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Global warming has shown an “Arctic amplification effect” in recent decades, leading to pronounced changes in pan-Arctic soil surface temperature (SST). SST plays a direct role in energy exchange between soil and atmosphere and serves as an indicator of the land–atmosphere energy balance. Remote sensing land surface temperature (LST) data is able to indicate near-surface temperature, but influences from environment factors, such as vegetation and snow, can introduce biases between LST and SST. In this study, the importances of five environment factors (vegetation, snow, surface soil composition, topography, and solar radiation) to monthly mean SST estimation from MODIS LST in pan-Arctic were analyzed. Then a method for pan-Arctic monthly mean SST estimation from MODIS LST by incorporating these environment factors and monthly-based modeling based on random forest (RF) algorithm was proposed. The results reveal that all the selected environment factors contribute to monthly-based modeling, with vegetation exerting the greatest importance from May to October and snow in March and April. The root mean square error (RMSE) of pan-Arctic monthly SST estimated by the proposed method from 2003 to 2022 ranges from 0.89 to 1.88 °C, which is a 42.95–––53.35 % reduction compared to the widely used season-based multivariate linear regression (MLR) models based solely on LST (RMSE between 1.56 and 4.03 °C). The accuracy is notably improved in areas with lower and no vegetation (grassy woodlands, grasslands, permanent wetlands, and barrens) in the cold season (September to the following April), and in higher vegetation (forests) areas in the warm season (May to August). The proposed method can contribute to producing high-precision monthly mean SST data from LST, estimating permafrost extent and active layer thickness, and understanding the land–atmosphere energy balance in pan-Arctic.

Leaf area index (LAI) serves as a pivotal parameter in crop monitoring, significantly impacting agricultural applications. Empirical models are one of the commonly used methods for estimating LAI, they are often dependent on vegetation indices (VIs), predominantly derived from low-to-moderate spatial resolution satellite sensors. A critical limitation of these VIs is their tendency to saturate at elevated LAI values. Additionally, the interplay between chlorophyll content (Cab), LAI, as well as average leaf inclination angle (ALA), particularly in reflectance spectra from red to red-edge regions, has been underexplored in past research. Based on Sentinel-2 satellite, with three red-edge bands and enhanced spatio-temporal resolution, this study introduced a new three red-edge vegetation index (VI3RE), comprising NDVI3RE and CI3RE, which leverages the unique spectral characteristics of these bands and related differential response to LAI and Cab variations. We investigated VI3RE’s efficacy through a threefold approach: firstly, by conducting sensitivity analyses using noise equivalent (NE)ΔLAI ((NE) ΔLAI=1.37 and 1.59 for NDVI3RE and CI3RE; (NE)ΔLAI = 1.75 – 3.25 for other VIs) and extended Fourier amplitude sensitivity test (EFAST) (the contribution of LAI: FOI > 30 % and TOI > 40 % for VI3RE; FOI ∼ 17 % − 28 % and TOI ∼ 25 % − 34 % for other VIs, except for NDVI with TOI ∼ 50 %), we demonstrated VI3RE’s heightened sensitivity to LAI and its improved capability for seasonal LAI estimation compared to other VIs. Secondly, by comprehensively considering the simplicity, interpretability, AIC and R2 values of various models, the linear regression model was selected for analyzing the relationships between LAI and various VIs. We established that VI3RE, particularly NDVI3RE, exhibits a robust correlation with LAI, thereby enhancing LAI estimation accuracy (R2 = 0.72, RMSE = 1.39). Lastly, we applied the LAI-VI regression models to generate crop LAI imageries throughout the growing season, subsequently validating by field measured LAI data. The results affirmed VI3RE’s superior performance in seasonal LAI estimation of crops, notably during peak growth and sowing phases. We conclude that the newly formulated VI3RE offers a universal, highly precise, and reliable model for LAI prediction across various crop types and phenological stages. However, the broader application of VI3RE may be limited, due to the three red-edge bands are not commonly found in most satellite platforms. In addition, some other aspects are not considered in this study, such as phenological information, meteorological factors, sampling strategies, etc., which should be meticulously considered in the future studies.

Texture is a key spatial feature for object recognition in remote sensing images. Currently, most texture feature extraction methods mainly focus on the repeated patterns of texture primitives (the basic texture units) but rarely consider their spatial arrangement. Although some methods can capture the spatial arrangement of texture primitives to some extent, their principles and algorithms are complex and difficult to implement. In this study, we proposed a new statistical feature extraction method, called distance coding diversity (DCD), which can measure the spatial arrangement of texture primitives with a rotation invariant characteristic. The texture features extracted by DCD were widely tested on a large dataset at three different scales: 50 digital matrix samples at the pixel neighborhood scale, 2508 pairs of small block images of pre- and post-damaged buildings at the image object scale, and five large-scale images covering different main land cover types (cropland, buildings, plantations, natural vegetation and abandoned dumps) at the regional landscape scale, and the test results were compared with Entropy of Grey-level Co-occurrence Matrix (GLCM) and Short Run Emphasis (SRE) of Grey Level Run Length Matrix (GLRLM). DCD can effectively measure the spatial arrangement of texture primitives, and its sensitivity to spatial arrangement is 98%, which is more than 5 times that of SRE (with a sensitivity value of 18%). The identification accuracy for different land cover types based on DCD improved by 12.31% over Entropy, and 14.40% over SRE. Due to the simple algorithm and superior performance, DCD can be widely used for object recognition and land cover/use classification and has wide application prospects.

Abstract Temperature sensitivity (TS) of the green‐up date (GUD) of plants is crucial for the prediction of grassland phenology that is important for animal husbandry and pasture management. Spatial variations in the TS are known to reflect interannual temperature variability and/or accumulated precipitation preceding the GUD (pre‐GUD). However, whether spatial TS variations are related to the interaction between pre‐GUD temperature variability and precipitation, which is a potential indicator of frost risk, remains unclear. Furthermore, because the interaction between interannual temperature variability and accumulated precipitation following the GUD (post‐GUD) can exert selection pressure on the plant life cycle, it may also be involved in shaping the spatial TS pattern. Using long‐term ground observations of GUD on the Tibetan Plateau, we show that TS is more negative (greater GUD advance per unit temperature increase) in areas with more pre‐GUD precipitation and low pre‐GUD interannual temperature variability, but less negative in areas with more pre‐GUD precipitation and high pre‐GUD interannual temperature variability. This result is likely because more pre‐GUD precipitation facilitates sprout and leaf development under stable temperature conditions, whereas it increases frost risk when the temperature variability is high. In contrast, TS magnitude decreases with increases in post‐GUD precipitation in areas where post‐GUD interannual temperature variability is low, but increases with post‐GUD precipitation in areas where post‐GUD interannual temperature variability is high. We speculate that because hydrothermal demands for leaf growth from the onset of green‐up to maturity are more easily fulfilled when interannual temperature variability is lower and precipitation is higher, green‐up need not be sensitive to pre‐GUD temperature. In contrast, high post‐GUD precipitation likely aggravates low‐temperature constraints on leaf growth when temperature variability is high, resulting in greater TS to maximize growing season length. These results suggest that spatial TS variations on the Tibetan Plateau are likely associated with adaptations of leaf‐out phenology to background pre‐GUD climatic conditions together with selection pressure from post‐GUD conditions.

AbstractTo improve the anti-metastasis effects of honokiol (HNK) on breast cancer, we designed cationic liposomes (Lip) in which HNK was encapsulated into Lip, and its surface was modified with negatively charged polysialic acid (PSA-Lip-HNK) for efficient treatment of breast cancer. PSA-Lip-HNK possessed a homogeneous spherical shape and high encapsulation efficiency. In vitro 4T1 cell experiments indicated that PSA-Lip-HNK increased cellular uptake and cytotoxicity via the endocytosis pathway mediated by PSA and selectin receptors. Furthermore, the significant antitumor metastasis impact of PSA-Lip-HNK was confirmed by wound healing and cell migration and invasion. Enhanced in vivo tumor accumulation of the PSA-Lip-HNK was observed in 4T1 tumor-bearing mice by living fluorescence imaging. For in vivo antitumor experiments using 4T1 tumor-bearing mice, PSA-Lip-HNK exhibited a higher tumor growth and metastasis inhibition compared with unmodified liposomes. Therefore, we believe that PSA-Lip-HNK well combined biocompatible PSA nano-delivery and chemotherapy, providing a promising drug delivery approach for metastatic breast cancer therapy.

Most of the existing remote sensing-based yield estimation methods adopt the mean or cumulative value of meteorological factors within the whole growing season, which may ignore the impact of adverse meteorological conditions on the growth of winter wheat in a certain phenological period. In this study, we distinguished the developmental progression of winter wheat as three phenological periods. In each phenological period, the vegetation indices and meteorological factors were optimized. Then the accuracy and spatiotemporal transferability of the phenological piecewise modelling was compared with that of the whole-season modelling based on four regression methods (i.e. multiple linear regression, artificial neural network, support vector regression and random forest). The results showed that the optimal combinations of variables for the whole-season modelling and the phenological piecewise modelling were different. Compared with the whole-season models, the R2 for the phenological piecewise models improved by 1.4% to 7.6%, the root mean square error (RMSE) decreased by 1.1% to 8.2% among four regression methods . In addition, compared with the whole-season models, the spatiotemporal transferability for the phenological piecewise models was generally better. The accuracies after spatiotemporal transfer for the phenological piecewise models were still higher than that for the whole-season models.

Currently, the obvious side effects of anti-tumor drugs, premature drug release, and low tumor penetration of nanoparticles have largely reduced the therapeutic effects of chemotherapy. A drug delivery vehicle (MCN-SS-GQDs) was designed innovatively. For this, the mesoporous carbon nanoparticles (MCN) with the capabilities of superior photothermal conversion efficiency and high loading efficiency were used as the skeleton structure, and graphene quantum dots (GQDs) were gated on the mesopores via disulfide bonds. The doxorubicin (DOX) was used to evaluate the pH-, GSH-, and NIR-responsive release performances of DOX/MCN-SS-GQDs. The disulfide bonds of MCN-SS-GQDs can be ruptured under high glutathione concentration in the tumor microenvironment, inducing the responsive release of DOX and the detachment of GQDs. The local temperature of a tumor increases significantly through the photothermal conversion of double carbon materials (MCN and GQDs) under near-infrared light irradiation. Local hyperthermia can promote tumor cell apoptosis, accelerate the release of drugs, and increase the sensitivity of tumor cells to chemotherapy, thus increasing treatment effect. At the same time, the detached GQDs can take advantage of their extremely small size (5–10 nm) to penetrate deeply into tumor tissues, solving the problem of low permeability of traditional nanoparticles. By utilizing the photothermal properties of GQDs, synergistic photothermal conversion between GQDs and MCN was realized for the purpose of synergistic photothermal treatment of superficial and deep tumor tissues.

The accurate detection of collapsed buildings is of great significance for post-disaster rescue and reconstruction. High-resolution optical images are important data sources for identifying collapsed buildings, and the identification accuracy mainly depends on the features extracted from the images. However, existing research lacks a comprehensive screening and general evaluation of the ability of remote sensing features to detect collapsed buildings, and there is still a considerable gap in the operational process of rapid identification of collapsed buildings in remote sensing. Based on 2630 pairs of building samples distributed in 6 regions worldwide, this study evaluated the ability of 25 remote sensing features (including spectral and spatial features) to detect collapsed buildings and select the most capable ones. Then, we test the application effect of selected features in identifying collapsed buildings on large-scale remote sensing images. Based on the two experiments above, an operational process for rapid identification of collapsed buildings was suggested. The result shows that Homogeneity, Energy, Local Entropy, Local Standard Deviation, and Gradient can effectively and stably distinguish collapsed buildings from non-collapsed buildings (Jeffries-Matusita distances are greater than 1.59 and Transformed Divergences are greater than 1.60) and have high recognition accuracy for collapsed buildings on large-scale remote sensing images (F1-scores are 0.71–0.94). In addition, Contrast, Local Coefficient of Variation, Edge Density, and Global Entropy can also distinguish collapsed buildings from non-collapsed buildings at a normal level (Jeffries-Matusita distances are 1.14–1.28, and Transformed Divergences are 1.24–1.48), while Gradient Orientation Entropy, Fractal Dimension, Local Binary Patterns, Edge, Local Mean, Correlation, Gradient Orientation Standard Deviation, Global Coefficient of Variation, Gabor feature, Local Moran’I, and six spectral features have relatively weak abilities (Jeffries-Matusita distances are less than 0.73, and Transformed Divergences are less than 1.07). The selected remote sensing features can support rapid identification of potential collapsed building areas from post-disaster remote sensing images.

The light use efficiency (LUE) model has been widely used in regional and global terrestrial gross primary productivity (GPP) estimation due to its simple structure, few input parameters, and particular theoretical basis. As a key input parameter of the LUE model, the maximum LUE (Ɛmax) is crucial for the accurate estimation of GPP and to the interpretability of the LUE model. Currently, most studies have assumed Ɛmax as a universal constant or constants depending on vegetation type, which means that the spatiotemporal dynamics of Ɛmax were ignored, leading to obvious uncertainties in LUE-based GPP estimation. Using quality-screened daily data from the FLUXNET 2015 dataset, this paper proposed a photosynthetically active radiation (PAR)-regulated dynamic Ɛmax (PAR-Ɛmax, corresponding model named PAR-LUE) by considering the nonlinear response of vegetation photosynthesis to solar radiation. The PAR-LUE was compared with static Ɛmax-based (MODIS and EC-LUE) and spatial dynamics Ɛmax-based (D-VPM) models at 171 flux sites. Validation results showed that (1) R2 and RMSE between PAR-LUE GPP and observed GPP were 0.65 (0.44) and 2.55 (1.82) g C m−2 MJ−1 d−1 at the 8-day (annual) scale, respectively; (2) GPP estimation accuracy of PAR-LUE was higher than that of other LUE-based models (MODIS, EC-LUE, and D-VPM), specifically, R2 increased by 29.41%, 2.33%, and 12.82%, and RMSE decreased by 0.36, 0.14, and 0.34 g C m−2 MJ−1 d−1 at the annual scale; and (3) specifically, compared to the static Ɛmax-based model (MODIS and EC-LUE), PAR-LUE effectively relieved the underestimation of high GPP. Overall, the newly developed PAR-Ɛmax provided an estimation method utilizing a spatiotemporal dynamic Ɛmax, which effectively reduced the uncertainty of GPP estimation and provided a new option for the optimization of Ɛmax in the LUE model.

Global warming has led to earlier spring green-up dates (GUDs) in recent decades with significant consequences for global carbon and hydrologic cycles. In addition to changes in climate, land cover change (LCC), including interchanges between vegetation and non-vegetation, and among plants with different functional traits, may also affect GUD. Here, we analyzed how satellite-derived GUD from 1992 to 2020 was impacted by changes in temperature, precipitation, standardized precipitation evapotranspiration index (SPEI), solar radiation, and LCC for the Northern Hemisphere (>30° N). While the climate variables had larger impact overall, variability in GUD was controlled by LCC for 6% of the Northern Hemisphere, with systematically earlier or later changes among transitions between different land cover types. These changes were found mainly along the southeastern coast of the United States, in Central-north Europe, and across northeastern China. We further showed that climate change attribution of earlier GUD during 1992–2020 was overestimated by three days when the impact of LCC was ignored. Our results deepen the understanding of how LCC impacts GUD variability and enables scientists to more accurately evaluate the impact of climate change on land surface phenology.

Vegetation phenology reflects the temporal dynamics of vegetation growth and is an important indicator of climate change. However, differences consistently exist in land surface phenology derived at different spatial scales, which hinders the understanding of phenological events and integration of land surface phenology products from different scales. The Qinling Mountains are a climatic and geographical transitional region in China. To better understand the spatial scale effect issues of land surface phenology in mountainous ecosystems, this study up-scaled vegetation start of season (SOS) and end of season (EOS) in the Qinling Mountains derived from three different Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) products to four scales (i.e., 2 km × 2 km, 4 km × 4 km, 6 km × 6 km, and 8 km × 8 km) using the spatial averaging method. Then, similarities and differences between the up-scaled SOSs/EOSs were examined using the simple linear regression, cumulative distribution function, and absolute difference. Finally, the random forest model was used to reveal the major factors influencing the spatial scale effect of land surface phenology in Qinling Mountains. Results showed that the derived basic SOS/EOS datasets using the same filtering method from the 250 m and 500 m NDVI datasets were consistent in spatial distribution, while the results from the 1000 m NDVI dataset differed. For both the basic and the up-scaled datasets, the land surface phenology derived from the Savitzky-Golay-filtered NDVI showed an advance in SOS, but a delay in EOS, compared to those derived from the asymmetric Gaussian- and double logistic-filtered NDVI. The up-scaled SOS was greatly impacted by both NDVI resolution and the filtering methods. On the other hand, EOS was mostly impacted by the filtering methods. Moreover, up-scaled SOSs usually had larger differences compared to up-scaled EOSs. While different filtering methods sometimes amplified the absolute differences between different SOS/EOS across scales, the upscaling reduced the differences. Influence factor analysis showed that spatial variations observed in SOS in Qinling Mountains were mainly caused by forest cover, uneven distribution of spring precipitation, and annual precipitation, while spatial variations in aspect, winter temperature, and autumn precipitation all strongly influenced the observed EOS across scales in the study area. These findings enhance our understanding of the effects of observational scale on vegetation phenology in mountain ecosystems and provide a reference for phenology modeling in mountainous areas.

Pollen allergies have a serious impact on people’s physical and mental health. Accurate and efficient prediction of the outbreak date of pollen allergies plays an important role in the conservation of people sensitive to allergenic pollen. It is a frontier research to combine new social media data and satellite data to develop a model to forecast the outbreak date of pollen allergies. This study extracted the real outbreak dates of spring pollen allergies from Sina Weibo records from 2011 to 2021 in Beijing and calculated five vegetation indices of three vegetation types as phenological characteristics within the 30 days before the average outbreak date. The sensitivity coefficients and correlation coefficients were used to screen the phenological characteristics that best reflected the outbreak date of spring pollen allergy. Based on the best characteristic, two kinds of prediction models for the outbreak date of spring pollen allergy in Beijing were established (the linear fit prediction model and the cumulative linear fit prediction model), and the root mean square error (RMSE) was calculated as the prediction accuracy. The results showed that (1) the date of EVI2 (2-band enhanced vegetation index) in evergreen forest first reaching 0.138 can best reflect the outbreak date of pollen allergies in spring, and (2) the cumulative linear fit prediction model based on EVI2 in evergreen forests can obtain a high accuracy with an average RMSE of 3.6 days, which can predict the outbreak date of spring pollen allergies 30 days in advance. Compared with the existing indirect prediction models (which predict pollen concentrations rather than pollen allergies), this model provides a new direct way to predict pollen allergy outbreaks by using only remote sensing time-series data before pollen allergy outbreaks. The new prediction model also has better representativeness and operability and is capable of assisting public health management.

Vegetation index (VI) time series derived from satellite sensors have been widely used in the estimation of vegetation parameters, but the quality of VI time series is easily affected by clouds and poor atmospheric conditions. The function fitting method is a widely used effective noise reduction technique for VI time series, but it is vulnerable to noise. Thus, ancillary data about VI quality are utilized to alleviate the interference of noise. However, this approach is limited by the availability, accuracy, and application rules of ancillary data. In this paper, we aimed to develop a new reconstruction method that does not require ancillary data. Based on the assumptions that VI time series follow the gradual growth and decline pattern of vegetation dynamics, and that clouds or poor atmospheric conditions usually depress VI values, we proposed a reconstruction method for VI time series based on self-weighting function fitting from curve features (SWCF). SWCF consists of two major procedures: (1) determining a fitting weight for each VI point based on the curve features of the VI time series and (2) implementing the weighted function fitting to reconstruct the VI time series. The double logistic function, double Gaussian function, and polynomial function were tested in SWCF based on a simulated dataset. The results indicate that the weighted function fitting with SWCF outperformed the corresponding unweighted function fitting with the root-mean-square error (RMSE) significantly reduced by 26.82–52.44% (p < 0.05), and it also outperformed the Savitzky–Golay filtering with the RMSE significantly reduced by 13.98–54.04% (p < 0.05) for 270 sample points selected in mid-high latitudes of the Northern Hemisphere. Moreover, SWCF showed excellent robustness and applicability in regional applications.

The accurate evaluation of shifts in vegetation phenology is essential for understanding of vegetation responses to climate change. Remote-sensing vegetation index (VI) products with multi-day scales have been widely used for phenology trend estimation. VI composites should be interpolated into a daily scale for extracting phenological metrics, which may not fully capture daily vegetation growth, and how this process affects phenology trend estimation remains unclear. In this study, we chose 120 sites over four vegetation types in the mid-high latitudes of the northern hemisphere, and then a Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 daily surface reflectance data was used to generate a daily normalized difference vegetation index (NDVI) dataset in addition to an 8-day and a 16-day NDVI composite datasets from 2001 to 2019. Five different time interpolation methods (piecewise logistic function, asymmetric Gaussian function, polynomial curve function, linear interpolation, and spline interpolation) and three phenology extraction methods were applied to extract data from the start of the growing season and the end of the growing season. We compared the trends estimated from daily NDVI data with those from NDVI composites among (1) different interpolation methods; (2) different vegetation types; and (3) different combinations of time interpolation methods and phenology extraction methods. We also analyzed the differences between the trends estimated from the 8-day and 16-day composite datasets. Our results indicated that none of the interpolation methods had significant effects on trend estimation over all sites, but the discrepancies caused by time interpolation could not be ignored. Among vegetation types with apparent seasonal changes such as deciduous broadleaf forest, time interpolation had significant effects on phenology trend estimation but almost had no significant effects among vegetation types with weak seasonal changes such as evergreen needleleaf forests. In addition, trends that were estimated based on the same interpolation method but different extraction methods were not consistent in showing significant (insignificant) differences, implying that the selection of extraction methods also affected trend estimation. Compared with other vegetation types, there were generally fewer discrepancies between trends estimated from the 8-day and 16-day dataset in evergreen needleleaf forest and open shrubland, which indicated that the dataset with a lower temporal resolution (16-day) can be applied. These findings could be conducive for analyzing the uncertainties of monitoring vegetation phenology changes.

The water solubility and side effects of lamivudine limit its application for the treatment of viral hepatitis type B and human immunodeficiency virus. In order to increase the solubility of LA and improve the in vivo membrane permeability of the drug, LA was modified with hexadecane acid to prepare the prodrug lamivudine palmitic acid (LAP) and loaded into nanoemulsome (NES). LAP-NES was prepared by the thin film dispersion method. The LAP-NES showed the sustained release performance up to 72h in pH 7.4 PBS. Moreover, the pharmacokinetics of LAP-NES after tail vein injection in rats and the biodistribution characteristics were evaluated. The tmax of LAP-NES was 2.5h. The t1/2, clearance rate and average retention time of LAP-NES obviously prolonged compared with free LAP. The tissue biodistribution behavior of NES in vivo showed the good targeting in the liver and spleen, with the maximum at 4h and then the fluorescence slowly decreased until 72h. LAP-NES could significantly delay the release of LA in vivo, effectively prolong the elimination time, and had obvious liver-targeting ability. In summary, LAP-NES shows great potential for liver-targeting delivery to increase the therapeutic effect and decrease the side effects of LA. [ABSTRACT FROM AUTHOR]

The Longgang volcanic field is one of the most active volcanoes in modern China. It is situated in the central region of the Longgang mountains on the west slopes of Changbaishan in Jilin. The present three-dimensional crustal movement velocity fields in the different periods were obtained by processing Global Navigation Satellite System (GNSS) observations from 2010 to 2020 and data for the leveling profiles from 1970s to 2010s in the area. The horizontal velocities of GNSS stations were larger in the eastern region than in the western regions, indicating that the eastern region is mainly tensile. The results of the continuous surface strain rates showed that a volcanic field is located in the expansion area. The results of the first-order leveling data for the Changfu profile and the Danfu profile showed that the vertical movements were predominantly uplift, with rates of 0.55~1.83 mm/a for most of the sites. The area with relatively higher uplifting rates of more than 1.0 mm/a was Fusong-Xianrenqiao-Laoshandui, which is rich in geothermal resources and prone to earthquakes. At the adjacent area, Meitong, the rates of the second-class leveling profile were relatively small, at 0.23~0.77 mm/a. The deep electrical structure of the volcanic area was obtained through the three-dimensional inversion of 99 broadband magnetotelluric soundings. The clear structures of the low resistivity in the middle and lower crusts corresponded to the uplift area, which is suggested to be a magmatic system. The position of the magma was relatively shallow. The electrical boundary zone at the northeast end of the Hunjiang fault was speculated to be the northern extension of the fault. The low-resistivity body had the largest scale and extended downward to the mantle. The shallowest low-resistivity body was approximately 10 km below the youngest Jinlongdingzi volcano, and the above high-resistivity structure was considered to be the retreat and consolidation magma after the overflow eruption ended. A comprehensive analysis showed that the ongoing inflation, uplift, and seismic activities of the crust in the volcanic area were related to the upwelling of mantle materials and fault movements caused by intermittent magma migration.

Abstract Understanding lithospheric rheology is crucial in investigating tectonic evolution of intra‐continental tectonic boundary. Here, we use geodetic observations to infer lithospheric rheology across the northeastern Tibet based on a 2D viscoelastic model. Our findings reveal a lower‐crust viscosity of

BackgroundWe aimed to investigate the efficacy of a novel regimen, external beam radiation (RT) combined with trans arterial chemoembolization (TACE) and lenvatinib (LEN), in the treatment of hepatocellular carcinoma (HCC) with portal vein tumor thrombus.MethodsWe prospectively observed 102 participants from three tertiary medical centers in China between October 2018 and October 2020, who chose either RT plus TACE and LEN (RT-TACE-LEN) or TACE and LEN (TACE-LEN). LEN (12 mg or 8 mg daily) was administrated orally and continued until progression or intolerable side effects were noted. TACE was given one day after administration of LEN, and RT began within 4 weeks after the first TACE. The median dose/fraction of RT was 50 Gy/25 fractions (range: 45-60 Gy/25 fractions). Overall survival and progression free survival were compared between two groups, and complications were assessed.ResultsBoth 51 patients received RT-TACE-LEN and TACE-LEN, respectively. Most patients had tumor size> 5 cm (73.8%) and tumor number≥ 2 (69.9%). The overall incidence of toxicities was significantly higher in RT-TACE-LEN group than TACE-LEN group (100% vs. 64.7%, p< 0.001), but incidences of grade 3-4 toxicities were comparable (54.9% vs. 49.0%, p= 0.552). Both median overall survival (22.8 vs. 17.1 months, p= 0.031) and median progression-free survival (12.8 vs. 10.5 months, p= 0.035) were significantly longer after RT-TACE-LEN treatment than TACE-LEN.ConclusionsThe addition of RT to TACE and LEN was safe, and might improve clinical outcomes of patients with advanced HCC, which needs conformation from further studies.

PurposeTo investigate the efficacy and safety of combined treatment of anlotinib and transarterial chemoembolization (TACE) in patients with unresectable hepatocellular carcinoma (uHCC) associated with hepatitis B virus (HBV) infection.MethodsWe retrospectively collected the data of 96 uHCC patients associated with HBV infection who received either TACE only (TO group; n = 64) or anlotinib combined with TACE (TA group; n = 32) from January 2017 to January 2021. The primary endpoint was overall survival (OS). The secondary outcomes included progression-free survival (PFS), tumor response according to modified Response Evaluation Criteria in Solid Tumors (mRECIST) and RECIST 1.1, and adverse events (AEs).ResultsThe median OS and median PFS were significantly longer in the TA group compared to the TO group (17.6 months vs. 9.4 months, p = 0.018; 6.7 months vs. 3.8 months, p = 0.003, respectively). In addition, the overall objective response rate (ORR) and disease control rate (DCR) numerically increased in the TA group (mRECIST, ORR 65.6% vs. 46.9%, p = 0.064, DCR 90.6% vs. 85.9%, p = 0.382; RECIST 1.1, ORR 46.9% vs. 15.6%, p = 0.001, DCR 90.6% vs. 85.9%, p = 0.382, respectively). It was worth noting that no treatment-related mortality occurred during the study. The most common AEs included elevated transaminases (56.3%), decreased appetite (46.9%), and abdominal pain (37.5%) in the TA group. Although the incidence rate of grade 3/4 AEs was higher in the TA group, all of them were controllable.ConclusionsThe combination of anlotinib and TACE has shown promising results in improving outcomes for patients with HBV-related uHCC, as compared to TACE monotherapy. In addition, this combination therapy has demonstrated a controllable safety profile. However, further validation is urgently needed through randomized controlled trials with large sample sizes.

The Sarez Dam, currently recognized as the world’s highest natural dam, holds immense economic significance, necessitating the reliable monitoring and early detection of potential deformations. This study utilizes the Beidou high-precision deformation monitoring system for the dam. Employing baseline network solutions, precise point positioning, and real-time kinematic positioning, the monitoring data from October 2021 to March 2023 were meticulously processed and comparatively analyzed. The results reveal varied degrees of displacement deformation at all sites, with horizontal deformation towards the lake center and vertical deformation showing subsidence. The three-dimensional vector average displacement is 29.1 mm, with an average monthly rate of 4.3 mm/month, and the maximum deformation is 41.4 mm. The cause of the horizontal deformation towards the lake center may be a slow collapse of the coastal soil and rock towards the lake center. Additionally, the monitoring detected a 7.2-magnitude earthquake on 23 February 2023, 52 km from the dam, causing an average displacement of 22.5 mm towards the south, which is 20 times the monthly deformation rate. In summary, deformation is present within the dam monitoring area, with a relatively stable deformation rate, warranting continued tracking and monitoring. Furthermore, earthquakes significantly impact dam deformation, necessitating heightened attention to the potential for seismic-induced dam damage in the future.

By using GPS-derived velocities of 2015–2021 and a negative dislocation program, we inverted the locking degree and slip rate deficit in the Qilian–Haiyuan fault zone, and combined with the distribution of small earthquakes in the fault, we studied the characteristics before the 2022 Menyuan MS6.9 earthquake and analyzed the future seismic hazards of each segment within this fault zone. The regional crustal deformation pattern is discussed with regard to the fault slip rate and regional strain rate field. The preliminary results show that before the earthquake, the seismogenic fault was strong locked, with a high locking depth, the slip rate deficit was large, and the distribution of small earthquakes was relatively few, these characteristics are closely related to the occurrence of strong earthquakes, according to the aftershock relocation results, further, it is believed that the earthquake may link the Lenglongling and Tuolaishan faults into a large strike-slip fault. The Jinqianghe fault, the Lenglongling fault, and the eastern segment of the Tuolaishan fault are strongly locked, with high locking depth and large slip rate deficit, combined with the occurrence of small earthquakes and the locking degree before the 2022 Menyuan MS6.9 earthquake, indicate that the eastern segment of the Tuolaishan fault is highly likely to have strong earthquakes in the future, which requires further attention. In addition, the strike-slip rate of the Qilian–Haiyuan fault zone is mainly between 3.9 and 4.3 mm/yr, the overall movement of the fault is consistent, and the compressional rate gradually decreases from 2.9 mm/yr in the western segment to 1 mm/yr in the eastern segment; the fault compressional rate may be related to the crustal shortening (formation basin and uplift mountain). Therefore, the present-day crustal deformation in the northeastern margin of the Tibetan Plateau is mainly distributed in the shortened region of the crust on the Qilian Shan area and left-lateral strike-slip localized on the Qilian–Haiyuan fault zone.