (2.3 mm/decade). In addition, as district-wise analyses of annual, seasonal, and monthly trends in the last four decades reveal, the maximum declined trend was in Sialkot (18.5 mm/decade), whereas other districts witnessed an overall increasing trend throughout the years. Out of them, Gujrat district experienced the maximum increasing trend in annual terns (50.81 mm/decade), and Faisalabad (25.45 mm/decade) witnessed this during the monsoon season. The uneven variability and trends have had a crucial imprint on the local environment, mainly in the primary activities.

The typhoon impact on an estuarine environment is complex and systematic. A three-dimensional hydrodynamic and salinity transport model with a high-resolution, unstructured mesh and a spatially varying bottom roughness, is applied to investigate the effects of a historical typhoon, Fongwong, which affected Shanghai, on the hydrodynamics and saline water intrusion in the North Branch (NB) of the Yangtze River Estuary (YRE). The model is well validated through observation data of the tidal level, current velocity and direction, and salinity. The numerical results of this typhoon event show that: (1) the tidal level and its range increase toward the upstream part of the NB due to the combined effects of the funnel-shaped plane geometry of the NB and the typhoon; (2) the current velocity and the flow spilt ratio of the NB varies with the tides, with a maximum increase by 0.13 m/s and 26.61% during the flood tide and a maximum decrease by 0.12 m/s and 83.33% during the ebb tide, i.e., the typhoon enhances the flood current and weakens the ebb current; (3) the salinity value increases in the NB to a maximum of 1.40 psu and water is well-mixed in the vertical direction in the typhoon’s stable and falling period. The salinity distribution gradually recovered to the normal salt wedge pattern in 3 days following the typhoon. Although this study is based on a site-specific model, the findings will provide valuable insights into saline water intrusion under typhoon events, and thus assist in implementing more efficient estuarine management strategies for drinking water safety.

The composition and distribution of wetland vegetation is critical for ecosystem diversity and sustainable development. However, tidal flat wetland environments are complex, and obtaining effective satellite imagery is challenging due to the high cloud coverage. Moreover, it is difficult to acquire phenological feature data and extract species-level wetland vegetation information by using only spectral data or individual images. To solve these limitations, statistical features, temporal features, and phenological features of multiple Landsat 8 time-series images obtained via the Google Earth Engine (GEE) platform were compared to extract species-level wetland vegetation information from Chongming Island, China. The results indicated that (1) a harmonic model obtained the phenological characteristics of wetland vegetation better than the raw vegetation index (VI) and the Savitzky–Golay (SG) smoothing method; (2) classification based on the combination of the three features provided the highest overall accuracy (85.54%), and the phenological features (represented by the amplitude and phase of the harmonic model) had the greatest impact on the classification; and (3) the classification result from the senescence period was more accurate than that from the green period, but the annual mapping result on all seasons was the most accurate. The method described in this study can be applied to overcome the impacts of the complex environment in tidal flat wetlands and to effectively classify wetland vegetation species using GEE. This study could be used as a reference for the analysis of the phenological features of other areas or vegetation types.

Abstract Surface modification is frequently used to solve the problems of low combustion properties and agglomeration for aluminum‐based fuels. However, due to the intrinsic incompatibility between the aluminum powder and the organic modifiers, the surface coating is usually uneven and disordered, which significantly deteriorates the uniformity and performances of the Al‐based fuels. Herein, a new approach of monolayer nano‐vesicular self‐assembly is proposed to prepare high‐performance Al fuels. Triblock copolymer G‐F‐G is produced by glycidyl azide polymer (GAP) and 2,2′‐(2,2,3,3,4,5,5‐Octafluorohexane‐1,6‐diyl) bis (oxirane) (fluoride) ring‐open addition reaction. By utilizing G‐F‐G vesicular self‐assembly in a special solvent, the nano‐sized vesicles are firmly adhered to the surface of Al powder through the long‐range attraction between the fluorine segments and Al. Meanwhile, the electrostatic repulsion between vesicles ensures an extremely thin coating thickness (≈15 nm), maintaining the monolayer coating structure. Nice ignition, combustion, anti‐agglomeration, and water‐proof properties of Al@G‐F‐G(DMF) are achieved, which are superior among the existing Al‐based fuels. The derived Al‐based fuel has excellent comprehensive properties, which can not only inspire the development of new‐generation energetic materials but also provide facile but exquisite strategies for exquisite surface nanostructure construction via ordered self‐assembly for many other applications.

Abstract Background The incidence of kidney disease caused by thyroid cancer is rising worldwide. Observational studies cannot recognize whether thyroid cancer is independently associated with kidney disease. We performed the Mendelian randomization (MR) approach to genetically investigate the causality of thyroid cancer on immunoglobulin A nephropathy (IgAN). Methods and results We explored the causal effect of thyroid cancer on IgAN by MR analysis. Fifty-two genetic loci and single nucleotide polymorphisms were related to thyroid cancer. The primary approach in this MR analysis was the inverse variance weighted (IVW) method, and MR‒Egger was the secondary method. Weighted mode and penalized weighted median were used to analyze the sensitivity. In this study, the random-effect IVW models showed the causal impact of genetically predicted thyroid cancer across the IgAN risk (OR, 1.191; 95% CI, 1.131–1.253, P

Abstract Background The severe unfavorable effects of doxorubicin on the heart restrict its clinical usage. Numerous investigations document that cyclic GMP-AMP synthase (cGAS) activator of interferon genes (STING) cascade influences inflammation along with the immune response in a variety of diseases. The pathophysiological function of the cGAS-STING cascade in Doxorubicin-induced cardiomyopathy (DIC) is, nevertheless, unknown. Methods In vivo, cardiotoxicity was triggered by a single dose of intra-peritoneal inoculation of doxorubicin (15 mg/kg) in wild-type C57BL/6J mice and STING knockdown animals. Adeno-associated virus 9 (AAV9) was utilized to silence STING. qPCR along with Western blotting were adopted to assess alterations in the cGAS/STING cascade. To assess cardiac function, we employed echocardiography coupled with histology, as well as molecular phenotyping. In vitro, HL-1 cardiomyocytes were introduced as test models. Results In wild type mice, doxorubicin stimulation significantly activated the cGAS/STING pathway. STING silencing increased rate of survival along with heart function in mice, as well as diminished myocardial inflammatory cytokines along with apoptosis. These observations were also confirmed by utilizing siRNA of STING in vitro studies. Conclusion This research premise established that STING inhibition could alleviate Dox-triggered cardiotoxicity in mice. As a result, preventing DIC by repressing STING in cardiomyocytes might be a possible treatment approach.

Abstract Aims Right ventricular pacing (RVP) dependence could impair left ventricular ejection fraction (LVEF). This study aimed to illuminate the relationship between RVP proportion and LVEF, as well as disclosing independent predictors of RVP dependence. Methods and results Patients indicated for permanent pacemaker implantation were included (2016–2020). The ventricular pacing lead was placed in right ventricular apex or septum. Pacing mode programming followed universal standard. Electrocardiographic, echocardiographic, and serological parameters were collected. RVP dependence was defined according to its influence on LVEF. This study was of case–control design. Included patients were matched by potentially confounding factors through propensity score matching. A total of 1183 patients were included, and the mean duration of follow‐up was 24 months. Percentage of RVP 120 ms (OR = 2.46, 95% CI: 1.42–4.27), QTc interval > 470 ms (OR = 2.01, 95% CI: 1.33–3.05), and pulmonary artery systolic pressure (PASP) > 40 mmHg (OR = 1.93, 95% CI: 1.46–2.56) were proved to predict RVP dependence. Conclusions High RVP percentage (>80%) indicating RVP dependence significantly correlates with poor prognosis of cardiac function. High degree/complete AVB, AF, ischaemic aetiology, PCI history, HRmax 120 ms, QTc interval > 470 ms, and PASP > 40 mmHg were verified as independent risk factors of RVP dependence.

During the SARS-CoV-2 (COIVD-19) outbreak, China repeatedly stressed that the response to the pandemic required action at all levels of government, including the issuance of Pandemic Bonds to help the country return to work and production. However, studies on the effectiveness of Pandemic Bonds during that period are rare. Starting with China’s national financial bond market data after COVID-19 in 2020, this paper focuses on the correlation between the Credit Spreads of the relevant bonds and the corresponding bond market rate of return, based on the Copula model. The empirical analysis is also carried out for multiple dimensional groupings such as enterprises, industries, provinces, and bond maturities. The results show that there is a significant positive correlation between the Credit Spreads of Pandemic Bonds and market returns. In addition, the market correlation is higher for Pandemic Bonds issued in Hubei Province, which is at the center of the 2020 pandemic, and the shorter the maturity of the Pandemic Bond issued, the stronger the relationship with market returns. Finally, this paper provides recommendations for financial regulators and policy makers to consider in their decisions on how to build a more resilient financial system under heavy economic, fiscal, and social pressures.

BackgroundInflammatory response markers are prognostic factors for several cancers, but their role in postoperative colorectal cancer (CRC) is unclear. The purpose was to evaluate the role of preoperative Neutrophil-to-Lymphocyte ratio (NLR), Platelet-to-Lymphocyte-ratio (PLR), and Lymphocyte-to-Monocyte ratio (LMR) in the prognosis of postoperative CRC patients.MethodsWe retrospectively reviewed 448 CRC patients who had undergone surgical resection from December 2015 to December 2017 in our hospital. The plasma NLR, PLR, LMR, CEA, and CA19-9 were collected within 2 weeks before the operation. We recorded the clinical characteristics and survival data by reviewing medical records and phone calls. We analyzed preoperative inflammatory markers and clinical features using Pearson chi-squared tests or Fisher’s tests. Uni- and multivariate Cox regression analyses were performed, and overall survival (OS) was estimated with the Kaplan–Meier method.ResultsHigh NLR and PLR were associated with worse overall survival in postoperative CRC (HR = 2.140, 95%CI = (1.488-3.078), P < 0.001; HR =1.820, 95%CI = (1.271-2.605), P = 0.001). High LMR was associated with improved overall survival in postoperative CRC (HR = 0.341, 95%CI = (0.188-0.618), P < 0.001). In the multivariate regression analysis, the increase of NLR resulted in an increase in the risk of death (HR = 1.678, 95%CI = (1.114-2.527), P = 0.013), and for the LMR, a reduction of the risk of death (HR = 0.480, 95%CI = (0.256 - 0.902), P = 0.023). Moreover, TNM stage, CA-199, CEA, nerve or vascular invasion (NVI) and adjuvant chemotherapy after surgery also were associated with worse overall survival in postoperative CRC.ConclusionCurrent evidence indicates that preoperative inflammatory markers NLR, LMR, and PLR are associated with overall survival in postoperative patients with colorectal cancer. NLR is an independent risk factor, and LMR is an independent protective factor in CRC patients after surgery.

The global epidemic of COVID-19 has made a huge impact on global health and financial markets. And the spread of the virus has stalled economic development in many parts of the world. As stocks and bonds are two important financial assets, how to take appropriate economic policies to restore the stock and bond markets is the focus of governments as they are seeking for quick recovery. Based on the Event Study method and the GARCH model, data from 1 October 2019 to 1 April 2020 were collected from 26 countries or regions as analytic samples. The results show: 1) COVID-19 has made greater impacts on the stock market than the bond market; 2) the economic policy responses after the COVID-19 has brought impacts on both of the stock and the bond markets; 3) the monetary policy responses has brought greater volatility to the stock market than the fiscal policy responses, while the fiscal policy responses has brought greater volatility to the bond market than the monetary policy; 4) the fiscal policy has brought more positive effects on the stock market, and monetary policy has brought more positive effects on the bond market. This research is helpful to understand the mechanism of COVID-19’s impacts on the stock and bond market. And it is of great practical significance to the governments’ decisions to make economic policy responses after an epidemic.

Abstract Backgrounds Inflammation underlies the mechanism of different kinds of heart disease. Cytoplasmic membrane localized N‐terminal fragment of gasdermin‐D (GSDMD‐N) could induce inflammatory injury to cardiomyocyte. However, effects and dynamic changes of GSDMD during the process of lipopolysaccharide (LPS) related inflammatory stress induced cardiomyocyte injury are barely elucidated to date. In this study, LPS related cardiomyocyte injury was investigated based on potential interaction of GSDMD‐N induced mitochondrial injury and mitophagy mediated mitochondria quality control. Methods HL‐1 cardiomyocytes were treated with LPS and Nigericin to induce inflammatory stress. The dual‐fluorescence‐labelled GSDMD expressed HL‐1 cardiomyocytes were constructed to study the translocation of GSDMD. The mitochondrial membrane potential (MMP) was measured by JC‐1 staining. Mitophagy and autophagic flux were recorded by transmission electron microscopy and fluorescent image. Results GSDMD‐N showed a time‐dependent pattern of translocation from mitochondria to cytoplasmic membrane under LPS and Nigericin induced inflammatory stress in HL‐1 cardiomyocytes. GSDMD‐N preferred to localize to mitochondria to permeablize its membrane and dissipate the MMP. This effect couldn't be reversed by cyclosporine‐A (mPTP inhibitor), indicating GSDMD‐N pores as alternative mechanism underlying MMP regulation, in addition to mitochondrial permeability transition pore (mPTP). Moreover, the combination between GSDMD‐N and autophagy related Microtubule Associated Protein 1 Light Chain 3 Beta (LC3B) was verified by co‐immunoprecipitation. Besides, mitophagy alleviating GSDMD‐N induced mitochondrial injury was proved by pre‐treatment of autophagy antagonist or agonist in GSDMD‐knock out or GSDMD‐overexpression cells. A time‐dependent pattern of GSDMD translocation and mitochondrial GSDMD targeted mitophagy were verified. Conclusion Herein, our study confirmed a crosstalk between GSDMD‐N induced mitochondrial injury and mitophagy mediated mitochondria quality control during LPS related inflammation induced cardiomyocyte injury, which potentially facilitating the development of therapeutic target to myocardial inflammatory disease. Our findings support pharmaceutical intervention on enhancing autophagy or inhibiting GSDMD as potential target for inflammatory heart disease treatment.

BackgroundBlood glucose disorders are prevalent in heart failure, while the influence of the gut microbiota on this process remains unclear. Here, we used heart failure model mice and fecal microbiota transplantation (FMT) mice to evaluate the effect of the gut microbiota on the regulation of blood glucose during heart failure.MethodsThoracic aortic constriction (TAC) surgery was performed in a heart failure model, while an antibiotic cocktail was used to eliminate the microbiota to establish a germ-free (GF) model. Blood glucose, insulin, and glucagon levels were measured, and an intraperitoneal glucose tolerance test (IPGTT) was performed. 16S rRNA sequencing and metabolomics were used to evaluate the changes in gut microbiota structure and metabolism induced by TAC. Another group of FMT mice was established to observe the effect of the gut microbiota on host metabolism.ResultsAfter microbiota clearance, the glucagon concentration, the homeostasis model assessment for insulin resistance (HOMA-IR), and the area under the curve (AUC) of the IPGTT were decreased significantly in the TAC germ-free (TAC-GF) group in the third month as compared to the other groups. 16S rRNA sequencing indicated that TAC surgery affected the gut microbiota structure, and fecal metabolomics suggested that noradrenaline and adrenaline levels were higher in the TAC group than in the sham group. The FMT mice transplanted with the feces of the TAC (FMT-TAC) mice displayed a higher AUC of IPGTT, accompanied by a higher glucagon level, insulin level, and HOMA-IR than those of the mice in the other groups. The serum metabolomics of the FMT-TAC group showed that noradrenaline levels were significantly higher than those of the FMT-sham group.ConclusionThe gut microbiota and its metabolism were altered during heart failure, which increased blood glucose and glucagon in the host.

Increasing demand for land resources at the coast has exerted immense pressure on vulnerable environments. Population and economic growth in coastal cities have combined to produce a scarcity of suitable space for development, the response to which has frequently been the reclamation of land from the sea, most prominently in China. Urbanization is a key driver of such changes and a detailed investigation of coastal land reclamation at the city scale is required. This study analyzed remote sensing imagery for the period 1990 to 2018 to explore the trajectories of coastal land reclamation in nine major urban agglomerations across the three largest deltas in China using the JRC Global Surface Water (Yearly Water Classification History, v1.1) (GSW) dataset on the Google Earth Engine platform. The results are considered in the context of major national policy reforms over the last three decades. The analysis reveals that total land reclaimed among nine selected cities had exceeded 2800 km2 since 1984, 82% of which occurred after 2000, a year following the enactment of China's agricultural 'red line' policy. Shanghai exhibited the greatest overall area of land extension, followed by Ningbo and Tianjin, especially in the period following the privatization of property rights in 2004. In analyzing annual trends, we identified the developmental stages of a typical coastal reclamation project and how these vary between cities. Scrutiny of the results revealed voids in nighttime light satellite data (2014–2018) in some localities. Although these voids appeared to be characterized by construction, they were occupied by vacant buildings, and were therefore examples of so-called "ghost cities." In China, as elsewhere, continual land reclamation needs to be considered in relation to, inter alia, sea level rise and land subsidence that pose significant challenges to the vision of sustainable urban development in these three deltaic megacities. [ABSTRACT FROM AUTHOR]