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Abstract Nitrate (NO3 −) leaching from nitrogen (N) fertilized soils is a significant global concern, affecting both the environment and public health. However, substantial uncertainties and variabilities in NO3 − leaching factors (LFs) among regions or crops impede accurate assessments of NO3 − leaching. Here we synthesize 2500 field observations worldwide and show that LFs vary by an order of magnitude across regions and crops, primarily driven by hydroclimatic and edaphic conditions rather than N fertilizer management. Global cropland NO3 − leaching from synthetic N fertilization, calculated through spatially explicit (15.4, 14.8–16.1 Tg N yr–1) and crop-specific (12.9, 11.0–14.8 Tg N yr–1) LFs, is 41% lower than the Intergovernmental Panel on Climate Change Tier 1 global inventory. Over 47% of this leaching is concentrated in China, India, and the United States, with maize, wheat, rice and vegetables accounting for nearly half of it. Improved regional and crop-specific LFs will provide a benchmark for NO3 − leaching abatement by pinpointing potential global hotspots.

Abstract The spatiotemporal overlap of multiple hazards defines what we call concurrent hazards, which usually cause more severe damage than what an isolated hazard would. Investigations of concurrent hazards at the global scale are limited. Here we first developed a novel criterion system for identifying concurrent hazards and then recognized 1,614 concurrent hazards during 1981–2020 from the 121,214 records including earthquake, storm, landslide, volcanic, wildfire and flood. Sixteen hot spot regions suffering from concurrent hazards were recognized for the first time at the global scale. By comparing two periods, 1981–2000 and 2001–2020, we found that the gross relative impact (economic damage and death) of concurrent hazards has considerably aggravated (6.3–117.0 times) in the past two decades. The low‐income regions suffer more prominent increase (mostly 2–3 times of high‐income regions), implying the inequitable patterns of concurrent hazard impact due to socioeconomic development. This spatial disparity entails the establishment of multidisciplinary and cross‐regional collaborations in mitigating the impact of concurrent hazards.

Abstract Lentic systems (lakes and reservoirs) are emission hotpots of nitrous oxide (N2O), a potent greenhouse gas; however, this has not been well quantified yet. Here we examine how multiple environmental forcings have affected N2O emissions from global lentic systems since the pre-industrial period. Our results show that global lentic systems emitted 64.6 ± 12.1 Gg N2O-N yr−1 in the 2010s, increased by 126% since the 1850s. The significance of small lentic systems on mitigating N2O emissions is highlighted due to their substantial emission rates and response to terrestrial environmental changes. Incorporated with riverine emissions, this study indicates that N2O emissions from global inland waters in the 2010s was 319.6 ± 58.2 Gg N yr−1. This suggests a global emission factor of 0.051% for inland water N2O emissions relative to agricultural nitrogen applications and provides the country-level emission factors (ranging from 0 to 0.341%) for improving the methodology for national greenhouse gas emission inventories.

Objective(s): Liver fibrosis is a common liver disease caused by chronic liver damage. However, there are currently no approved drugs available to treat it. Therefore, the therapeutic effect of indirubin on liver fibrosis was evaluated. This study investigated the protective effect and related molecular mechanism of indirubin against CCl4-induced liver fibrosis in mice.Materials and Methods: We first detected the effect of indirubin on liver fibrosis in mice (n=8 per group, 32 mice total) by ELISA, HE, and Masson staining. Subsequently, the proliferation of activated HSCs was detected by MTT and EdU. Finally, the changes of related proteins and signaling pathways in mice treated with indirubin were investigated by qRT-PCR and Western blot. One-way ANOVA or two-tailed student’s t-test was used for comparison between groups.Results: Firstly, we found that indirubin (25 mg/kg) therapy could attenuate liver injury and significantly down-regulate α-SMA (P=0.0038) and collagen 1 (P=0.0057) in the liver using CCl4-induced liver fibrosis in mice. Secondly, we showed that indirubin (25 μM) could significantly inhibit hepatic stellate cell (HSC) trans-differentiation into myofibroblasts and proliferation (P=0.0063) in HSC-T6 cells treated by TGF-β. Finally, we showed that indirubin could greatly reduce the protein levels of p-Smad2/3, p38, p-ERK, and p-JNK in vivo and in vitro.Conclusion: Our results suggested that indirubin alleviated liver fibrosis and HSC activation mainly through TGF-β-mediated signaling pathways in vivo and in vitro. In conclusion, our data showed that indirubin could be a promising clinical therapeutic drug for the prevention and treatment of liver fibrosis.

The use of location-based service brings convenience to people’s daily lives, but it also raises concerns about users’ location privacy.In the k-nearest neighbor query problem, constructing K-anonymizing spatial regions is a method used to protects users’ location privacy, but it results in a large waste of communication overhead.The SpaceTwist scheme is an alternative method that uses an anchor point instead of the real location to complete the k-nearest neighbor query,which is simple to implement and has less waste of communication overhead.However,it cannot guarantee K-anonymous security, and the specific selection method of the anchor point is not provided.To address these shortcomings in SpaceTwist, some schemes calculate the user’s K-anonymity group by introducing a trusted anonymous server or using the way of user collaboration, and then enhance the end condition of the query algorithm to achieve K-anonymity security.Other schemes propose the anchor point optimization method based on the approximate distribution of interest points, which can further reduce the average communication overhead.A lightweight K-anonymity incremental nearest neighbor (LKINN) location privacy protection algorithm was proposed to improve SpaceTwist.LKINN used convex hull mathematical tool to calculate the key points of K-anonymity group, and proposed an anchor selection method based on it, achieving K-anonymity security with low computational and communication costs.LKINN was based on a hybrid location privacy protection architecture, making only semi-trusted security assumptions for all members of the system, which had lax security assumptions compared to some existing research schemes.Simulation results show that LKINN can prevent semi-trusted users from stealing the location privacy of normal users and has smaller query response time and communication overhead compare to some existing schemes.

Abstract Unmanned aerial vehicles (UAVs) are widely used in wireless communication networks due to their rapid deployment and high mobility. However, in practical scenarios, the existence of obstacles and eavesdroppers will seriously interfere with the communication quality of the UAV network and produce a security risk. Thus, this paper combines reconfigurable intelligent surface (RIS) technology with UAVs to build a secure UAV communication network. Normally, a rotary‐wing UAV (labeled as UAV‐S) acting as a base station sends information signals to a legitimate user on the ground with RIS equipment. However, there is a passive eavesdropper on the ground who can steal the information. Therefore, a friendly UAV jammer (labeled as UAV‐J) with a fixed location is introduced to send jamming signals to confuse the eavesdropper. The goal of this paper is to maximize the average secrecy rate of the communication network by jointly optimizing the flight trajectory, transmit power of the UAV‐S and UAV‐J, and phase shifter of the RIS. Since the constructed problem is highly nonconvex, an alternating optimization algorithm based on successive convex approximation techniques is proposed to solve the problem. Simulation results show that the proposed algorithm can achieve a higher secrecy rate in comparison with other schemes.

Global wetlands are the largest and most uncertain natural source of atmospheric methane (CH _4 ). The FLUXNET-CH _4 synthesis initiative has established a global network of flux tower infrastructure, offering valuable data products and fostering a dedicated community for the measurement and analysis of methane flux data. Existing studies using the FLUXNET-CH _4 Community Product v1.0 have provided invaluable insights into the drivers of ecosystem-to-regional spatial patterns and daily-to-decadal temporal dynamics in temperate, boreal, and Arctic climate regions. However, as the wetland CH _4 monitoring network grows, there is a critical knowledge gap about where new monitoring infrastructure ought to be located to improve understanding of the global wetland CH _4 budget. Here we address this gap with a spatial representativeness analysis at existing and hypothetical observation sites, using 16 process-based wetland biogeochemistry models and machine learning. We find that, in addition to eddy covariance monitoring sites, existing chamber sites are important complements, especially over high latitudes and the tropics. Furthermore, expanding the current monitoring network for wetland CH _4 emissions should prioritize, first, tropical and second, sub-tropical semi-arid wetland regions. Considering those new hypothetical wetland sites from tropical and semi-arid climate zones could significantly improve global estimates of wetland CH _4 emissions and reduce bias by 79% (from 76 to 16 TgCH _4 y ^−1 ), compared with using solely existing monitoring networks. Our study thus demonstrates an approach for long-term strategic expansion of flux observations.

Reversible data hiding in encrypted images has attracted considerable attention due to the privacy-preserving requirement for cloud data management.The good performance in this area can be achieved by using the existing framework of reserving room before encryption, where the image is partitioned to two independent slices consisting of blocks and then traditional reversible data hiding techniques are utilized to vacate room.In order to better exploit the spatial correlation of images, a fine-grained scalable embedding room reservation strategy in which blocks were rearranged to constitute the textured slice and the smooth slice was proposed.The block-size can be adjusted adaptively according to the texture of the images and the size of room to be vacated.The original locations of these blocks were efficiently represented as the to-be-embedded auxiliary information for image restoration.Because pixels in the smooth slice are easier to be predicted, the smooth slice can contain more pixel bits from the textured slice to reserve more room and fewer embedding distortions are induced with traditional reversible data hiding techniques.Extensive experiments demonstrate the merits of the proposed method in terms of embedding capacity and image quality.

Mining cities are clusters of communities that specialized in mining and extractive industries. The extensive mining activities in these cities have stimulated widespread and substantial ecological stresses to the surrounding environment, that significantly jeopardize the health condition of vegetation and human. Given the recent recognition of remote sensing in monitoring large-scale environmental change, we incorporated Ziyuan #1-02D, a recently released hyperspectral remote sensing data, into the ecological vulnerability assessment framework, using Panzhihua city as a case study, which is recognized as one of the most representative mining cities in China. The multi-spectral imaging data was widely applied in previous research. However, with the wide bands, multi-spectral imaging data cannot depict detailed characteristics of spectral targeted. As a result, we introduce indexes from the hyperspectral imaging to ecological vulnerability assessment proposed in this study, which can depict and monitor the growth and restoration of vegetation more accurately. We used the optimum index factor method to select bands from the satellite-based Ziyuan #1-02D data for quantifying vegetation indexes and red edge. Besides, we obtained inventory data, land-use, soil type, and typography of Panzhihua city to reconstruct its ecological vulnerability index (EVI) for 2020 and 2021. Comparing to the multi-spectral data, the ecological vulnerability results from hyperspectral imaging performed better in precision and concentration in EVI values, reaching the conclusions more directly. Specifically, the mining area and the relevant hazard types and impact areas were delineated through intensive fieldwork. Results suggested that the east and west districts, and north of Renhe district suffer great ecological stress, in which we observed intensive coal and metal-related mining industry. The central region, which occupies vanadium titanomagnetite mines, also shows substantial ecological issues, while the other mining industries, such as granite ore did not significantly influence the local environment. Although the newly released satellite-based data only have two-year periods, we still observed improving ecological conditions, with the southeast and west regions showing much lower ecological vulnerability values. The spatial autocorrelation analysis suggested that the high-high clustering region of EVI is located in the east, west, and Renhe districts, primarily due to the mining industries of variations scales. We also found that the clustering of low ecological vulnerable regions, mostly surrounds the vanadium titanomagnetite excavation industry, thanks to the local restoration projects.

Understanding trends of vegetation evolution and its spatial characteristics is critical for sustainable social development in the Greater Mekong Subregion (GMS), which is densely populated and still has uneven economic development. Through Theil–Sen/Mann–Kendall tests, polynomial regression and bivariate local autocorrelation analyses, we investigated vegetation greening trends and their spatial correlation with bioclimatic and environmental variables. The study yielded the following results: (1) Land cover in the GMS has changed significantly over the last 20 years. Conversion between forest and grassland was the main type of change. (2) The upward trend in the forest enhanced vegetation index (EVI) significantly exceeded the downward trend in countries over 20 years. In GMS, the spatial variation in forest trend slope values ranged from −0.0297 a−1 to 0.0152 a−1. (3) Anthropogenic activities have played an important role in forest greening; planted, plantation and oil palm forests exhibit the largest contributions to greening. (4) Changes in forest EVI were most spatially correlated with radiation (12.19% for surface net solar radiation and 12.14% for surface solar radiation downwards) and least spatially correlated with seasonality precipitation (8.33%) and mean annual temperature (8.19%). The results of the analysis of EVI trends in vegetation and their spatial correlation with bioclimatic and environmental variables can provide a reference for strategies aimed for protecting the vegetation ecology.

With the privacy leakage problem of image delivery in networks,an image privacy preservation scheme based on QR code and reversible visible watermarking was proposed.In order to preserve the privacy information including shooting time,shooting location and detailed description,the encrypted privacy information was stored in cloud platforms and corresponding universal resource locator was generated firstly.Secondly,the universal resource locator was encoded as the QR code and it was embedded into the cover image using reversible visible watermarking.Therefore,the access control for privacy information could be achieved.The visual perceptual model was utilized to select adaptive watermark embedding parameters to balance watermark visibility and marked image quality.Experimental results demonstrate that the good visual quality of marked images can be obtained and the embedded QR code can be correctly decoded.The proposed scheme can guarantee marked image quality and reduce the risk of private information leakage.

Abstract Mapping flood risk zone is an essential task in the arid region for sustainable water resources management. Due to the lack of hydrological and meteorological information and disaster event inventory in Xinjiang, China, storm flood disaster (SFD) risk zoning is an effective technique in investigating the potential impact of SFD. In this study, the statistics about natural, social, and risk related to SFD are collated. With the help of the compiled inventory data, a disaster risk assessment model for storm flood is proposed for the Xinjiang region based on the random forest (RF) algorithm. Randomly selected negative and positive samples from the historical SFD locations are composed of five different total samples. The overall prediction accuracy of the five sample groups attained 83.48%, indicating that the proposed RF model can well capture the spatial distribution of SFD in Xinjiang. It should also be noted that the spatial heterogeneity and complexity of SFD had a significant effect on its spatial distribution in Xinjiang. There are spatial distribution characteristics of lowland plains and high plateaus; the main mountainous regions, plains in the middle‐lower reaches of major rivers, and areas surrounding major lakes are prone to flooding. The variable importance RF indicates that the disaster risk is mainly affected by the following factors, including hazard factors, catastrophic intensity, population density, as well as economic development in the affected area. Besides, latitude, longitude, agricultural acreage, road density, distance from rivers, and the maximum monthly precipitation account for most of the increase in storm flooding disasters, and they are the main triggering point for SFD in Xinjiang. The proposed model provides some insight into the disaster in the mountainous region, and gives useful guidance for the national macro‐control of flood prevention and disaster reduction.

Abstract The biogeochemical processes of carbon (C), nitrogen (N), and phosphorous (P) are fully coupled in the Earth system, which shape the structure, functioning, and dynamics of terrestrial ecosystems. However, the representation of P cycle in terrestrial biosphere models (TBMs) is still in an early stage. Here we incorporated P processes and C‐N‐P interactions into the C‐N coupled Dynamic Land Ecosystem Model (DLEM‐CNP), which had a major feature of the ability in simulating the N and P colimitation on vegetation C assimilation. DLEM‐CNP was intensively calibrated and validated against daily or annual observations from four eddy covariance flux sites, two Hawaiian sites along a chronosequence of soils, and other 13 tropical forest sites. The results indicate that DLEM‐CNP significantly improved simulations of forest gross and net primary production (R2: 0.36–0.97, RMSE:1.1–1.49 g C m−2 year−1 for daily GPP at eddy covariance flux sites; R2 = 0.92, RMSE = 176.7 g C m−2 year−1 for annual NPP across 13 tropical forest sites). The simulations were also consistent with field observations in terms of biomass, leaf N:P ratio and plant response to fertilizer addition. A sensitivity analysis suggests that simulated results are reasonably robust against uncertainties in model parameter estimates and the model was very sensitive to parameters of P uptake. These results suggest that incorporating P processes and N‐P interaction into terrestrial biosphere models is of critical importance for accurately estimating C dynamics in tropical forests, particularly those P‐limited ones.

Flash floods are among the most dangerous natural disasters. As climate change and urbanization advance, an increasing number of people are at risk of flash floods. The application of remote sensing and geographic information system (GIS) technologies in the study of flash floods has increased significantly over the last 20 years. In this paper, more than 200 articles published in the last 20 years are summarized and analyzed. First, a visualization analysis of the literature is performed, including a keyword co-occurrence analysis, time zone chart analysis, keyword burst analysis, and literature co-citation analysis. Then, the application of remote sensing and GIS technologies to flash flood disasters is analyzed in terms of aspects such as flash flood forecasting, flash flood disaster impact assessments, flash flood susceptibility analyses, flash flood risk assessments, and the identification of flash flood disaster risk areas. Finally, the current research status is summarized, and the orientation of future research is also discussed.

Research on the service values of urban ecosystems is a hot topic of ecological studies in the current era of rapid urbanization. To quantitatively estimate the ecosystem service value in Chengdu, China from the perspectives of natural ecology and social ecology, the technologies of remote sensing (RS) and geographic information system (GIS) are utilized in this study to extract the land use type information from RS images of Chengdu in 2003, 2007, 2013 and 2018. Subsequently, a driver analysis of the ecosystem services of Chengdu was performed based on socioeconomic data from the last 16 years. The results indicated that: (1) from 2003 to 2018, the land utilization in Chengdu changed significantly, with the area of cultivated lands, forest lands and water decreasing remarkably, while the area of construction lands dramatically increased. (2) The ecosystem services value (ESV) of Chengdu decreased by 30.92% in the last 16 years, from CNY 2.4078 × 1010 in 2003 to CNY 1.6632 × 1010 in 2018. Based on a future simulation, the ESV is further predicted to be reduced to CNY 1.4261 × 1010 by 2033. (3) The ESV of Chengdu showed a negative correlation with the total population, the urbanization rate and the per capita GDP of the region, indicating that the ESV of the studied region was inter-coupled with the socioeconomic development and can be maintained at a high level through rationally regulating the socioeconomic structure.

Recently, the advances in perfluorocarbon nanoparticles have been introduced to expand the diagnostic and therapeutic capability of ultrasound molecular imaging, a non-invasive diagnostic imaging, which passed from robust anatomical presentation to detection of physiological processes and recognition of specific tissue epitopes at the cellular level. The good properties of perfluorocarbon nanoparticles, such as nontoxicity, small size, phase-shift capability, etc., have been resulted in tremendous potential prospects in clinical application. Herein, this review focuses on the mechanisms of perfluorocarbon nanoparticles in terms of phase transition and ultrasonic theranostic. And the potential applications of perfluorocarbon nanoparticles in ultrasound medicine are also discussed.

Microbubbles (MBs) are considered as an important enhancer for high intensity focused ultrasound (HIFU) treatment of benign or malignant tumors. Recently, different sizes of gas-filled bubbles have been investigated to improve the therapeutic efficiency of HIFU thermal ablation and reduce side effects associated with ultrasound power and irradiation time. However, nanobubbles (NBs) as an ultrasound contrast agent for synergistic therapy of HIFU thermal ablation remain controversial due to their small nano-size in diameter. In this study, phospholipid-shell and gas-core NBs with a narrow size range of 500–600 nm were developed. The synergistic effect of NBs for HIFU thermal ablation was carefully studied both in excised bovine livers and in breast tumor models of rabbits, and made a critical comparison with that of commercial SonoVue microbubbles (SonoVue MBs). In addition, the pathological changes of the targeted area in tumor tissue after HIFU ablation were further investigated. Phosphate buffer saline (PBS) was used as the control. Under the same HIFU parameters, the quantitative echo intensity of B-mode ultrasound image and the volume of coagulative necrosis in lipid NBs groups were significantly higher and larger than that in PBS groups, but could not be demonstrated a difference to that in SonoVue MBs groups both ex vivo and in vivo. These results showed that the synergistic effect of lipid NBs for HIFU thermal ablation were similar with that of SonoVue MBs, and further indicate that lipid NBs could potentially become an enhancer for HIFU thermal ablation of tumors.

Objective(s): Liver fibrosis is a common liver disease caused by chronic liver damage. However, there are currently no approved drugs available to treat it. Therefore, the therapeutic effect of indirubin on liver fibrosis was evaluated. This study investigated the protective effect and related molecular mechanism of indirubin against CCl4-induced liver fibrosis in mice. Materials and Methods: We first detected the effect of indirubin on liver fibrosis in mice (n=8 per group, 32 mice total) by ELISA, HE, and Masson staining. Subsequently, the proliferation of activated HSCs was detected by MTT and EdU. Finally, the changes of related proteins and signaling pathways in mice treated with indirubin were investigated by qRT-PCR and Western blot. One-way ANOVA or two-tailed student's t-test was used for comparison between groups. Results: Firstly, we found that indirubin (25 mg/kg) therapy could attenuate liver injury and significantly down-regulate α-SMA (P=0.0038) and collagen 1 (P=0.0057) in the liver using CCl4-induced liver fibrosis in mice. Secondly, we showed that indirubin (25 μM) could significantly inhibit hepatic stellate cell (HSC) trans-differentiation into myofibroblasts and proliferation (P=0.0063) in HSC-T6 cells treated by TGF-β. Finally, we showed that indirubin could greatly reduce the protein levels of p-Smad2/3, p38, p-ERK, and p-JNK in vivo and in vitro. Conclusion: Our results suggested that indirubin alleviated liver fibrosis and HSC activation mainly through TGF-β-mediated signaling pathways in vivo and in vitro. In conclusion, our data showed that indirubin could be a promising clinical therapeutic drug for the prevention and treatment of liver fibrosis. [ABSTRACT FROM AUTHOR]

Climate-sensitive northern cryosphere inland waters emit greenhouse gases (GHGs) into the atmosphere, yet their total emissions remain poorly constrained. We present a data-driven synthesis of GHG emissions from northern cryosphere inland waters considering water body types, cryosphere zones, and seasonality. We find that annual GHG emissions are dominated by carbon dioxide (1149.21307.5 1004.8 teragrams of CO2; medianQ3 Q1) and methane (14.218.5 10.1 teragrams of CH4), while the nitrous oxide emission (5.412.2 -1.4 gigagrams of N2O) is minor. The annual CO2-equivalent (CO2e) GHG emissions from northern cryosphere inland waters total 1.51.8 1.3 or 2.32.8 1.8 petagrams of CO2e using the 100-or 20-year global warming potentials, respectively. Rivers emit 64% more CO2e GHGs than lakes, despite having only one-fifth of their surface area. The continuous permafrost zone contributed half of the inland water GHG emissions. Annual CO2e emissions from northern cryosphere inland waters exceed the region's terrestrial net ecosystem exchange, highlighting the important role of inland waters in the cryospheric land-aquatic continuum under a warming climate. [ABSTRACT FROM AUTHOR]

The article informs Radar remote sensing provides a new perspective on rainfall kinetic energy (RKE) and its role in soil erosion, challenging traditional empirical relations between RKE and rainfall intensity. The study presents global estimates of RKE based on raindrop microphysics, highlighting potential underestimations of RKE and rainfall erosivity with implications for soil erosion assessment and conservation practices, particularly in the context of climate change.