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Background/Aims: Cholangiocarcinoma (CCA) is a primary malignant neoplasm with an extremely poor prognosis. While combined chemoradiotherapy has been demonstrated to delay CCA progression to a certain extent, the absence of specific molecular biomarkers or targets significantly hinders the diagnosis and treatment of CCA.Methods: Through cross-analysis of proteomics and ADMA modificationomics, we identified DDX1 overexpressed in CCA with elevated R602-ADMA modifications. HPLC-MS/MS identified PRMT1 as the methyltransferase and USP10 as the deubiquitinating enzyme for DDX1. Immunofluorescence and nuclear-cytoplasmic partitioning experiments confirmed DDX1’s nuclear localization. GO and KEGG analyses clarify the biological functions of DDX1 in response to hypoxia. RNA-seq transcriptomics analyzed key pathways influenced by DDX1. A hydrodynamic in situ CCA mouse model was established to validate the chemopreventive effects of the PRMT1-specific inhibitor GSK715 on CCA development.Results: DDX1 promotes CCA progression both in vivo and in vitro and can be inhibited by GSK715. Mechanistically, PRMT1 mediates ADMA modification at position R602 of DDX1. This modification promotes DDX1 nuclear localization by recruiting USP10 to deubiquitinate DDX1, while simultaneously inhibiting PRMT1 degradation. DDX1 promotes the transcription of PRMT1 and USP10 by binding to the mRNA 3’UTR region, establishing a positive feedback regulatory pathway. This mechanism promotes the occurrence and development of CCA and can serve as a target for the inhibitor GSK715 to suppress CCA progression.Conclusions: Our study identified DDX1-R602-ADMA modification as a novel ADMA modification in CCA. It further confirmed its pivotal role in CCA progression. Targeting the USP10-PRMT1-DDX1 axis may represent a significant therapeutic approach for CCA.

Background/Aims: Cholangiocarcinoma (CCA) is a primary malignant neoplasm with an extremely poor prognosis. While combined chemoradiotherapy has been demonstrated to delay CCA progression to a certain extent, the absence of specific molecular biomarkers or targets significantly hinders the diagnosis and treatment of CCA. Methods: Through cross-analysis of proteomics and ADMA modificationomics, we identified DDX1 overexpressed in CCA with elevated R602-ADMA modifications. HPLC-MS/MS identified PRMT1 as the methyltransferase and USP10 as the deubiquitinating enzyme for DDX1. Immunofluorescence and nuclear-cytoplasmic partitioning experiments confirmed DDX1’s nuclear localization. GO and KEGG analyses clarify the biological functions of DDX1 in response to hypoxia. RNA-seq transcriptomics analyzed key pathways influenced by DDX1. A hydrodynamic in situ CCA mouse model was established to validate the chemopreventive effects of the PRMT1-specific inhibitor GSK715 on CCA development. Results: DDX1 promotes CCA progression both in vivo and in vitro and can be inhibited by GSK715. Mechanistically, PRMT1 mediates ADMA modification at position R602 of DDX1. This modification promotes DDX1 nuclear localization by recruiting USP10 to deubiquitinate DDX1, while simultaneously inhibiting PRMT1 degradation. DDX1 promotes the transcription of PRMT1 and USP10 by binding to the mRNA 3’UTR region, establishing a positive feedback regulatory pathway. This mechanism promotes the occurrence and development of CCA and can serve as a target for the inhibitor GSK715 to suppress CCA progression. Conclusions: Our study identified DDX1-R602-ADMA modification as a novel ADMA modification in CCA. It further confirmed its pivotal role in CCA progression. Targeting the USP10-PRMT1-DDX1 axis may represent a significant therapeutic approach for CCA. (Clin Mol Hepatol 2026;32:843-865)

Precipitation scavenging of aerosol particles is an important removal process in the atmosphere that can change aerosol physical and optical properties. This paper analyzes the changes in aerosol physical and optical properties before and after four rain events using in situ observations of mass concentration, number concentration, particle size distribution, scattering and absorption coefficients of aerosols in June and July 2013 at the Xianghe comprehensive atmospheric observation station in China. The results show the effect of rain scavenging is related to the rain intensity and duration, the wind speed and direction. During the rain events, the temporal variation of aerosol number concentration was consistent with the variation in mass concentration, but their size-resolved scavenging ratios were different. After the rain events, the increase in aerosol mass concentration began with an increase in particles with diameter ＆lt;0.8 μm [measured using an aerodynamic particle sizer（APS）], and fine particles with diameter ＆lt;0.1 μm [measured using a scanning mobility particle sizer（SMPS）]. Rainfall was most efficient at removing particles with diameter ～0.6 μm and greater than 3.5 μm. The changes in peak values of the particle number distribution（measured using the SMPS） before and after the rain events reflect the strong scavenging effect on particles within the 100–120 nm size range. The variation patterns of aerosol scattering and absorption coefficients before and after the rain events were similar, but their scavenging ratios differed, which may have been related to the aerosol particle size distribution and chemical composition.

Al2O3/SiO2 multilayer high-reflective（HR） mirrors at 355 nm were prepared by electron beam evaporation, and post-irradiated with Ar/O mixture plasma. The surface defect density, reflective spectra, and laser-induced damage characteristics were measured using optical microscopy, spectrophotometry, a damage testing system, and scanning electron microscopy（SEM）, respectively. The results indicated that moderate-time of irradiation enhanced the laser-induced damage threshold（LIDT） of the mirror, but prolonged irradiation produced surface defects, resulting in LIDT degradation. LIDT of the mirrors initially increased and subsequently decreased with the plasma processing time. SEM damage morphologies of the mirrors revealed that nanoscale absorbing defects in sub-layers was one of the key factors limiting the improvement of LIDT in 355 nm HR mirror.

Developing geographical indication agricultural products will help to expand regional characteristic industries by taking actions that suit local circumstances. Improving the adaptability of the kiwifruit farmers to cope with meteorological disasters is conducive to promoting the optimization of rural industrial structure and the implementation of rural revitalization strategy. Based on the field survey data of Shaanxi Province, this research uses the method of natural breaks to classify the resilience scores of meteorological disasters under the framework of Sustainable Livelihoods Approach. Finally, the ordinal logistic regression model is used to quantitatively research how livelihood capital contributes to the resilience of kiwifruit farmers to meteorological disasters during the phenological phases. The results show that the perception of meteorological disasters by farmers does not significantly affect their resilience, and the impacts of different livelihood capitals on the resilience strategies of farmers are quite distinct: physical capital, financial capital, social capital, and human capital have significant positive impact on the resilience strategies of farmers, while natural capital has a significant negative impact on the resilience strategies of farmers. The results extend the theoretical foundation of resilience strategies for meteorological disasters in kiwifruit phenological phases and bring quantitative evidence linkage of livelihood capital and resilience strategies. Furthermore, the study emphasizes that the agricultural activities of kiwifruit farmers during the phenological phases should be combined with the livelihood capital guarantee measures, as well as a better financial environment should be created by government intervention. Paying attention to science popularization work of middle-aged and elderly farmers, accelerating the linkages between the government and the mass, would help the government to obtain the best agricultural management methods.

The China–Pakistan Karakoram Highway is an important land route from China to South Asia and the Middle East via Pakistan. Due to the extremely hazardous geological environment around the highway, landslides, debris flows, collapses, and subsidence are frequent. Among them, debris flows are one of the most serious geological hazards on the Karakoram Highway, and they often cause interruptions to traffic and casualties. Therefore, the development of debris flow susceptibility mapping along the highway can potentially facilitate its safe operation. In this study, we used remote sensing, GIS, and machine learning techniques to map debris flow susceptibility along the Karakoram Highway in areas where observation data are scarce and difficult to obtain by field survey. First, the distribution of 544 catchments which are prone to debris flow were identified through visual interpretation of remote sensing images. The factors influencing debris flow susceptibility were then analyzed, and a total of 17 parameters related to geomorphology, soil materials, and triggering conditions were selected. Model training was based on multiple common machine learning methods, including Ensemble Methods, Gaussian Processes, Generalized Linear models, Navies Bayes, Nearest Neighbors, Support Vector Machines, Trees, Discriminant Analysis, and eXtreme Gradient Boosting. Support Vector Classification (SVC) was chosen as the final model after evaluation; its accuracy (ACC) was 0.91, and the area under the ROC curve (AUC) was 0.96. Among the factors involved in SVC, the Melton Ratio (MR) was the most important, followed by drainage density (DD), Hypsometric Integral (HI), and average slope (AS), indicating that geomorphic conditions play an important role in predicting debris flow susceptibility in the study area. SVC was used to map debris flow susceptibility in the study area, and the results will potentially facilitate the safe operation of the highway.

With climate change, the Qinghai–Tibet Highway (QTH) is facing increasingly severe risks of natural hazards, posing a significant threat to its normal operation. However, the types, distribution, and future risks of hazards along the QTH are still unclear. In this study, we established an inventory of multi-hazards along the QTH by remote sensing interpretation and field validation, including landslides, debris flows, thaw slumps, and thermokarst lakes. The QTH was segmented into three sections based on hazard distribution and environmental factors. Susceptibility modelling was performed for each hazard within each section using machine learning models, followed by further evaluation of hazard susceptibility under future climate change scenarios. The results show that, at present, approximately 15.50% of the area along the QTH exhibits high susceptibility to multi-hazards, with this proportion projected to increase to 20.85% and 23.32% under the representative concentration pathways (RCP) 4.5 and RCP 8.5 distant future scenarios, respectively. Variations in hazard-prone environments dominate the spatial heterogeneity of multi-hazard distribution. Gravity hazards demonstrate limited sensitivity to climate change, whereas thermal hazards exhibit a more pronounced response. Our geomorphology-based segmented assessment framework effectively enhances evaluation accuracy and model interpretability. The results can provide critical insights for the operation, maintenance, and hazard risk management of the QTH.

Groups of landslides induced by heavy rainfall are widely distributed on a global basis and they usually result in major losses of human life and economic damage. However, compared with landslides induced by earthquakes, inventories of landslides induced by heavy rainfall are much less common. In this study we used high-precision remote sensing images before and after continuous heavy rainfall in southern Tianshui, China, from 20 June to 25 July 2013, to produce an inventory of 14,397 shallow landslides. Based on the results of landslide inventory, we utilized machine learning and the geographic information system (GIS) to map landslide susceptibility in this area and evaluated the relative weight of various factors affecting landslide development. First, 18 variables related to geomorphic conditions, slope material, geological conditions, and human activities were selected through collinearity analysis; second, 21 selected machine learning models were trained and optimized in the Python environment to evaluate the susceptibility of landslides. The results showed that the ExtraTrees model was the most effective for landslide susceptibility assessment, with an accuracy of 0.91. This predictive ability means that our landslide susceptibility results can be used in the implementation of landslide prevention and mitigation measures in the region. Analysis of the importance of the factors showed that the contribution of slope aspect (SA) was significantly higher than that of the other factors, followed by planar curvature (PLC), distance to river (DR), distance to fault (DTF), normalized difference vehicle index (NDVI), distance to road (DTR), and other factors. We conclude that factors related to geomorphic conditions are principally responsible for controlling landslide susceptibility in the study area.

The eastern margin of the Tibetan Plateau is one of the regions with the most severe landslide disasters on a global scale. With the intensification of seismic activity around the Tibetan Plateau and the increase in extreme rainfall events, the prevention of landslide disasters in the region is facing serious challenges. This article selects the Bailong River Basin located in this region as the research area, and the historical landslide data obtained from high-precision remote sensing image interpretation combined with field validation are used as the sample library. Using machine learning algorithms and data-driven landslide susceptibility assessment as the methods, 17 commonly used models and 17 important factors affecting the development of landslides are selected to carry out the susceptibility assessment. The results show that the BaggingClassifier model shows advantageous applicability in the region, and the landslide susceptibility distribution map of the Bailong River Basin was generated using this model. The results show that the road and population density are both high in very high and high susceptible areas, indicating that there is still a significant potential landslide risk in the basin. The quantitative evaluation of the main influencing factors emphasizes that distance to a road is the most important factor. However, due to the widespread utilization of ancient landslides by local residents for settlement and agricultural cultivation over hundreds of years, the vast majority of landslides are likely to have occurred prior to human settlement. Therefore, the importance of this factor may be overestimated, and the evaluation of the factors still needs to be dynamically examined in conjunction with the development history of the region. The five factors of NDVI, altitude, faults, average annual rainfall, and rivers have a secondary impact on landslide susceptibility. The research results have important significance for the susceptibility assessment of landslides in the complex environment of human–land interaction and for the construction of landslide disaster monitoring and early warning systems in the Bailong River Basin.

Debris flow risk comprehensively reflects the natural and social properties of debris flow disasters and is composed of the risk of the disaster-causing body and the vulnerability of the carrier. The Bailong River Basin (BRB) is a typical mountainous environment where regional debris flow disasters occur frequently, seriously threatening the lives of residents, infrastructure, and regional ecological security. However, there are few studies on the risk assessment of mountainous debris flow disasters in the BRB. By considering a complete catchment, based on remote sensing and GIS methods, we selected 17 influencing factors, such as area, average slope, lithology, NPP, average annual precipitation, landslide density, river density, fault density, etc. and applied a machine learning algorithm to establish a hazard assessment model. The analysis shows that the Extra Trees model is the most effective for debris flow hazard assessments, with an accuracy rate of 88%. Based on socio-economic data and debris flow disaster survey data, we established a vulnerability assessment model by applying the Contributing Weight Superposition method. We used the product of debris flow hazard and vulnerability to construct a debris flow risk assessment model. The catchments at a very high-risk were distributed mainly in the urban area of Wudu District and the northern part of Tanchang County, that is, areas with relatively dense economic activities and a high disaster frequency. These findings indicate that the assessment results provide scientific support for planning measures to prevent or reduce debris flow hazards. The proposed assessment methods can also be used to provide relevant guidance for a regional risk assessment of debris flows in the BRB and other regions.