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Forest fires lead to permafrost degradation and localized drought, and regional droughts increase the probability of forest fires, leading to a positive feedback loop between climate change and fires. However, the relationship between fire occurrence and climatic factors change is unclear for boreal forests, which represent the largest land-based biome and stock of carbon. Here, we analyzed the relationship between lightning fire occurrence and meteorological and topographic factors based on the fire frequency, burned area, and meteorological data from the primeval forest region of the northern Daxing’an Mountains in China. We found that lightning fires occurred most frequently at an altitude of 600 to 700 m. From 1999 to 2019, the frequency of lightning fires showed an overall upward trend, whereas the affected area had no obvious change. It can be attributed to fire suppression efforts and greatly increased investment in fire prevention in China. Snow cover had a strong regulatory effect on the start and end dates of lightning fires for seasonal cycle. The frequency of lightning fires was positively correlated with the average temperature, maximum temperature, and surface evaporation and negatively correlated with precipitation and surface soil moisture (0–10 cm). The result will be useful in the spatially assessment of fire risk, the planning and coordination of regional efforts to identify areas at greatest risk, and in designing long-term lightning fires management strategies.

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.

Crown fires are a major disturbance in boreal and cold–temperate forests worldwide, threatening both ecosystems and human activities. The Da Xing’anling Mountains of Northeast China exemplify these dangers due to their complex vegetation and high crown fire potential. Crown fire occurrence depends on vertical fuel continuity, fuel load, heating value, surface fire spread rate, and critical fireline intensity. However, many assessments rely on single-factor metrics or low-adaptability simulations. This study developed a Potential Canopy Fire Danger Index (PCDI) that integrates five parameters—fuel vertical distribution continuity index, fuel loading, heating value, surface fire rate of spread, and critical fireline intensity—based on field surveys and combustion tests. Pinus pumila (Regel, 1861), with its dense shrub layer, showed the highest PCDI values (0.502, 0.583 and 0.527), whereas other forest types generally fell in the low to low–moderate range (0.350–0.450), with ≈75% of plots within these classes. Surface fire spread rate correlated most strongly with PCDI, followed by vertical fuel continuity, heating value, and fuel load; critical fireline intensity had minimal influence. The elevated hazard in P. pumila reflects its structural and fuel characteristics, while other forest types present comparatively lower dangers. Model checks indicated high stability and agreement with BehavePlus 6.0 scenarios, with the PCDI showing the lowest sample SD. The PCDI provides a quantitative framework for assessing crown fire danger in cold–temperate forests and supports targeted mitigation—prioritizing P. pumila while employing cost-effective maintenance in low-danger forest types.

Ecological zoning is essential for optimizing regional ecological management and improving environmental protection efficiency. While previous studies have primarily focused on the independent analysis of land use intensity (LUI) and landscape ecological risk (LER), there has been limited research on their coupled relationship. This study, conducted in the Western Jilin (WJL), introduces an innovative ecological zoning method based on the Production–Living–Ecological Space (PLES) framework, which explores the interactions between LUI and LER, filling a gap in existing research. The method employs a coupling coordination degree (CCD) model and Geographic Information System (GIS) technology to construct an LUI-ERI coupling model, which is used to delineate ecological zones. The results indicate that: (1) The PLES in the study area is predominantly production space (PS), with the largest area of transfer being production ecological space (PES) 2784.23 km2, and the most significant transfer in being PS 3112.33 km2. (2) Between 2000 and 2020, both LUI and LER exhibited downward trends, with opposite spatial distribution characteristics. The “middle” intensity zone and “highest” risk zone were the dominant LUI and LER types, covering approximately 46% and 45% of the total area, respectively. (3) The coupling coordination degree between LUI and LER showed a polarized trend, with an overall upward trajectory from 2000 to 2020. (4) The ecological zoning of the WJL can be categorized into an ecological core protection (ECP) zone, ecological potential governance (EPG) zone, ecological comprehensive monitoring (ECM) zone, ecological optimization (EO) zone, and ecological restoration (ER) zone, with the ecological core protection area occupying 61.63% of the total area. This study provides a novel perspective on ecological zoning and offers a systematic scientific basis for regional ecological management and spatial planning.

Conversational search provides a more convenient interface for users to search by allowing multi-turn interaction with the search engine. However, the effectiveness of the conversational dense retrieval methods is limited by the scarcity of training data required for their fine-tuning. Thus, generating more training conversational sessions with relevant labels could potentially improve search performance. Based on the promising capabilities of large language models (LLMs) on text generation, we propose ConvSDG, a simple yet effective framework to explore the feasibility of boosting conversational search by using LLM for session data generation. Within this framework, we design dialogue/session-level and query-level data generation with unsupervised and semi-supervised learning, according to the availability of relevance judgments. The generated data are used to fine-tune the conversational dense retriever. Extensive experiments on four widely used datasets demonstrate the effectiveness and broad applicability of our ConvSDG framework compared with several strong baselines.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with poor survival rates. The presence of cancer stem-like cells (CSCs) is believed to be among the underlying reasons for the aggressiveness of PDAC, which contributes to chemoresistance and recurrence. However, the mechanisms that induce chemoresistance and inhibit apoptosis remain largely unknown. We used serum-free medium to enrich CSCs from panc-1 human pancreatic cancer cells and performed sphere formation testing, flow cytometry, quantitative reverse transcription polymerase chain reaction (RT-qPCR) and semi-quantitative western blotting to confirm the stemness of panc-1 CSCs. Hallmarks of endoplasmic reticulum (ER) stress, including IRE1, PERK, ATF4, ATF6α, GRP78 and uPA expression, were detected after gemcitabine treatment. Effects of gemcitabine-induced uPA expression on cell invasion, sphere formation, colony formation and gemcitabine sensitivity were detected. Electrophoretic mobility shift assays (EMSAs) and RNA-immunoprecipitation (RIP) were performed to detect interaction between the uPA mRNA 3'-UTR and mutant p53-R273H expressed by panc-1 CSCs. The effects of upregulated uPA by gemcitabine on apoptosis were detected by Annexin V-FITC/PI staining, and the impact of uPA on small molecule CP-31398-restored mutant p53 transcriptional activity was measured by a luciferase reporter assay. Enriched panc-1 CSCs expressing high levels of CD44 and CD133 also produced significantly higher amounts of Oct4 and Nanog. Compared with panc-1 cells, panc-1 CSCs presented chemoresistance to gemcitabine. ER stress gene detections demonstrated effects of gemcitabine-induced ER stress on both the pro-apoptotic and pro-survival branches. ER stress-induced ATF6α upregulated level of uPA by transcriptionally activating GRP78. Gemcitabine-induced uPA promoted invasion, sphere formation and colony formation and attenuated apoptosis induced by gemcitabine in panc-1 CSCs, depending on interaction with mutant p53-R273H. Upregulation of uPA abolished CP-31398-mediated restoration of mutant p53 transcriptional activity in panc-1 CSCs. Gemcitabine treatment induced ER stress and promoted mutant p53-R273H stabilization via transcriptionally activated uPA which may contribute to chemoresistance to gemcitabine. Notably, upregulation of uPA by gemcitabine treatment may lead to the failure of CP-31398; thus, a novel strategy for modulating mutant p53 function needs to be developed.

Background Pancreatic ductal adenocarcinoma (PDAC) is a fatal disease with molecular heterogeneity, inducing differences in biological behavior, and therapeutic strategy. NGS profiles of pathogenic alterations in the Chinese PDAC population are limited. We conducted a retrospective study to investigate the predictive role of DNA damage repair (DDR) mutations in precision medicine. Methods The NGS profiles were performed on resected tissues from 195 Chinese PDAC patients. Baseline clinical or genetic characteristics and survival status were collected. The Kaplan–Meier survival analyses were performed by the R version 3.6.1. Results The main driver genes were KRAS, TP53, CDKN2A, and SMAD4. Advanced patients with KRAS mutation showed a worse OS than KRAS wild-type (p = 0.048). DDR pathogenic deficiency was identified in 30 (15.38%) of overall patients, mainly involving BRCA2 (n = 9, 4.62%), ATM (n = 8, 4.10%) and RAD50 genes (n = 3, 1.54%). No significance of OS between patients with or without DDR mutations (p = 0.88). But DDR mutation was an independent prognostic factor for survival analysis of advanced PDAC patients (p = 0.032). For DDR mutant patients, treatment with platinum-based chemotherapy (p = 0.0096) or olaparib (p = 0.018) respectively improved the overall survival. No statistical difference between tumor mutation burden (TMB) and DDR mutations was identified. Treatment of PD-1 blockades did not bring significantly improved OS to DDR-mutated patients than the naive DDR group (p = 0.14). Conclusions In this retrospective study, we showed the role of germline and somatic DDR mutation in predicting the efficacy of olaparib and platinum-based chemotherapy in Chinese patients. However, the value of DDR mutation in the prediction of hypermutation status and the sensitivity to the PD-1 blockade needed further investigation.

Advanced pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis. Immunotherapy alone offers limited efficacy, but it is still unknown whether its combination with chemotherapy could offer synergistic anti-tumor effects. This phase Ib/II study evaluated the safety and efficacy of combining toripalimab with the gemcitabine plus nab-paclitaxel (GnP) regimen as first-line treatment for locally advanced or metastatic PDAC and explored predictive biomarkers (ChiCTR2000032293). The primary endpoints were safety and overall survival (OS). The secondary outcomes were objective response rate (ORR), disease control rate (DCR), and progression-free survival (PFS). Immune-related biomarkers including programmed death-ligand 1 (PD-L1) expression, genetic status, cytokine levels, and spatial features of the tumor immune microenviroment (TIME) were investigated. Neither serious treatment-related adverse events nor grade 4 immune-related adverse events were reported. Among the 72 patients, the median OS was 8.9 months, 12-month OS rate was 31.9%, with median PFS of 5.6 months, ORR of 33.3%, and DCR of 90.3%. Higher PD-L1 expression, without liver metastases were associated with higher ORR, however these factors could not effectively distinguish responders and non-responders. Importantly, dendritic cells - T helper cells - cytotoxic T lymphocytes (DC-Th-CTL) enriched immune niche and their spatial interactions were dominant predictors of response based on TIME analysis using a cyclic multiplex tissue staining assay, with an area under the curve value of 0.8. Overall, GnP plus toripalimab exhibited good safety and differentiated efficacy in selected population, and the spatial interactions of DC-Th-CTL represent promising predictors to efficacy of immunochemotherapy in locally advanced or metastatic PDAC.

Background: In 2017, the World Health Organization (WHO) defined a new category of pancreatic neuroendocrine neoplasms named G3 pancreatic neuroendocrine tumors (p-NETs), whose surgical outcomes, long-term survivals and staging systems have not been well documented. Methods: Data from eligible patients with G3 p-NETs defined using the WHO 2017 grading classification at our institute were retrospectively analyzed. Results: Our study enrolled 80 patients with WHO G3 p-NETs, including 50 women and 30 men. The accumulative 5-year overall survival (OS) of G3 p-NETs was 29.7%. The current staging system by the American Joint Committee on Cancer (AJCC) failed to discriminate the survival difference between Stage II and Stage III (p = 0.172), while notable differences with regard to the OS were statistically offered between each stage using the modified tumor–node–metastasis (mTNM) staging system (all p < 0.05). The OS of patients receiving surgical resection was significantly better than those with palliative operation (p < 0.05). Both the current AJCC system and proposed mTNM system were independent predictors for the OS of G3 p-NETs (p = 0.017 and p = 0.032, respectively). The 95% confidence intervals of the proposed mTNM staging system were smaller than that of the current AJCC system (0.626–8.217 and 0.329–10.013, respectively), indicating a relatively more accurate predictive ability. Conclusion: Our demonstration revealed that surgical resection was an independent predictor for the favorable prognosis of patients with G3 p-NETs. Moreover, the new mTNM staging system was more suitable and practical than the current AJCC system for stratifying G3 p-NETs into prognostic groups.