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The launch of Sentinel-1 satellite introduces a novel approach for synthetic aperture radar (SAR) interferometry (InSAR). However, its capabilities for topographic mapping are reportedly limited. It is very challenging to create high-quality digital elevation models (DEMs) from InSAR data in mountain area. The main goal of the study was to generate a new high-quality DEM by combining Sentinel-1A and Sentinel-1B data with a short temporal baseline and multiple perpendicular baselines from 43 to 143 m. Five DEMs were produced from five interferometric pairs. The five DEMs were fused to generate a more reliable fused DEM. The performance of the new DEMs was evaluated against ICESat-2/ATL06 product and global DEM products (NASADEM, advanced spaceborne thermal emission and reflection radiometer (ASTER) global digital elevation model (GDEM), and advanced land observing satellite ALOS World 3D-30 m). The results showed that all Sentinel-1 DEMs performed better than ASTER GDEM, four of DEMs had higher accuracies than NASADEM. The fused DEM with the vertical error of 9.08 m for steep terrain (slope>20°) revealed higher accuracy than three global DEMs. The accuracy of DEM was related to terrain slope and land cover type. The accuracies of DEMs decreased as slope increased. The DEMs in glacier area revealed higher errors than those in bare rock. Besides, there was no clear relationship between the perpendicular baseline and vertical accuracy of DEM. The interferometric pair with the shortest baseline (43 m) produced the worst quality DEM, while the interferometric pair with slightly shorter baseline (69 m) produced the highest quality DEM. The study revealed the outstanding accuracy of the new DEM product, which is very valuable data for local glacier research.

Abstract Understanding elevation changes on the Tibetan Plateau is crucial to comprehend the changes in topography, landscape, climate, environmental conditions, and water resources. However, some of the current products that track elevation changes only cover specific surface types or limited areas, and others have low spatial resolution. We propose an algorithm to extract ICESat-2 crossover points dataset for the Tibetan Plateau, and form a dataset. The crossover points dataset has a density of 2.015 groups/km², and each group of crossover points indicates the amount of change in elevation before and after a period of time over an area of approximately 17 meters in diameter. Comparing ICESat-2 crossover points data with existing studies on glaciers and lakes, we demonstrated the reliability of the derived elevation changes. The ICESat-2 crossover points provide a refined data source for understanding high-spatial-resolution elevation changes on the Tibetan Plateau. This dataset can provide validation data for various studies that require high-precision or high-resolution elevation change data on the Tibetan Plateau.

Dust deposited on the surface of snow and glaciers can significantly reduce the snow and ice albedo and accelerate melting. Manual observations of the dust mass concentration (DMC) on snow and glacier surfaces are routinely performed at many locations worldwide. However, snow and ice surface DMC monitoring methods based on remote sensing data still face challenges. This study presents a new retrieval scheme for estimating dust load on snow-covered surfaces from a moderate-resolution imaging spectroradiometer and visible infrared imaging radiometer suite in Northeast and Northwest China that utilizes a classical snow radiative transfer model. Our results indicate that the coefficient of variation of DMC retrieved from the in situ measurements of snow spectral reflectance is 4%, which is within a +4% difference compared with DMC observed in snow and ice samples. Estimating atmospheric dust pollutants content deposited on snow surfaces based on satellite remote sensing observations is feasible. In Northwest China, the root-mean-square error (RMSE) of the DMC values retrieved from VNP09GA data is 9.78 ppm, while that of the DMC values retrieved from MOD09GA data is 13.74 ppm. In Northeast China, the RMSE of the DMC values retrieved from VNP09GA data is 73.98 ppm, while that of the DMC values retrieved from MOD09GA data is 184.32 ppm. The research results can realize continuous monitoring of the atmospheric dust pollutants deposited on snow surfaces, which is of great practical significance to understanding and studying the pollution process of atmospheric dust on snow.

Lake ice phenology serves as a sensitive indicator of climate change in the lake-rich Northeast China. In this study, the freeze-up date (FUD), break-up date (BUD), and ice cover duration (ICD) of 31 lakes were extracted from a time series of the land water surface temperature (LWST) derived from the combined MOD11A1 and MYD11A1 products for the hydrological years 2001 to 2021. Our analysis showed a high correlation between the ice phenology measures derived by our study and those provided by hydrological records (R2 of 0.89) and public datasets (R2 > 0.7). There was a notable coherence in lake ice phenology in Northeast China, with a trend in later freeze-up (0.21 days/year) and earlier break-up (0.19 days/year) dates, resulting in shorter ice cover duration (0.50 days/year). The lake ice phenology of freshwater lakes exhibited a faster rate of change compared to saltwater lakes during the period from HY2001 to HY2020. We used redundancy analysis and correlation analysis to study the relationships between the LWST and lake ice phenology with various influencing factors, including lake properties, local climate factors, and atmospheric circulation. Solar radiation, latitude, and air temperature were found to be the primary factors. The FUD was more closely related to lake characteristics, while the BUD was linked to local climate factors. The large-scale oscillations were found to influence the changes in lake ice phenology via the coupled influence of air temperature and precipitation. The Antarctic Oscillation and North Atlantic Oscillation correlate more with LWST in winter, and the Arctic Oscillation correlates more with the ICD.

Abstract With the ongoing mutation of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) leading to various variants, there is an urgent need for new diagnostic methods for SARS‐CoV‐2 infection. The existing nucleic acid test and antigen test suffer from long assay time and low sensitivity, respectively. Matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS)‐based nasal swabs analysis have been demonstrated as a promising technique in SARS‐CoV‐2 infection screening. However, the applicability of the technique in the different variants of SARS‐CoV‐2 is uncertain. Given the prevalence of the Omicron variant since 2022, we developed a MALDI‐TOF‐based diagnosis method with nasal swab samples to detect the infection by this variant. We collected 325 SARS‐CoV‐2‐positive and 221 SARS‐CoV‐2‐negative nasal swab samples, and the molecular mass fingerprints were acquired from the samples by MALDI‐TOF MS. Using a random forest machine learning classification model to analyze the molecular mass fingerprints MALDI‐TOF mass spectra, the accuracy of 97%, false negative rate of 0%, and false positive rate of 7.6% were achieved for the diagnosis of SARS‐CoV‐2 infection. Combining the MALDI‐TOF analysis with top‐down proteomics, we identified four potential protein biomarkers, that is, humanin‐like 4, thymosin beta‐10, thymosin beta‐4 and statherin, in the nasal swab for the diagnosis of coronavirus disease 2019. It was further found that the four protein biomarkers can also differentiate the SARS‐CoV‐2 original strains infection and Omicron strains infection. These results suggest that the MALDI‐TOF MS‐based nasal swab analysis holds effective diagnostic capabilities of SARS‐CoV‐2 infection, and shows promising potential for global application and extension to other infectious diseases.

The sensitivity of synthetic aperture radar (SAR) polarization information to snow depth changes provides new opportunities for regional snow depth retrieval in mountains with thick snow cover. However, interference from soil signals can affect the accurate quantification of snow volume scattering signals. The aim of this study was to develop a dual-polarimetric radar snow depth estimation (DpRSE) methodological framework that utilizes Sentinel-1 data for snow depth retrieval in the Scandinavian Mountains. This framework is based on the dual-polarimetric radar vegetation index (DpRVIc), which enhances the snow volume scattering signal and reduces soil interference by integrating soil purity and a conditioning factor to fully exploit the advantages of polarimetric SAR information. The results revealed a strong correlation between the DpRVIc and snow depth, and the DpRVIc significantly outperformed the other polarimetric radar indices. The multitemporal retrieval results clearly reflect the heterogeneity of the snow depth distribution. This method is applicable to areas without tree cover. Meanwhile, the shrub-filled regions also affect the snow depth retrieval accuracy. Under the same environmental conditions and using the same validation dataset, a comparison between the DpRSE method and the cross-polarization ratio method was conducted. The results indicated that the DpRSE method surpasses similar existing methods in terms of snow depth retrieval accuracy. Specifically, the coefficient of determination (R2) for the DpRSE method reached 0.66, which represents a 26.9 % improvement over that of the cross-polarization ratio method. Moreover, the mean absolute error (MAE) decreased by 20 %. This study offers a novel perspective on SAR snow depth retrieval utilizing polarization information.

Abstract Leaf rust, caused by Puccinia triticina Eriksson (Pt), is one of the most severe foliar diseases of wheat. Breeding for leaf rust resistance is a practical and sustainable method to control this devastating disease. Here, we report the identification of Lr47, a broadly effective leaf rust resistance gene introgressed into wheat from Aegilops speltoides. Lr47 encodes a coiled-coil nucleotide-binding leucine-rich repeat protein that is both necessary and sufficient to confer Pt resistance, as demonstrated by loss-of-function mutations and transgenic complementation. Lr47 introgression lines with no or reduced linkage drag are generated using the Pairing homoeologous1 mutation, and a diagnostic molecular marker for Lr47 is developed. The coiled-coil domain of the Lr47 protein is unable to induce cell death, nor does it have self-protein interaction. The cloning of Lr47 expands the number of leaf rust resistance genes that can be incorporated into multigene transgenic cassettes to control this devastating disease.

Study region: The Xiying and Heihe River basins, cold and arid mountainous regions of northwestern China. Study focus: In data-limited areas of Northwestern China, satellite- and reanalysis-based precipitation products provide crucial assistance. However, a comprehensive evaluation of their advantages, limitations, and hydrological performance has yet to be ascertained. This knowledge gap impedes a deeper understanding of hydrological processes within the region. This study evaluates the accuracy of ERA5L, IMERG-F, GSMaP-G, and FY4A-QPE from three aspects: time, space, and extreme precipitation. Comprehensive validation was conducted on four products using discharge, flood events, and peak flood simulated by Geomorphology-Ecology-Hydrology Model. New hydrological insights for the region: ERA5L data can be used to build long-term studies on regional meteorological and hydrological trends. IMERG-F is valuable for analyzing extreme precipitation and hydrological events. GSMaP-G is well-suited for identifying precipitation events in the region. It is essential to be cautious of precipitation underestimation when using satellite precipitation products at high elevations and during the winter.This study highlights the differences in accuracy among various precipitation products and provides recommendations for policymakers in selecting data for high-altitude research. It also offers critical information on hydrometeorology and directions for producing high-accuracy precipitation products.

Water vapor is a critical component of the hydrological cycle， and has an important impact on precipitation and water resources on to the Qinghai-Xizang Plateau （QXP）.In this paper， we investigated the water vapor source and contribution of extreme precipitation over four subregions in the Tibetan Plateau during the snow season from 1979 to 2018 by using the Flexible Particle Dispersion Method （FLEXPART）.Based on the existing studies， QXP was divided into the northeastern part of QXP （NEQXP）， northwestern part of QXP （NWQXP）， southeastern part of QXP （SEQXP） and southwestern part of QXP （SWQXP）.The results shows that CMFD has better quality than CPC and HAR， thus we applied CMFD detecting extreme precipitation events over subregions of QXP.The spatial distribution of extreme precipitation showed great spatial difference among four subregions.The areas with high extreme precipitation of NEQXP， NWQXP， SEQXP and SWQXP are mainly distributed around Qionglai Mountain， Karakoram Mountains， Yarlung Zangbo River， and western Himalayas， respectively.Annual extreme precipitation and annual extreme precipitation days increase significantly in the NEQXP， NWQXP and SWQXP while it is insignificantly increasing in the SEQXP.There were significant differences in the water vapor contribution to extreme precipitation over the subregions of QXP.The extreme precipitation in the SEQXP and SWQXP are mainly controlled by water vapor from the Arabian Sea and the Bay of Bengal， while that in the NEQXP is dominated by the water vapor from the local sources， and that in the NWQXP is controlled by water vapor originating from the Central Asia.The continuously increasing water vapor supply over the Bay of Bengal， the Mediterranean， the Arabian Peninsula， the SWQXP， the Central Asia and the NWQXP lead to the increase of the annual extreme precipitation over the NWQXP.Increase of extreme precipitation in the NEQXP is more due to the increase of water vapor in the NEQXP year by year.The continuously increasing water vapor supply from the Central Asia and SWQXP is responsible for the increase extreme precipitation over the SWQXP.Annual water vapor contribution from Bay of Bengal and Arabian Sea and SEQXP local area don’t show increasingly trend to SEQXP which may be the reason for the insignificantly increasing trend of extreme precipitation during the snow season over SEQXP.These results will helpful to improve the understanding the water vapor cycle and extreme precipitation events on the QXP， and explain the mechanism behind the increase of extreme precipitation from the perspective of water vapor sources.

Cadmium (Cd) toxicity affects many crops. Members of the OsNramp (Natural resistance-associated macrophage protein) gene family play important roles in the transport of divalent or trivalent cations in rice (Oryza sativa L.). This study explored a possible involvement of OsNramp4 in Cd2+ transport in rice. We employed the CRISPR/cas9 technique to obtain rice OsNramp4 knockout lines. Using NMT (Non-invasive Micro-test Technology) system for real-time measurement, it was found that the net Cd2+ flux was significantly lower than that of wild-type. The yeast strain expressing OsNramp4 grew poorly under Cd2+ stress, and accumulated more Cd2+ than the control strain, which enhanced the sensitivity of yeast to Cd2+. OsNramp4 was membrane-localized and mainly expressed in roots, but after tillering, its expression shifted to the nodes and glumes. Furthermore, the loss-of-function OsNramp4 mutation lowered the root cell sap Cd2+ content, resulting in a significant decrease in Cd2+ content in shoot and Cd2+ accumulation in grains. We characterized the OsNramp4, an Al3+ transporter with the ability to alter the cellular distribution of Cd2+ in rice and to reduce Cd2+ content in the grain. Our study highlights the complexity of ion uptake and transport in plants.

Study region: Zhangjiakou competition zone (ZJK-CZ) of the Beijing Olympic Winter Games. Study focus: Under the extreme uncertainty of future climate change scenarios, a system solution capable of snow depth forecasting is urgently needed for the sustainable use of Olympic Winter Games venues and the regular organization of winter sports in the post-Winter-Olympic era. This study predicts the snow depth distribution and changes in the ZJK-CZ of the Beijing Olympic Winter Games for the next 30 years utilizing the GBEHM (Geomorphology-Based Ecohydrological Model) model, together with CMIP6 climate forecast model data, and analyzes the operation and profitability of the ZJK-CZ in the post-Winter-Olympic era. New hydrological insights for the region: This study developed a new integrated simulation methodology for the climate risk assessment of Olympic Winter Games venues in the context of future climate change. The percent bias of the snow depth simulated based on the new integrated model was found to be only 3 %. In the next 30 years, the ZJK-CZ will need to be replenished with at least 190 mm of artificial snow annually to meet the minimum standards for suitable winter sports days and requires an average of at least 49 days of artificially assisted snowmaking per year to ensure proper operation and profitability. This study is expected to provide a climate risk assessment for the construction and operation of winter sports venues.

Background: BRII-196 and BRII-198 are two anti-SARS-CoV-2 monoclonal neutralizing antibodies as a cocktail therapy for treating COVID-19 with a modified Fc region that extends half-life.Methods: Safety, tolerability, pharmacokinetics, and immunogenicity of BRII-196 and BRII-198 were investigated in first-in-human, placebo-controlled, single ascending dose phase 1 studies in healthy adults. 44 participants received a single intravenous infusion of single BRII-196 or BRII-198 up to 3,000 mg, or BRII-196 and BRII-198 combination up to 1500/1500 mg, or placebo and were followed up for 180 days. Primary endpoints were incidence of adverse events (AEs) and changes from pre-dose baseline in clinical assessments. Secondary endpoints included pharmacokinetics profiles of BRII-196/BRII-198 and detection of anti-drug antibodies (ADAs). Plasma neutralization activities against SARS-CoV-2 Delta live virus in comparison to post-vaccination plasma were evaluated as exploratory endpoints.Results: All infusions were well-tolerated without systemic or local infusion reactions, dose-limiting AEs, serious AEs, or deaths. Most treatment-emergent AEs were isolated asymptomatic laboratory abnormalities of grade 1-2 in severity. BRII-196 and BRII-198 displayed pharmacokinetics characteristic of Fc-engineered human IgG1 with mean terminal half-lives of 44.6–48.6 days and 72.2–83.0 days, respectively, with no evidence of interaction or significant anti-drug antibody development. Neutralizing activities against the live virus of the SARS-CoV-2 Delta variant were maintained in plasma samples taken on day 180 post-infusion.Conclusion: BRII-196 and BRII-198 are safe, well-tolerated, and suitable therapeutic or prophylactic options for SARS-CoV-2 infection.Clinical Trial Registration:ClinicalTrials.gov under identifiers NCT04479631, NCT04479644, and NCT04691180.

Accurate precipitation measurements are essential for understanding hydrological processes in high-altitude regions. Conventional gauge measurements often yield large underestimations of actual precipitation, prompting the development of statistical methods to correct the measurement bias. However, the complex conditions at high altitudes pose additional challenges to the statistical methods. To improve the correction of precipitation measurements in high-altitude areas, we selected the Yakou station, situated at an altitude of 4147 m on the Tibetan plateau, as the study site. In this study, we employed the machine learning method XGBoost regression to correct precipitation measurements using meteorological variables and remote sensing data, including Global Satellite Mapping of Precipitation (GSMaP), Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Additionally, we examined the transferability of this method between different stations in our study site, Norway, and the United States. Our results show that the Yakou station experiences a large underestimation of precipitation, with a magnitude of 51.4%. This is significantly higher than similar measurements taken in the Arctic or lower altitudes. Furthermore, the remote sensing precipitation datasets underestimated precipitation when compared to the Double Fence Intercomparison Reference (DFIR) precipitation observation. Our findings suggest that the machine learning method outperformed the traditional statistical method in accuracy metrics and frequency distribution. Introducing remote sensing data, especially the GSMaP precipitation, could potentially replace the role of in situ wind speed in precipitation correction, highlighting the potential of remote sensing data for correcting precipitation rather than in situ meteorological observation. Moreover, our results indicate that the machine learning method with remote sensing data demonstrated better transferability than the traditional statistical method when we cross-validated the method with sites located in different countries. This study offers a promising strategy for obtaining more accurate precipitation measurements in high-altitude regions.

The double motor hybrid vehicle distributes the power on both sides through the parallel planetary gear mechanism. The meshing force of the two rows of planetary gears affects each other， resulting in the increase of dynamic load coefficient and vibration excitation. The harmonic analysis method is used to obtain the frequency ratio of meshing force under multiple working conditions， so as to establish RBF neural network model， and a prediction of meshing force in a certain working range is carried out. The maximum meshing force and dynamic load coefficient are calculated on the prediction results， and the meshing force is proportional to the difference between speed ratio and output torque ratio minus 1， and the dynamic load coefficient on the low-speed side is greater than that on the high-speed side， which can be expressed as a multiple or reciprocal multiple relationship. It provides a theoretical basis for the study of dynamics of hybrid vehicles with dual motors.

Abstract Background Recent studies have suggested that estrogen (E2) plays an important role in epithelial ovarian cancer (EOC). However, the mechanism of E2 in ovarian cancers is unclear. The purpose of this study was to investigate the effect of E2 on ovarian cancers and illuminate the mechanism of E2 in promote ovarian cancers proliferation. Results We demonstrated that E2 stimulated the proliferation and invasion of ovarian cancer cells. In this study, ovarian cancer specimens were also analyzed for transient receptor potential channel C3 (TRPC3) expression; TRPC3 expression levels were higher in ovarian cancer samples than in normal ovarian tissue samples. Previous studies have shown that TRPC3 contributes to the progression of human ovarian cancer. In this study, we further investigated the interaction between E2 and TRPC3. We found that E2 stimulation enhanced the expression of TRPC3 at both the mRNA and protein levels. E2 stimulation enhanced the influx of Ca2+. Moreover, siRNA-mediated silencing of TRPC3 expression inhibited the ability of E2 to stimulate the influx of Ca2+. Conclusions In conclusion, TRPC3 plays a significant role in the stimulatory activity of E2 and could be a therapeutic target for the treatment of EOC. Furthermore, this study elucidates the molecular mechanism by which E2 promotes the proliferation and migration of EOC cells.

As an essential data-driven model, machine learning can simulate runoff based on meteorological data at the watershed level. It has been widely used in the simulation of hydrological runoff. Considering the impact of snow cover on runoff in high-altitude mountainous areas, based on remote sensing data and atmospheric reanalysis data, in this paper we established a runoff simulation model with a random forest model and ANN (artificial neural network) model for the Xiying River Basin in the western Qilian region The verification of the measured data showed that the NSE (Nash–Sutcliffe efficiency), RMSE (root mean square error), and PBIAS (percent bias) values of the random forest model and ANN model were 0.701 and 0.748, 6.228 m3/s and 4.554 m3/s, and 4.903% and 8.329%, respectively. Considering the influence of ice and snow on runoff, the simulation accuracy of both the random forest model and ANN model was improved during the period of significant decreases in the annual snow and ice water equivalent in the Xiying River Basin from April to May, after the snow remote sensing data were introduced into the model. Specifically, for the random forest model, the NSE increased by 0.099, the RMSE decreased by 0.369 m3/s, and the PBIAS decreased by 1.689%. For the ANN model, the NSE increased by 0.207, the RMSE decreased by 0.700 m3/s, and the PBIAS decreased by 1.103%. In this study, based on remote sensing data and atmospheric reanalysis data, the random forest model and ANN model were used to effectively simulate hydrological runoff processes in high-altitude mountainous areas without observational data. In particular, the accuracy of the machine learning simulations of snowmelt runoff (especially during the snowmelt period) was effectively improved by introducing the snow remote sensing data, which can provide a methodological reference for the simulation and prediction of snowmelt runoff in alpine mountains.

Climate change has profoundly impacted elements of land surface at high latitudes and elevations, especially snow and vegetation [...]

Four up-to-date daily cloud-free snow products – IMS (Interactive Multisensor Snow products), MOD-SSM/I (combination of the MODIS and SSM/I snow products), MOD-B (Blending method basing on the MODIS snow cover products) and TAI (Terra–Aqua–IMS) – with high-resolutions over the Qinghai-Tibetan Plateau (QTP) were comprehensively assessed. Comparisons of the IMS, MOD-SSM/I, MOD-B and TAI cloud-free snow products against meteorological stations observations over 10 snow seasons (2004–2013) over the QTP indicated overall accuracies of 76.0%, 89.3%, 92.0% and 92.0%, respectively. The Khat values of the IMS, MOD-SSM/I, MOD-B and TAI products were 0.084, 0.463, 0.428 and 0.526, respectively. The TAI products appear to have the best cloud-removal ability among the four snow products over the QTP. Based on the assessment, an I-TAI (Improvement of Terra–Aqua–IMS) snow product was proposed, which can improve the accuracy to some extent. However, the algorithms of the MODIS series products show instability when identifying wet snow and snow under forest cover over the QTP. The snow misclassification is an important limitation of MODIS snow cover products and requires additional improvements.

Snow cover phenology (SCP) is critical to the climate system. China has the most comprehensive snow cover distribution in the middle and low latitudes and has shown dramatic changes over the past few decades. However, the spatiotemporal characteristics of SCP parameters and their sensitivity to meteorological factors (temperature and precipitation) under different conditions (altitude, snow cover classification, or season) in China are insufficiently studied. Therefore, using improved daily MODIS cloud-gap-filled (CGF) snow-cover-extent (SCE) products, the spatiotemporal characteristics (distribution and variation) and respond to climate of snow cover area (SCA), snow cover start (SCS), snow cover melt (SCM), and snow cover days (SCD) are explored from 2000 to 2020. The results show that in the past 20 years, snow cover in China has demonstrated a trend of decreasing SCA, decreasing SCD, advancing SCS, and advancing SCM, with SCM advancing faster than SCS. The greatest snowfall occurs in January, mainly in northeastern China, northern Xinjiang, and the Tibet Plateau. Spatially, the slope of SCP was mainly within ±0.5 day/year (d/y) Statistics indicated that the area proportion where SCD is significantly reduced is greater than increased; SCD, SCS, and SCM are shortened or advanced in three snow-covered area classifications. Moreover, compared with precipitation, the significantly correlated regions (6–47.2% more than precipitation) and correlation degree (1.23–8.33 times precipitation in significantly correlated snow cover classification) between temperature and SCP in different seasons are larger. For stable snow-covered areas (SSA), SCD are mainly affected by spring temperature below 1500 m and mainly by autumn temperature above 1500 m; the precipitation is more affected in autumn. The correlation of SCP with temperature and precipitation has obvious spatial and seasonal differences and shows characteristic variation with altitude. These results can provide important data support for climate prediction, hydrological research, and disaster warning.

Soil temperature is an important physical variable that characterises geothermal conditions and influences geophysical, biological and chemical processes in the earth sciences. Soil temperature is not only affected by climatic and geographical factors; it is also modulated by local factors such as snow cover and vegetation. This paper investigates the relationship between snow cover and vegetation and soil temperature with the help of two classical remote sensing indicators, the Snow Cover Days (SCD) based Advanced Very High Resolution Radiometer and the Normalized Difference Vegetation Index (NDVI)-based Global Inventory Modelling and Mapping Studies, to analyse the influence of local factors on soil temperature in the Three River Source Region (TRSR). Combing multi-layer geothermal observations from 23 stations in the TRSR with meteorological dataset, soil properties datasets, snow cover and vegetation indices, a non-linear model, the Random Forest model, is used to establish a multi-layer soil temperature dataset to analyse the influence of surface cover factors in each depth. The results showed that the annual SCD had a decreasing trend during 1982–2015 and was negatively correlated with the annual mean soil temperature; the annual NDVI had no significant trend, but it was positively correlated with the annual mean soil temperature. Regionally, there was a significant decrease in SCD in the mountainous areas bordering the source areas of the three rivers, and there was a trend of increasing NDVI in the northwest and decreasing vegetation in the southwest in the TRSR. The stronger the correlation with soil temperature in areas with a larger SCD, the more the snow has a cooling effect on the shallower soil temperatures due to the high albedo of the accumulated snow and the repeated melting and heat absorption of the snow in the area. The snow has an insulating effect on the 40 cm soil layer by impeding the cooling effect of the atmosphere in winter. In sparsely vegetated areas, vegetation lowers ground albedo and warms the soil, but in July and August, in areas with more vegetation, NDVI is negatively correlated with soil temperature, with heavy vegetation intercepting summer radiant energy and having a cooling effect on the soil.

Reliable cloud masks in Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products have a high potential to improve the retrieval of snow properties. However, cloud–snow confusion is a popular problem in MODIS snow cover products, especially in boreal forest areas. A large amount of forest snow is misclassified as clouds because of the low normalized difference snow index (NDSI), and excessive cloud masks limit the application of snow products. In addition, ice clouds are easily misclassified as snow due to their similar spectral characteristics, which leads to snow commission errors. In this paper, we quantitatively evaluated the cloud–snow confusion in Northeast China and found that snow-covered forests and transition zones from snow-covered to snow-free areas are prone to being misclassified as clouds, while clouds are less likely to be misclassified as snow. A temporal-sequence cloud–snow-distinguishing algorithm based on the high-frequency observation characteristics of the Himawarri-8 geostationary meteorological satellite is proposed. In the temporal-sequence images acquired from that satellite, the NDSI variance in cloud pixels should be greater than that of snow because clouds vary over time, while snow is relatively stable. In the MODIS snow cover products, the cloud pixels with NDSI variance lower than a threshold are identified as cloud-free areas and attributed their raw NDSI value, while the snow pixels with NDSI variance greater than the threshold are marked as clouds. We applied this method to MOD10A1 C6 in Northeast China. The results showed that the excessive cloud masks were greatly eliminated, and the new cloud mask was in good agreement with the real cloud distribution. At the same time, some possible ice clouds which had been misclassified as snow for their spectral characteristics similar to those of snow were identified correctly.

Background/Aims: The elaborate structure of the extracellular matrix (ECM) and the appropriate surface glycoforms upon it are indispensable to CD4+ T cell regulation. Methods: To explore the effects of Glcα1,2Galβ1 glycosylation mediated by GLT25D2 (Colgalt2) for CD4+ T cell regulation, we prepared C57BL/6J Glt25d2-/- mice. In the induction of hepatitis, after concanavalin A (Con A) challenge for 6, 12, and 24 h, more extensive parenchymal injury was noted in Glt25d2-/- mice than in wild-type (WT) mice at 12 h. Immunohistochemistry and laser scanning confocal microscopy were used to detect GLT25D2 expression, and subsets of CD4+T cells was analyzed by flow cytometry. A total of 26 cytokines in serum samples were determined using Luminex technology. Results: The trend in liver injury score variation was consistent with serum alanine aminotransferase and aspartate aminotransferase levels. The levels of interleukin 4 (IL-4), IL-1β, IL-9, and several chemokines such as macrophage inflammatory protein-2, eotaxin, and growth-related oncogene α were significantly increased in Glt25d2-/- mice compared with WT mice after Con A challenge. A further phenotype analysis of primary Glt25d2-/- CD4+ T cells showed that Glt25d2 knockout increased the frequency of the CD25+CD69- subset but decreased the frequency of the CD25-CD69+ subset after Con A challenge for 6, 12, and 24 h compared with those of WT CD4+ T cells. Activation-induced apoptosis was also significantly increased in Glt25d2-/- CD4+ T cells after Con A challenge compared with WT CD4+ T cells. Lectin microarray hybridization showed that Glt25d2 knockout increased the binding activity of Narcissus pseudonarcissus lectin to CD4+ T cells but Amaranthus caudatus lectin–binding activity was lost during Con A challenge. Conclusion: The present results suggest that collagen glycosylation mediated by GLT25D2 may regulate a subset of CD4+ T cells and be involved in the pathogenesis of Con A–induced hepatitis.

High-resolution Synthetic Aperture Radar (SAR), as an efficient Earth observation technology, can be used as a complementary means of observation for snow depth (SD) and can address the spatial heterogeneity of mountain snow. However, there is still uncertainty in the SD retrieval algorithm based on SAR data, due to soil surface scattering. The aim of this study is to quantify the impact of soil signals on the SD retrieval method based on the cross-ratio (CR) of high-spatial resolution SAR images. Utilizing ascending Sentinel-1 observation data during the period from November 2016 to March 2020 and a CR method based on VH- and VV-polarization, we quantitatively analyzed the CR variability characteristics of rock and soil areas within typical thick snow study areas in the Northern Hemisphere from temporal and spatial perspectives. The correlation analysis demonstrated that the CR signal in rock areas at a daily timescale shows a strong correlation (mean value > 0.60) with snow depth. Furthermore, the soil areas are more influenced by freeze-thaw cycles, such that the monthly CR changes showed no or negative trend during the snow accumulation period. This study highlights the complexity of the physical mechanisms of snow scattering during winter processes and the influencing factors that cause uncertainty in the SD retrieval, which help to promote the development of high-spatial resolution C-band data for snow characterization applications.

A thorough understanding of the freshwater ice process received considerable critical attention due to increasing winter recreations and ice engineering. The development of the lake ice process of Chagan Lake was monitored using MODIS and Landsat images over eight consecutive snow seasons from October 2013 to April 2021. We derived the lake ice phenology from an eight-day time series of lake water skin temperature (LWST) provided by MODIS, including freeze-up date, break-up date, and ice cover duration. We discovered a large-scale fracture extending from northwest to southeast that repeatedly appeared on Landsat images since 1986. A novel fractal-based auto-extraction is proposed to extract the length and angle of these fractures. We also carried out a field campaign and an ice ridge was found at the southernmost part of what we observed from the images. Moreover, we explained the fracturing development by thermal changes, wind in lake, and underlying flow. Results show that the lake ice fracture is nearly perpendicular to the dominant wind direction in the cold season, which indicates the crucial role of wind on lake ice fracturing.

Background. In our previous study, we found that the FAM172A recombinant protein could promote proliferation of L02 cells. However, the underlying mechanisms are still unknown. The present study was aimed at investigating the effect of FAM172A on proliferation of HepG2 cells and exploring the possible molecular mechanisms and its role in hepatocellular carcinoma (HCC). Methods. Cell proliferation was measured by MTT assay. Western blot test was carried out to investigate the mechanism. Rabbit antibodies against FAM172A and membrane proteins isolated from lysate of HepG2 cell were coprecipitated and the resultant precipitates were analyzed by mass spectrum. Results. The MTT assay showed that recombinant protein FAM172A isoform 1 (FAM172A-1) could induce HepG2 cell proliferation at the concentration of 10-100 ng/mL, while protein FAM172A isoform 3 (FAM172A-3) was at the concentration of 80-100 ng/mL. Western blot demonstrated that both FAM172A-1 and FAM172A-3 could activate the mitogen-activated protein kinase/extracellular signal-regulated protein kinase (MAPK/ERK) pathway and the phosphatidylinositol 3-kinase/threonine-protein kinase (PI3K/Akt) pathway. Mass spectrum analysis suggested that there were some membrane proteins interacting with FAM172A. Several candidate interacting proteins might mediate proliferation signals induced by FAM172A recombinant protein, including seven membrane proteins. Conclusion. In conclusion, FAM172A recombinant protein could induce proliferation of HepG2 cells, in which the MAPK/ERK and PI3K/Akt signaling pathways might be involved. The role of FAM172A in HepG2 cell proliferation also indicated its possible involvement in HCC. The receptor of FAM172A on cells still needs to be exploited.

Endmember extraction is a primary and indispensable component of the spectral mixing analysis model applicated to quantitatively retrieve fractional snow cover (FSC) from satellite observation. In this study, a new endmember extraction algorithm, the spatial–spectral–environmental (SSE) endmember extraction algorithm, is developed, in which spatial, spectral and environmental information are integrated together to automatically extract different types of endmembers from moderate resolution imaging spectroradiometer (MODIS) images. Then, combining the linear spectral mixture analysis model (LSMA), the SSE endmember extraction algorithm is practically applied to retrieve FSC from standard MODIS surface reflectance products in China. The new algorithm of MODIS FSC retrieval is named as SSEmod. The accuracy of SSEmod is quantitatively validated with 16 higher spatial-resolution FSC maps derived from Landsat 8 binary snow cover maps. Averaged over all regions, the average root-mean-square-error (RMSE) and mean absolute error (MAE) are 0.136 and 0.092, respectively. Simultaneously, we also compared the SSEmod with MODImLAB, MODSCAG and MOD10A1. In all regions, the average RMSE of SSEmod is improved by 2.3%, 2.6% and 5.3% compared to MODImLAB for 0.157, MODSCAG for 0.157 and MOD10A1 for 0.189. Therefore, our SSE endmember extraction algorithm is reliable for the MODIS FSC retrieval and may be also promising to apply other similar satellites in view of its accuracy and efficiency.

Seasonal snow cover is closely related to regional climate and hydrological processes. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products from 2001 to 2018 were applied to analyze the snow cover variation in northern Xinjiang, China. As cloud obscuration causes significant spatiotemporal discontinuities in the binary snow cover extent (SCE), we propose a conditional probability interpolation method based on a space-time cube (STCPI) to remove clouds completely after combining Terra and Aqua data. First, the conditional probability that the central pixel and every neighboring pixel in a space-time cube of 5 × 5 × 5 with the same snow condition is counted. Then the snow probability of the cloud pixels reclassified as snow is calculated based on the space-time cube. Finally, the snow condition of the cloud pixels can be recovered by snow probability. The validation experiments with the cloud assumption indicate that STCPI can remove clouds completely and achieve an overall accuracy of 97.44% under different cloud fractions. The generated daily cloud-free MODIS SCE products have a high agreement with the Landsat–8 OLI image, for which the overall accuracy is 90.34%. The snow cover variation in northern Xinjiang, China, from 2001 to 2018 was investigated based on the snow cover area (SCA) and snow cover days (SCD). The results show that the interannual change of SCA gradually decreases as the elevation increases, and the SCD and elevation have a positive correlation. Furthermore, the interannual SCD variation shows that the area of increase is higher than that of decrease during the 18 years.

Snow surface spectral reflectance is very important in the Earth’s climate system. Traditional land surface models with parameterized schemes can simulate broadband snow surface albedo but cannot accurately simulate snow surface spectral reflectance with continuous and fine spectral wavebands, which constitute the major observations of current satellite sensors; consequently, there is an obvious gap between land surface model simulations and remote sensing observations. Here, we suggest a new integrated scheme that couples a radiative transfer model with a land surface model to simulate high spectral resolution snow surface reflectance information specifically targeting multisource satellite remote sensing observations. Our results indicate that the new integrated model can accurately simulate snow surface reflectance information over a large spatial scale and continuous time series. The integrated model extends the range of snow spectral reflectance simulation to the whole shortwave band and can predict snow spectral reflectance changes in the solar spectrum region based on meteorological element data. The kappa coefficients (K) of both the narrowband snow albedo targeting Moderate Resolution Imaging Spectroradiometer (MODIS) data simulated by the new integrated model and the retrieved snow albedo based on MODIS reflectance data are 0.5, and both exhibit good spatial consistency. Our proposed narrowband snow albedo simulation scheme targeting satellite remote sensing observations is consistent with remote sensing satellite observations in time series and can predict narrowband snow albedo even during periods of missing remote sensing observations. This new integrated model is a significant improvement over traditional land surface models for the direct spectral observations of satellite remote sensing. The proposed model could contribute to the effective combination of snow surface reflectance information from multisource remote sensing observations with land surface models.

Post-translational modifications such as glycosylation play an important role in the functions of homeostatic proteins, and are critical driving factors of several diseases; however, the role of glycosylation in autoimmune hepatitis is poorly understood. Here, we established an O-GlcNAc glycosylation-deficient rat model by knocking out the Eogt gene by TALEN-mediated gene targeting. O-GlcNAc glycosylation deficiency overtly aggravated liver injury in concanavalin-A induced autoimmune hepatitis, and delayed self-recovery of the liver. Furthermore, flow cytometry analysis revealed increased CD4+ T cell infiltration in the liver of rats with O-GlcNAc glycosylation deficiency, and normal differentiation of regulatory T cells (Tregs) in the liver to inhibit T cell infiltration could not be activated. Moreover, in vitro experiments showed that O-GlcNAc glycosylation deficiency impaired Treg differentiation to inhibit the Notch signaling pathway in CD4+ T cells. These finding indicate that O-GlcNAc glycosylation plays a critical role in the activation of Notch signaling, which could promote Treg differentiation in the liver to inhibit T cell infiltration and control disease development in autoimmune hepatitis. Therefore, this study reveals a regulatory role for glycosylation in the pathogenesis of autoimmune hepatitis, and highlights glycosylation as a potential treatment target.

Excessive cadmium (Cd) accumulation in grains of rice (Oryza sativa L.) is a risk to food security. The transporters in the nodes of rice are involved in the distribution of mineral elements including toxic elements to different tissues such as grains. However, the mechanism of Cd accumulation in grains is largely unknown. Here, we report a node-expressed transporter gene, OsCCX2, a putative cation/calcium (Ca) exchanger, mediating Cd accumulation in the grains of rice. Knockout of OsCCX2 caused a remarkable reduction of Cd content in the grains. Further study showed that disruption of this gene led to a reduced root-to-shoot translocation ratio of Cd. Moreover, Cd distribution was also disturbed in different levels of internode and leaf. OsCCX2 is localized to plasma membrane, and OsCCX2 is mainly expressed in xylem region of vascular tissues at the nodes. OsCCX2 might function as an efflux transporter, responsible for Cd loading into xylem vessels. Therefore, our finding revealed a novel Cd transporter involved in grain Cd accumulation, possibly via a Ca transport pathway in the nodes of rice.

During double fertilization of angiosperms, the central cell of the female gametophyte fuses with a sperm cell to produce the endosperm, a storage tissue that nourishes the developing embryo within the seed. Although many genetic mutants defective in female gametophytic functions have been characterized, the molecular mechanisms controlling the specification and differentiation of the central cell are still not fully understood. Here, we report a mitochondrial ribosomal protein, RPS9M, is required for central cell maturation. RPS9M was highly expressed in the male and female gametophytes before and after double fertilization. The female gametophytes were defective in the rps9m mutant specifically concerning maturation of central cells. The morphological defects include unfused polar nuclei and smaller central vacuole in central cells. In addition, embryo initiation and early endosperm development were also severely affected in rps9m female gametophytes even after fertilized with wild type pollens. The RPS9M can interact with ANK6, an ankyrin-repeat protein in mitochondria previously reported to be required for fertilization. The expression pattern and mutant phenotype of RPS9M are similar to those of ANK6 as well, suggesting that RPS9M may work together with ANK6 in controlling female gametophyte development, possibly by regulating the expression of some mitochondrial proteins.

This study was designed to investigate the role of serum GP73 for diagnosing significant fibrosis in patients with chronic hepatitis B virus (HBV) infections. Two populations were enrollment. All subjects were patients with chronic HBV infections. First population included 761 patients, who received liver stiffness measurement; the second population included 633 patients, who undertaken liver biopsy, in which 472 patients with nearly normal ALT. All patients received serum GP73 test. The effect of GP73 recombinant protein to HepG2 cells and LX2 cells were observed in vitro. Results showed that serum GP73 concentration is correlated with liver stiffness (r = 0.601). The area under ROC curve is 0.76. The sensitivity and specificity of GP73 for significant fibrosis (≥F2) diagnosis were 62.81%, 80.05% respectively (cut off: 76.6 ng/ml). Serum GP73 concentration was significantly correlated with the grading of fibrosis (r = 0.32, and 0.35, in 633 and 472 patients, respectively.) GP73 had a striking performance for diagnosing S2 in patients with chronic HBV infections. In 472 patients with nearly normal ALT, the sensitivity and specificity of GP73 for S2 diagnosis were 62.5% and 80.0% respectively, where the cut-off was set at 82 ng/ml. GP73 recombinant protein may prompt LX2 cells proliferation at the concentration 10-100 ng/ml. The present results indicated that GP73 may be a marker for diagnosing significant fibrosis in patients with chronic HBV infections, and may be a new contributor to fibrogensis.