Yunnan Normal University

Fires, amplified by drought and heat, are increasingly threatening the ecologically fragile Mongolian Plateau (MP). Because of the need for a better understanding of fire dynamics in terms of their characteristics, especially on a diurnal time scale, we quantified spatiotemporal variability of fires and their sensitivity to daily meteorological variations on the MP using satellite observations and meteorological data over the past two decades. We found that forest steppe and grasslands on the eastern and northeastern MP feature the most prominent fire extents and intensities, with extreme fire years occurring approximately once per decade. Furthermore, in Inner Mongolia, fire frequency showed an increasing trend, while the average fire size declined with fluctuations (mean fire size: 2.24 km²). Conversely, Mongolia exhibited the opposite pattern, with a decrease in fire occurrence but an increase in fire size (mean fire size: 29.46 km²). Also, China-Mongolia transboundary fires increased overall, but with marked cross-border differences. In Inner Mongolia, transboundary fires mainly occurred within 5 km of the border, exhibiting declining trends. Conversely, transboundary fires on the Mongolian side displayed an area increase with distance from the border, stabilizing beyond 30 km. Extreme fire events across the MP were predominately driven by hot, dry climatic conditions, yet exhibited divergent ecosystem-dependent influences. Forest ecosystems on the MP showed a 2.02% nonlinear increase in burned area per W/m² rise in solar radiation, with precipitation suppressing it by 8.6%/mm. Steppe ecosystems in the Mongolian Plateau displayed high sensitivity to temperature, with 18.5 and 17.3% nonlinear increases in burned area per °C rise in maximum and minimum temperatures, respectively, whereas vapor pressure deficit induced a 9.42% area expansion per hPa increase.

Accurate subtyping of lung cancer is essential for improving patient prognosis and enabling personalized treatment. However, current clinical techniques are often time-consuming and heavily dependent on the operator's subjective judgment and experience, which limits the accuracy and timeliness of intraoperative subtype diagnosis and margin assessment. In this study, we developed an intelligent diagnostic model by integrating Fourier transform infrared (FTIR) spectroscopy with a Random Forest (RF) classifier. A total of 210 tumor and adjacent tissue samples from 105 patients, including adenocarcinoma, squamous cell carcinoma, and benign lung tumors were analyzed. The constructed RF model achieved an accuracy of 97.95% with an Area Under the Curve (AUC) of 0.99 in binary classification (lung cancer vs. adjacent tissues), and an accuracy of 94.91% in multiclass classification of lung cancer subtypes, significantly outperforming conventional algorithms such as Support Vector Machine, Naive Bayes, and Logistic Regression. In addition, spectral analysis methods, including peak area comparison, peak fitting, and second derivative analysis, revealed distinct differences in nucleic acids, proteins, and lipids, highlighting the characteristic bands responsible for subtype discrimination and providing spectroscopic insights into the pathological features of different lung cancer subtypes. Collectively, our findings demonstrate that the diagnostic model is a powerful approach for distinguishing lung cancer tissues from normal tissues and for subtype classification, offering a promising tool for lung cancer diagnosis.

The gut microbiota plays a central role in the health of social wasps, contributing to nutrition, immunity, and environmental adaptation. However, its role in disease occurrence and host responses in the hornet Vespa magnifica remains poorly understood. Here, we investigated the gut microbial composition and host transcriptomic responses in diseased and healthy V. magnifica from Yunnan, China. High-throughput 16S rRNA gene sequencing revealed that diseased individuals exhibited significantly reduced microbial richness and diversity, characterized by depletion of beneficial taxa such as Lactobacillus (Lb.), Leuconostoc (Leu.), and Bifidobacterium (B.), alongside increased abundance of potential pathobionts including Lactococcus (Lc.) and Yokenella (Y.). Transcriptomic analysis identified 32 differentially expressed genes (DEGs), some of which were enriched for immune signaling pathways. Key immune-related genes, including peptidoglycan recognition proteins (PGRPs) and defensins (DEF), were strongly upregulated, indicating activation of host immune recognition and effector responses. Mantel correlation analysis further revealed significant associations between the top 30 differentially abundant bacterial genera and host immune genes, highlighting microbiota-host interactions in disease development. Collectively, these findings demonstrate that gut dysbiosis is associated with microbial imbalance and immune activation in V. magnifica, providing new insights into its gut health and potential strategies for disease management in hornet farming.