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Warming on Earth’s Third Pole is leading to rapid loss of ice and the formation and expansion of glacial lakes, posing a severe threat to downstream communities. Here we provide a holistic assessment of past evolution, present state and modelled future change of glacial lakes and related glacial lake outburst flood (GLOF) risk across the Third Pole. We show that the highest GLOF risk is at present centred in the eastern Himalaya, where the current risk level is at least twice that in adjacent regions. In the future, GLOF risk will potentially almost triple as a consequence of further lake development, and additional hotspots will emerge to the west, including within transboundary regions. With apparent increases in GLOF risk already anticipated by the mid-twenty-first century in some regions, the results highlight the urgent need for forward-looking, collaborative, long-term approaches to mitigate future impacts and enhance sustainable development across the Third Pole.Global warming-driven deglaciation in high-mountain Asia raises the likelihood of natural dam failure and associated glacial lake outburst flood risk. This is estimated for lake development under present-day and future warming scenarios, highlighting emerging hotspots and transboundary impacts.

Landslide disasters frequently occur along the highway G30 in the Guozigou Valley, the corridor of energy, material, economic and cultural exchange, etc., between Yili and other cities of China and Central Asia. However, little attention has been paid to assess the detailed landslide susceptibility of the strategically important highway, especially with high spatial resolution data and the generative presence-only MaxEnt model. Landslide susceptibility assessment (LSA) is a first and vital step for preventing and mitigating landslide hazards. The goal of the current study was to perform LSA for the landslide-prone highway G30 in Guozigou Valley, China with the aid of GIS tools and Chinese high resolution Gaofen-1 (GF-1) satellite data, and analyze and compare the performance of the maximum entropy (MaxEnt) model and logistic regression (LR). Thirty five landslides were determined in the study region, using GF-1 satellite data, official data, and field surveys. Seven landslide conditioning factors, including altitude, slope, aspect, gully density, lithology, faults density, and NDVI, were used to investigate their existing spatial relationships with landslide occurrences. The LR and MaxEnt model performance were assessed by the receiver operating characteristic curve, presenting areas under the curve equal to 0.85 and 0.94, respectively. The performance of the MaxEnt model was slightly better than that of the LR model. A landslide susceptibility map was created through reclassifying the landslides occurrence probability with the classification method of natural breaks. According to the MaxEnt model results, 3.29% and 3.82% of the study region is highly and very highly susceptible to future landslide events, respectively, with the highest landslide susceptibility along the highway. The generated landslide susceptibility map could help government agencies and decision-makers to make wise decisions for preventing or mitigating landslide hazards along the highway and design schemes of highway engineering and maintenance in Guozigou Valley, the mountainous areas.

Mitochondrial autophagy (or mitophagy) regulates the mitochondrial network and function to contribute to multiple cellular processes. The protective effect of homeostatic mitophagy in cardiovascular diseases (CVDs) has attracted increasing attention. FUN14 domain containing 1 (FUNDC1), an identified mitophagy receptor, plays an essential role in CVDs. Different expression levels of FUNDC1 and its phosphorylated state at different sites alleviate or exacerbate hypoxia and ischemia/reperfusion injury, cardiac hypertrophy, or metabolic damage through promotion or inhibition of mitophagy. In addition, FUNDC1 can be enriched at contact sites between mitochondria and the endoplasmic reticulum (ER), determining the formation of mitochondria-associated membranes (MAMs) that regulate cellular calcium (Ca 2+ ) homeostasis and mitochondrial dynamics to prevent heart dysfunction. Moreover, FUNDC1 has also been involved in inflammatory cardiac diseases such as septic cardiomyopathy. In this review, we collect and summarize the evidence on the roles of FUNDC1 exclusively in various CVDs, describing its interactions with different cellular organelles, its involvement in multiple cellular processes, and its associated signaling pathways. FUNDC1 may become a promising therapeutic target for the prevention and management of various CVDs.

Using Earth observation to accurately extract snow phenology changes is of great significance for deepening the understanding of the ecological environment and hydrological process, agricultural and animal husbandry production, and high-quality development of the social economy in Xinjiang. Considering snow cover phenology based on MODIS product MOD10A1 data, this paper constructed a method for automatically extracting key phenological parameters in Xinjiang and calculated three key phenological parameters in Xinjiang from 2001 to 2020, including SCD (snow cover duration), SOD (snow onset date), and SED (snow end date). The daily data of four field camera observation points during an overlapping period from 2017 to 2019 were used to evaluate the snow cover phenological parameters extracted by MOD10A1, and the mean absolute error (MAE) and root mean square error (RMSE) values were 0.65 and 1.07, respectively. The results showed the following: 1. The spatiotemporal variation in snow phenology was highly altitude dependent. The mean gradients of SCD in the Altai Mountains, Tienshan Mountains, and Kunlun Mountains is 2.6, 2.1, and 1.2 d/100 m, respectively. The variation trend of snow phenology with latitude and longitude was mainly related to the topography of Xinjiang. Snow phenological parameters of different land-use types were different. The SCD values in wasteland were the lowest and the SED was the earliest, while forest land was the first to enter SOD accumulation. According to the study, the mean annual values of SCD, SOD, and SED were 25, 342 (8 December), and 51 (8 February) as day of year (DOY), respectively. The snow cover area was mainly distributed in the Altai Mountains, Junggar Basin, Tianshan Mountains, and Kunlun Mountains. 2. The variation trend and significance of snow cover phenological parameters in different regions are different, and the variation trend of snow cover phenological parameters in most regions of Xinjiang is non-significant.

The rapid desiccation of the Aral Sea has transformed the region into one of the world’s most severe soil wind-erosion hotspots. Despite growing concern, long-term, high-resolution assessments and driver attribution remain insufficient. This study integrates the Revised Wind Erosion Equation (RWEQ) with multi-source remote sensing data on the Google Earth Engine (GEE) platform to simulate wind erosion dynamics from 1990 to 2020. The residual trend method was used to disentangle the contributions of climate change and human activities, while erosion risk was assessed using the Information Quantity model and Analytic Hierarchy Process (AHP). This study reveals five key findings: (1) wind erosion increased significantly after 2011, peaking in 2015 with an annual growth rate of 2.418 kg/m2. (2) The Aral Sea Basin’s relative contribution to regional erosion declined sharply, indicating a shift in dominant erosion zones to peripheral deserts. (3) Climate change emerged as the primary driver, contributing 70.19% overall, and up to 92.13% in recent years, while human activities showed a peak influence (55.53%) in 2005. (4) Spatial attribution showed climate dominance in desert areas and localized human impact in exposed lakebeds. (5) High-risk erosion zones expanded rapidly into the Kyzylkum Desert after 2010, due to rising wind speeds and vegetation loss. This study provides a robust remote sensing–based framework for wind erosion monitoring and attribution, offering critical insights for erosion mitigation and ecological restoration in arid, climate-sensitive regions.

Inland lakes are major surface water resource in arid regions of Central Asia. The area changes in these lakes have been proved to be the results of regional climate changes and recent human activities. This study aimed at investigating the area variations of the nine major lakes in Central Asia over the last 30 years. Firstly, multi-temporal Landsat imagery in 1975, 1990, 1999, and 2007 were used to delineate lake extents automatically based on Normalized Difference Water Index (NDWI) threshold segmentation, then lake area variations were detailed in three decades and the mechanism of these changes was analyzed with meteorological data and hydrological data. The results indicated that the total surface areas of these nine lakes had decreased from 91,402.06 km 2 to 46,049.23 km 2 during 1975–2007, accounting for 49.62% of their original area of 1975. Tail-end lakes in flat areas had shrunk dramatically as they were induced by both climate changes and human impacts, while alpine lakes remained relatively stable due to the small precipitation variations. With different water usage of river outlets, the variations of open lakes were more flexible than those of other two types. According to comprehensive analyses, different types of inland lakes presented different trends of area changes under the background of global warming effects in Central Asia, which showed that the increased human activities had broken the balance of water cycles in this region.

Background Nutrient-containing nanomaterials have been developed as fertilizers to foster plant growth and agricultural yield through root applications. However, if applied through leaves, how these nanomaterials, e.g. γ-Fe 2 O 3 nanoparticles (NPs), influence the plant growth and health are largely unknown. This study is aimed to assess the effects of foliar-applied γ-Fe 2 O 3 NPs and their ionic counterparts on plant physiology of Citrus maxima and the associated mechanisms. Results No significant changes of chlorophyll content and root activity were observed upon the exposure of 20–100 mg/L γ-Fe 2 O 3 NPs and Fe 3+ . In C. maxima roots, no oxidative stress occurred under all Fe treatments. In the shoots, 20 and 50 mg/L γ-Fe 2 O 3 NPs did not induce oxidative stress while 100 mg/L γ-Fe 2 O 3 NPs did. Furthermore, there was a positive correlation between the dosages of γ-Fe 2 O 3 NPs and Fe 3+ and iron accumulation in shoots. However, the accumulated iron in shoots was not translocated down to roots. We observed down-regulation of ferric-chelate reductase (FRO2) gene expression exposed to γ-Fe 2 O 3 NPs and Fe 3+ treatments. The gene expression of a Fe 2+ transporter, Nramp3, was down regulated as well under γ-Fe 2 O 3 NPs exposure. Although 100 mg/L γ-Fe 2 O 3 NPs and 20–100 mg/L Fe 3+ led to higher wax content, genes associated with wax formation (WIN1) and transport (ABCG12) were downregulated or unchanged compared to the control. Conclusions Our results showed that both γ-Fe 2 O 3 NPs and Fe 3+ exposure via foliar spray had an inconsequential effect on plant growth, but γ-Fe 2 O 3 NPs can reduce nutrient loss due to their the strong adsorption ability. C. maxima plants exposed to γ-Fe 2 O 3 NPs and Fe 3+ were in iron-replete status. Moreover, the biosynthesis and transport of wax is a collaborative and multigene controlled process. This study compared the various effects of γ-Fe 2 O 3 NPs, Fe 3+ and Fe chelate and exhibited the advantages of NPs as a foliar fertilizer, laying the foundation for the future applications of nutrient-containing nanomaterials in agriculture and horticulture. Graphical abstract γ-Fe 2 O 3 NPs exposed on plants via foliar spray and genes associated with the absorption and transformation of iron, as well as wax synthesis and secretion in Citrus maxima leaves

At present, deep-learning methods have been widely used in road extraction from remote-sensing images and have effectively improved the accuracy of road extraction. However, these methods are still affected by the loss of spatial features and the lack of global context information. To solve these problems, we propose a new network for road extraction, the coord-dense-global (CDG) model, built on three parts: a coordconv module by putting coordinate information into feature maps aimed at reducing the loss of spatial information and strengthening road boundaries, an improved dense convolutional network (DenseNet) that could make full use of multiple features through own dense blocks, and a global attention module designed to highlight high-level information and improve category classification by using pooling operation to introduce global information. When tested on a complex road dataset from Massachusetts, USA, CDG achieved clearly superior performance to contemporary networks such as DeepLabV3+, U-net, and D-LinkNet. For example, its mean IoU (intersection of the prediction and ground truth regions over their union) and mean F1 score (evaluation metric for the harmonic mean of the precision and recall metrics) were 61.90% and 76.10%, respectively, which were 1.19% and 0.95% higher than the results of D-LinkNet (the winner of a road-extraction contest). In addition, CDG was also superior to the other three models in solving the problem of tree occlusion. Finally, in universality research with the Gaofen-2 satellite dataset, the CDG model also performed well at extracting the road network in the test maps of Hefei and Tianjin, China.

The overconsumption of fossil energy sources has resulted in serious environmental impacts and an ensuing energy crisis. Therefore, the search for a new alternative energy technology has become a focus of attention. The long-established Fischer–Tropsch synthesis technology and the recent CO2 hydrogenation technology with unlimited potential seem to be among the ways to solve the above problems. Among them, the development of efficient Fe-based catalysts has become a key issue. Weaker interactions on carbon supports are more favourable for the formation of active phases in Fe-based catalysts than stronger metal–support interactions on conventional oxide supports. In this work, we systematically summarise the application of various types of carbon materials (carbon nanotubes, mesoporous carbon, graphene, activated carbon, etc.) in COx hydrogenation reactions. The effects of different structural types of carriers on the dispersion of active sites are discussed. At the same time, the effects of different carrier preparation methods on catalytic performance are compared. In addition, the role of surface modifications to carbon materials in the promotion of active sites is discussed. Finally, we propose possible research directions based on the current problems in these catalytic systems. The aim is to provide a reference for the development of new carbon materials and their application in COx hydrogenation.

The controllability of complex networks is a core issue in network research. Assessing the controllability robustness of networks under destructive attacks holds significant practical importance. This paper studies the controllability of networks from the perspective of malicious attacks. A novel attack model is proposed to evaluate and challenge network controllability. This method disrupts network controllability with high precision by identifying and targeting critical candidate nodes. The model is compared with traditional attack methods, including degree-based, betweenness-based, closeness-based, pagerank-based, and hierarchical attacks. Results show that the model outperforms these methods in both disruption effectiveness and computational efficiency. Extensive experiments on both synthetic and real-world networks validate the superior performance of this approach. This study provides valuable insights for identifying key nodes crucial for maintaining network controllability. It also offers a solid framework for enhancing network resilience against malicious attacks.