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The high temperature, negative temperature coefficient (NTC), thermosensitive ceramics xY2O3-(1-x)LaCrO3 (x = 0.4, 0.5, 0.6, 0.7) were prepared via spark plasma sintering. The effects of components, sintering temperature, and sintering pressure on the electrical properties of the composite ceramics were studied. The composite NTC ceramics were composed of orthogonal perovskite structure LaCrO3 and Y2O3. The presence of Cr3+ and Cr4+ was confirmed using X-ray photoelectron spectroscopy, which may be responsible for the hopping conduction. The composite NTC ceramics sintered at 1500 °C had good electrical properties. The resistivities and the coefficient of temperature sensitivity (B) increased with the rise in Y2O3 content. The values of ρ250, ρ700, B400/700, and activation energy of xY2O3-(1-x)LaCrO3 composite NTC ceramics sintered at 1500 °C were 1.7 × 1010–5.51 × 1010 Ω cm, 4.32 × 104–6.16 × 106 Ω cm, 14,111–16,223 K, and 1.221–1.403 eV, respectively. The composite NTC ceramics prepared at different sintering pressures displayed identical electrical properties.

Recently emerged SARS-CoV-2 Omicron subvariant, BA.2.75, displayed a growth advantage over circulating BA.2.38, BA.2.76, and BA.5 in India. However, the underlying mechanisms for enhanced infectivity, especially compared with BA.5, remain unclear. Here, we show that BA.2.75 exhibits substantially higher affinity for host receptor angiotensin-converting enzyme 2 (ACE2) than BA.5 and other variants. Structural analyses of BA.2.75 spike shows its decreased thermostability and increased frequency of the receptor binding domain (RBD) in the "up" conformation under acidic conditions, suggesting enhanced low-pH-endosomal cell entry. Relative to BA.4/BA.5, BA.2.75 exhibits reduced evasion of humoral immunity from BA.1/BA.2 breakthrough-infection convalescent plasma but greater evasion of Delta breakthrough-infection convalescent plasma. BA.5 breakthrough-infection plasma also exhibits weaker neutralization against BA.2.75 than BA.5, mainly due to BA.2.75's distinct neutralizing antibody (NAb) escape pattern. Antibody therapeutics Evusheld and Bebtelovimab remain effective against BA.2.75. These results suggest BA.2.75 may prevail after BA.4/BA.5, and its increased receptor-binding capability could support further immune-evasive mutations.

As a crucial source of runoff in headwater regions, seasonal snowmelt plays an important role in ensuring water availability downstream, particularly during low flow periods. As the major constituent of China's water towers, the headwater region of the Yangtze River (HRYR) provides water to hundreds of millions of people downstream. Therefore, accurately simulating snowmelt is critical to developing a better understanding of the hydrological processes, which would, in turn, benefit water supply management, irrigation, hydropower generation, and ecological integrity over the HRYR and its lower reaches. However, it is a considerable challenge to conduct hydrological modeling for ungauged and poorly gauged headwater regions, owing to a lack of in situ measurements to appropriately constrain the model and evaluate its results. Satellite remote sensing provides an unprecedented opportunity to capture hydrological state variables globally, such as snow cover area (SCA) based on optical remote sensing and snow water equivalent (SWE) based on passive microwave remote sensing. This study simulates snow and glacier meltwater of the HRYR (above the Zhimenda gauging station), and quantifies proportional meltwater contributions to total runoff using multisource remote sensing data and a distributed hydrological model. We, for the first time ever, evaluate the snowmelt simulations based on the hydrological consistency among precipitation, air and land surface temperatures, and remotely sensed SWE/SCA. Results show that the snowmelt simulations using either SWE or SCA as a reference for calibrating parameters of the hydrological model are highly consistent, with snow and glacier meltwater contributing ~7% and ~5%, respectively, to the total runoff during 2003–2014. This study serves as a basis to simulate snowmelt to understand runoff generation and evolution under climate change across ungauged and poorly gauged headwater regions using multisource remote sensing data.

Snow cover condition across the Tibetan Plateau (TP) is not only a significant indicator of climate change but also a vital variable in water availability because of its water storage function in high-mountain regions of Southwest China and the surrounding Asian countries. Limited by low spatial resolution, incomplete spatial coverage, and short time span of the current snow cover products, the long-term snow cover change across the TP under the climate change background remains unclear. To resolve this issue, a composite long-term gap-filled TP daily 5-km snow cover extent (SCE) record (TPSCE) is generated by integrating SCE from the Advanced Very High-Resolution Radiometer (AVHRR) surface reflectance climate data record (CDR) and several existing snow cover data sets, with the help of a decision tree snow cover mapping algorithm, for the period 1981–2016. A snow discrimination process was used to classify the land surface into snow (pre-TPSCE) and non-snow using AVHRR surface reflectance CDR. To fill gaps caused by invalid observations and cloud contamination in pre-TPSCE, several existing daily SCE products, including MOD10C1, MYD10C1, IMS, JASMES, and a passive microwave snow depth data set are employed in the composition process. The daily snow discrimination accuracy, tested by ground snow-depth observations during 2000–2014, shows that the TPSCE captures the distribution of snow duration days (R2 = 0.80, bias = 3.93 days) effectively. The comparison between the TPSCE and fine-resolution snow cover maps (MCD10A1-TP) indicates high comparability between the TPSCE and MCD10A1-TP. In addition, cross-comparisons with changes in temperature, precipitation, and land surface albedo indicate that the TPSCE is reliable in climate change studies. In summary, the TPSCE is spatially complete and covers the longest period among all current snow cover products from satellite observations. The TPSCE seamlessly records changes in snow cover across the TP over the past 36 years, thereby providing valuable snow information for climate change and hydrological studies.

Remote sensing can potentially be used to monitor river ice, which is of significance for water resource utilization and hydrological research, especially on the Tibetan Plateau, over which field observations are severely limited. However, methods to monitor the river ice distribution over a large scale while overcoming snow interference have not been reported yet. To monitor river ice on the Tibetan Plateau on a large scale, we present a relative difference river ice (RDRI) identification method based on the differential spectral characteristics of river ice. The difference between the red and near-infrared (NIR) band reflectance of river ice is divided by the sum of the NIR reflectance and infrared band reflectance. The resulting values are notably different for river ice and other neighboring similar landscapes, such as snow cover. This method can overcome the interference caused by snow cover and other surrounding landscapes and enable the monitoring of river ice in different forms at different elevations on the Tibetan Plateau. The RDRI method is applied to Landsat and Sentinel-2 images. The validation results show that the mean overall accuracy and Kappa coefficient were 99.76% and 0.94, respectively. The comparison of the RDRI and normalized difference snow index (NDSI) methods indicates that the accuracies of both methods for river ice monitoring are similar in the absence of any snow disturbance. The RDRI can enable more accurate remote sensing of river ice on a large scale, which can support river ice distribution research over the Tibetan Plateau.

Soybean import accounts for 90% of China's total domestic soybean supply. Such import has a substantial impact on how the country's resources are used as well as on its environment. In this study, we performed a national-scale assessment of the impact of soybean import on domestic cropland conversion, crop production, water use and nitrogen (N) fertilizer application. Results show that soybean production in China decreased by 26% (4.46 million tons) and sown areas were reduced by 25% (2.39 million ha) from the peak of 2004 to 2016. Of the areas taken out of the soybean production, 70% were converted to maize, 20% to rice, 3% to vegetables and 7% to fruits during this period. As a result of the cropland conversion, the production of maize, rice, vegetables and fruits increased by 10.42, 3.34, 2.49 and 3.26 million tons respectively. However, irrigation water use in the areas that were converted to the cultivation of the four types of crops increased by 96.42% (3.05 km3), with much of it coming from northern provinces where water is generally scarce. The application of N fertilizer increased by 256.65 thousand tons (almost 5 times) on the converted areas, partly due to the loss of the N-fixing soybean cultivation. Although a large quantity of virtual water and land were imported through soybean trade, the water use and N application were increased in reality. The analysis of the land-water-food-environment nexus in the context of soybean import provides comprehensive and useful information about the benefits and trade-offs associated with China's international soybean trade.

River ice monitoring is important for hydrological research and water resource management of the Tibetan Plateau but limited by the serious shortage of field observations, and remote sensing can be used as an effective supplementary means for monitoring river ice. However, remote sensing high-altitude river ice is scarce and a basin-scale understanding of river ice is lacking on the Tibetan Plateau. To ascertain the spatial and temporal distribution characteristics of high-altitude river ice at the basin scale, we selected the Babao River basin as the study area, which is a typical river basin located in the northeastern Tibetan Plateau. Utilizing 447 available Landsat images during the river ice period from 1999 to 2018 and the classical normalized difference snow index (NDSI) algorithm, we monitored the river ice in a long time series at the Babao River basin. The average Khat of accuracy validation reached 0.973. The average area of river ice in the river ice period of this basin showed a weak decreasing trend and was negatively correlated with air temperature. We also found that gentle slopes and high elevations are beneficial for the development of river ice. The melting of river ice supplements river discharge in spring. This study is the first to reveal the distribution characteristics and changing trend of river ice at the basin scale on the Tibetan Plateau, and the results provide a reference for river ice research in this region.

The performance of the Noah land surface model (LSM) with multi-parameterization options (Noah-MP) in simulating snow depth was evaluated in northern Xinjiang, China. A total number of 13,824 Noah-MP physics-ensemble simulations were conducted at the Altay site by combining different parameterization schemes of physical processes while disregarding the uncertainties of forcing data and model parameters. The natural selection approach and Tukey’s test, which are two sensitivity analysis methods, were used to analyze the sensitivity of snow to parameterization schemes. Then, the uncertainty intervals of the ensemble simulation experiments were compared. According to the results of the sensitivity and uncertainty experiments, snow depth could be simulated by three typical combination schemes at the regional scale: the longest snow melting time scheme (LT), the shortest snow melting time scheme (ST) and the default combination scheme (DT). Observation data of snow depth from thirty-nine meteorological stations in northern Xinjiang were used to evaluate the snow simulation performance of typical combination schemes. The simulation performances of the three typical combination schemes were examined and compared in groups that were divided according to elevation and land cover. The results demonstrated that the simulation results of snow depth and snow water equivalent (SWE) were sensitive to four of the eleven physics options within Noah-MP. The exclusion of the parameterization schemes that notably reduced the simulation performance in the sensitive physical processes can significantly reduce uncertainty. Snow simulation performances of three typical combination schemes were diverse in northern Xinjiang, China; no single scheme performed best at all sites, but the length of the snow melting phase exhibited the best performance.

Although Golgi protein 73 (GP73) has been widely evaluated for diagnosing hepatocellular carcinoma (HCC) and other liver diseases in recent decade, its serum profile of patients with hepatitis B virus (HBV)-associated acute-on-chronic liver failure (HBV-ACLF) is still unknown. This study was designed to evaluate the serum levels of GP73 in patients with HBV-ACLF. The participants included 200 apparently healthy controls; 200 patients with chronic hepatitis B (CHB); 200 patients with HCC; 210 patients with HBV-ACLF, in which 29 HBV-ACLF patients were followed up for 3 months. All patients were Hepatitis B virus surface antigen (HBsAg) positive. The concentrations of GP73 in patients with HBV-ACLF (285.3 ± 128.5 ng/mL) were markedly higher than those HCC patients (159.1 ± 105.8 ng/mL), CHB patients (64.65 ± 44.99 ng/mL), and healthy controls (35.37 ± 12.41 ng/mL). When the cut-off value was set at 182.1 ng/mL, the sensitivity and specificity of HBV-ACLF diagnosis were 77.62% (95% confidence interval [CI]: 71.37%-83.07%) and 95.50% (95% CI: 92.27%-98.26%), respectively. If serum GP73 concentration was still above 361.6 ng/mL after 14 days of follow-up, the patient's prognosis may be depressed. Serum GP73 may be used to diagnosis HBV-ACLF in population with chronic HBV infections.

Precise glacier information is important for assessing climate change in remote mountain areas. To obtain more accurate glacier mapping, rough set theory, which can deal with vague and uncertainty information, was introduced to obtain optimal knowledge rules for glacier mapping. Optical images, thermal infrared band data, texture information and morphometric parameters were combined to build a decision table used in our proposed rough set theory method. After discretizing the real value attributes, decision rules were calculated through the decision rule generation algorithm for glacier mapping. A decision classifier based on the generated rules classified the multispectral image into glacier and non-glacier areas. The result of maximum likelihood classification (MLC) was used to compare with the result of the classification based on the rough set theory. Confusion matrix and visual interpretation were used to evaluate the overall accuracy of the results of the two methods. The accuracies of the rough set method and maximum likelihood classification were compared, yielding overall accuracies of 94.15% and 93.88%, respectively. It showed the area difference based on rough set was smaller by comparing the glacier areas of the rough set method and MLC with visual interpreter, respectively. The high accuracy for glacier mapping and the small area difference for glacier based on rough set theory demonstrated that this method was effective and promising for glacier mapping.