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Snow depth (SD) provides information on the spatial distribution of snow cover, which is critical to assess water resources and global climate change. Currently, SD can be obtained from passive microwave radiometers, reanalysis models, and in-situ observations. However, the SD data produced from different methods have poor performance in completeness and consistency, and it is difficult to meet the needs of related scientific research. In this study, we developed an SD fusion method based on the random forest algorithm (RF) and used it to generate the SD spatial distribution over China from 2014 to 2018. This method can combine the information from multiple sources of SD data (ground-based, satellite-derived, and reanalysis) to improve the representation of the spatiotemporal distribution of SD. Five SD products (WESTDC, ERA-Interim, CMC, GLDAS-NOAH, and MERRA2) were adopted as input data for constructing the model. In addition, the fusion model was built with consideration of ancillary information (e.g., land cover types, forest cover fraction, geographical information, land cover heterogeneity, surface roughness, and snow class). We evaluated the error of merged SD (RF-SD) data and five SD products against in-situ observations in detail under different land cover types, forest cover fractions, land cover heterogeneity, surface roughness, and snow classification. The results showed that RF-SD data improved the accuracy of SD estimates over China, and increased the Kling-Gupta efficiency (KGE) from 0.21 to 0.64 to 0.73 and lower RMSE (5.1 cm) when compared with five original SD datasets. This method can be effective for integrating the advantages of each SD data source, improving the accuracy of SD estimation and reducing inconsistency among multi-source SD datasets.

Knowledge of grassland classification in a timely and accurate manner is essential for grassland resource management and utilization. Although remote sensing imagery analysis technology is widely applied for land cover classification, few studies have systematically compared the performance of commonly used methods on semi-arid native grasslands in northern China. This renders the grassland classification work in this region devoid of applicable technical references. In this study, the central Xilingol (China) was selected as the study area, and the performances of four widely used machine learning algorithms for mapping semi-arid grassland under pixel-based and object-based classification methods were compared: random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and naive Bayes (NB). The features were composed of the Landsat OLI multispectral data, spectral indices, Sentinel SAR C bands, topographic, position (coordinates), geometric, and grey-level co-occurrence matrix (GLCM) texture variables. The findings demonstrated that (1) the object-based methods depicted a more realistic land cover distribution and had greater accuracy than the pixel-based methods; (2) in the pixel-based classification, RF performed the best, with OA and Kappa values of 96.32% and 0.95, respectively. In object-based classification, RF and SVM presented no statistically different predictions, with OA and Kappa exceeding 97.5% and 0.97, respectively, and both performed significantly better than other algorithms. (3) In pixel-based classification, multispectral bands, spectral indices, and geographic features significantly distinguished grassland, whereas, in object-based classification, multispectral bands, spectral indices, elevation, and position features were more prominent. Despite the fact that Sentinel 1 SAR variables were chosen as an effective variable in object-based classification, they made no significant contribution to the grassland distinction.

The desert steppe serves as a transitional zone between grasslands and deserts, and long-term monitoring of aboveground biomass (AGB) in the desert steppe is essential for understanding grassland changes. While AGB observation techniques based on multisource remote-sensing data and machine-learning algorithms have been widely applied, research on monitoring methods specifically for the desert steppe remains limited. In this study, we focused on the desert steppe of Inner Mongolia, China, as the study area and used field sampling data, MODIS data, MODIS-based vegetation indices (VI), and environmental factors (topography, climate, and soil) to compare the performance of four commonly used machine-learning algorithms: multiple linear regression (MLR), partial least-squares regression (PLS), random forest (RF), and support vector machine (SVM) in AGB estimation. Based on the optimal model, the spatial–temporal characteristics of AGB from 2000 to 2020 were calculated, and the driving forces of climate change and human activities on AGB changes were quantitatively analyzed using the random forest algorithm. The results are as follows: (1) RF demonstrated outstanding performance in terms of prediction accuracy and model robustness, making it suitable for AGB estimation in the desert steppe of Inner Mongolia; (2) VI contributed the most to the model, and no significant difference was found between soil-adjusted VIs and traditional VIs. Elevation, slope, precipitation, and temperature all had positive effects on the model; (3) from 2000 to 2020, the multiyear average AGB in the study area was 58.34 g/m2, exhibiting a gradually increasing distribution pattern from the inner region to the outer region (from north to south); (4) from 2000 to 2020, the proportions of grassland with AGB slightly and significantly increasing trend in the study area were 87.08% and 5.13%, respectively, while the proportions of grassland with AGB slightly and significantly decreasing trend were 7.76% and 0.05%, respectively; and (5) over the past 20 years, climate change, particularly precipitation, has been the primary driving force behind AGB changes of the study area. This research holds reference value for improving desert steppe monitoring capabilities and the rational planning of grassland resources.

Background Acute pulmonary embolism (APE) is a critical disease with a high mortality rate, some of the surviving patients may develop chronic thromboembolic pulmonary disease (CTEPD), which affects the patient’s prognosis. However, the research on the early diagnosis of CTEPD is limited. This study aimed to establish a prediction model for earlier identification of CTEPD. Methods This prospective study included 464 consecutive patients with APE confirmed between January 2020 and September 2023, at 7 centers from China. After follow-up for at least 3 months, the patients were divided into the CTEPD and non-CTEPD groups based on symptoms and computed tomography pulmonary angiography (CTPA) or pulmonary ventilation perfusion (V/Q) scans showing residual thrombosis. The independent risk factors for CTEPD were identified via univariate and multivariate logistic regression analyses. Next, a nomogram of predictive model was established, and validation was completed via decision curve analysis (DCA) and receiver operating characteristic curve analysis. Result In total, 130 (28%) patients presented with CTEPD, 17% (22/130) of CTEPD patients developed chronic thromboembolic pulmonary hypertension (CTEPH). Based on the multivariate analysis, a time interval from symptoms onset to diagnosis (time-to-diagnosis) ≥ 15 days (95% confidence interval [CI]: 3.392–14.972, p  < 0.001), recurrent pulmonary embolism (RPE) (95%CI: 1.560–17.300, p  = 0.007), right ventricular dysfunction (RVD) (95%CI: 1.042–6.437, p  = 0.040), central embolus (95%CI: 1.776–7.383, p  < 0.001) and residual pulmonary vascular obstruction (RPVO) > 10% (95%CI: 4.884–21.449, p  < 0.001) were identified as the independent predictors of CTEPD. Then, A prediction model with a C-index of 0.895 (95% CI 0.863–0.927) was established for high-risk patients. The nomogram had an excellent predictive performance for earlier identification of CTEPD, with an area under the curve of 0.908 (95%CI: 0.875–0.941) in the training cohort and 0.875 (95%CI: 0.803–0.947) in the validation cohort. Conclusion The current study established and validated a reliable nomogram for predicting CTEPD, which would assist clinicians identify the high-risk patients for CTEPD earlier.

Pulmonary embolism (PE) is a significant contributor to mortality in patients with cancer. Although anticoagulation serves as the cornerstone of treatment for cancer-associated PE, it has not been emphasized in real-world settings. The aim of this study was to examine the impact of suboptimal anticoagulant treatment on the prognosis of cancer-associated PE. A cohort of 356 individuals newly diagnosed with acute PE were enrolled. The primary outcome of the study was recurrent venous thromboembolism (VTE), and the secondary outcomes were all-cause mortality and major bleeding (consisting of a reduction in the hemoglobin level by at least 20 g/L, transfusion of at least 2 units of blood, or symptomatic bleeding in a critical area or organ or fatal bleeding). Of the total participants, 156 (43.8%) were diagnosed with cancer. A comparison between the cancer and noncancer groups revealed that patients with cancer were more frequently asymptomatic (41.0% vs 4.5%; P < 0.001), less likely to have right ventricular dysfunction (4.5% vs 14.0%; P = 0.001), received less anticoagulant treatment during hospitalization (85.3% vs 98.5%; P < 0.001), and had a shorter duration of anticoagulation (5.02 [7.40] months vs 14.19 [10.65] months; P < 0.001). In addition, patients with cancer were found to be at a higher risk of recurrent VTE (17.3% vs 4.0%; P < 0.001) and all-cause mortality (23.7% vs 10.5%; P = 0.001). Multiple Cox regression analysis indicated that discontinuation of anticoagulation at 3 months was a significant risk factor for recurrent VTE in the cancer group (HR, 15.815; 95% CI, 3.047–82.079; P = 0.001). The brief duration of anticoagulation therapy and elevated likelihood of recurrent VTE serve as cautionary indicators for the need to enhance awareness of standardized anticoagulant treatment for cancer-associated PE. The ultimate goal is to enhance patient prognosis and quality of life.

The present study aimed to explore the clinical significance of neutrophils infiltration and carcinoembryonic antigen related cell adhesion molecule 1 (CEACAM1) expression in the tongue squamous cell carcinoma (TSCC), and to probe the possible relationship between them. Tissue microarray and immunohistochemistry were used to detect neutrophils density and CEACAM1 expression in 74 cases of primary TSCC specimens and 17 cases of corresponding peritumoral tissues. The relationship of CEACAM1 expression and neutrophils density with clinicopathologic parameters and cancer-related survival of TSCC patients were evaluated. The correlation between CEACAM1 expression and neutrophils density was also evaluated. Real-time quantitative transcription polymerase chain reaction (qRT-PCR) was used to explore the possible molecular mechanisms between CEACAM1 expression and neutrophils infiltration. Immunohistochemistry evaluation revealed that there was more neutrophils infiltration in TSCC tissues than in peritumoral tissues. High neutrophil density was associated with LN metastasis (P=0.01), higher clinical stage (P=0.037) and tumor recurrence (P=0.024). CEACAM1 overexpression was also associated with lymph node metastasis (P=0.000) and higher clinical stage (P=0.001). Survival analysis revealed that both neutrophils infiltration and CEACAM1 overexpression were associated with poorer cancer-related survival of TSCC patients (P<0.05), and neutrophils infiltration was an independent prognostic factor for TSCC (P<0.05). Furthermore, overexpression of CEACAM1 was correlated with more neutrophils infiltration in TSCC tissues (P<0.01). qRT-PCR results showed that CEACAM1-4L can upregulate the mRNA expression of IL-8 and CXCL-6, which were strong chemotactic factors of neutrophils. Our results demonstrated that more neutrophils infiltration and overexpression of CEACAM1 were associated with poor clinical outcomes in TSCC tissues. Overexpression of CEACAM1 on tumor cells correlated with more neutrophils infiltration to some extent through upregulating mRNA expression of IL-8 and CXCL-6.

Global warming and human activities are complicating the spatial and temporal relationships between basin hydrologic processes and ecosystem quality (EQ), especially in arid and semi-arid regions. Knowledge of the synergy between hydrological processes and ecosystems in arid and semi-arid zones is an effective measure to achieve ecologically sustainable development. In this study, the inland river basin Ulagai River Basin (URB), a typical arid and semi-arid region in Northern China, was used as the study area; based on an improved hydrological model and remote-sensing and in situ measured data, this URB-focused study analyzed the spatial and temporal characteristics of hydrological process factors, such as precipitation, evapotranspiration (ET), surface runoff, lateral flow, groundwater recharge, and EQ and the synergistic relationships between them. It was found that, barring snowmelt, the hydrological process factors such as precipitation, ET, surface runoff, lateral flow, and groundwater recharge had a rising trend in the URB, since the 20th century. The rate of change was higher in the downstream areas when compared with what it was in the upstream and midstream areas. The multi-year average of EQ in the basin is 53.66, which is at a medium level and has an overall improving trend, accounting for 95.14% of the total area, mainly in the upstream, downstream southern, and downstream northern areas of the basin. The change in relationship between the hydrological process factors and EQ was found to have a highly synergistic effect. Temporally, EQ was consistent with the interannual trends of precipitation, surface runoff, lateral flow, and groundwater recharge. The correlation between the hydrological process factors and EQ was found to be higher than 0.7 during the study period. Spatially, the hydrological process factors had a synergistic relationship with EQ from strong to weak upstream, midstream, and downstream, respectively. In addition, ecosystem improvements were accelerated by government initiatives such as the policy of Returning Grazing Land to Grassland Project, which has played an important role in promoting soil and water conservation and EQ. This study provides theoretical support for understanding the relationship between hydrological processes and ecological evolution in arid and semi-arid regions, and it also provides new ideas for related research.

To further improve the road performance of waterborne epoxy resin, it was prepared by using the phase inversion method. The tensile properties, bending properties, impact resistance, and storage stability of waterborne epoxy resin were determined. The bonding properties of waterborne epoxy resin were analyzed. At the same time, their properties were compared with those of waterborne epoxy resin prepared by using the curing agent emulsification method. The performance of waterborne epoxy resin was comprehensively evaluated based on multi-index grey target decision model. The results show that the optimum preparation parameters for the preparation of waterborne epoxy resin by phase inversion method are shear time 1.5 h, shear temperature 60°C, and shear rate 1300–1500 r/min. The suitable contents of emulsifier A and B are 18% and 16%, respectively. The tensile strength, elongation at break, bending strength, bending deformation, and impact strength of waterborne epoxy resin prepared by emulsifier A can reach 34.46 MPa, 12.96%, 85.37 MPa, 19.42 mm, and 15.66 kJ/m2, respectively. It shows improved mechanical strength, deformation ability, impact resistance, and bonding performance. The comprehensive properties of waterborne epoxy resin prepared by emulsifier A are the best. It is suggested to use phase inversion method to prepare waterborne epoxy resin for roads.

Agricultural flood disaster risk assessment plays a vital role in agricultural flood disaster risk management. Extreme precipitation events are the main causes of flood disasters in the Midwest Jilin province (MJP). Therefore, it is important to analyse the characteristics of extreme precipitation events and assess the flood risk. In this study, the Multifractal Detrended Fluctuation Analysis (MF-DFA) method was used to determine the threshold of extreme precipitation events. The total duration of extreme precipitation and the total extreme precipitation were selected as flood indicators. The copula functions were then used to determine the joint distribution to calculate the bivariate joint return period, which is the flood hazard. Historical data and flood indicators were used to build an agricultural flood disaster vulnerability surface model. Finally, the risk curve for agricultural flood disasters was established to assess the flood risk in the MJP. The results show that the proposed approaches precisely describe the joint distribution of the flood indicators. The results of the vulnerability surface model are in accordance with the spatiotemporal distribution pattern of the agricultural flood disaster loss in this area. The agricultural flood risk of the MJP gradually decreases from east to west. The results provide a firm scientific basis for flood control and drainage plans in the area.

Due to climate change and global warming, the frequency of sandstorms in northern China is increasing. Stipa breviflora, a dominant species in Eurasian grasslands, can help prevent desertification from becoming more serious. Studies on S. breviflora cover a wide range of fields. To the best of our knowledge, the present study is the first to sequence, assemble, and annotate the S. breviflora genome. In total, 2,781,544 contigs were assembled, and 2,600,873 scaffolds were obtained, resulting in a total length of 649,849,683 bp. The number of scaffolds greater than 1 kb was 70,770. We annotated the assembled genome (>121 kb), conducted a selective sweep analysis, and ultimately succeeded in assembling the Matk gene of S. breviflora. More importantly, our research identified 26 scaffolds that may be responsible for the drought tolerance of S. breviflora Griseb. In summary, the data obtained regarding S. breviflora will be of great significance for future research.