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Drought is one of the most complex and least-understood environmental disasters that can trigger environmental, societal, and economic problems. To accurately assess the drought conditions in the Yellow River Basin, this study reconstructed the Land Surface Temperature (LST) using the Annual Temperature Cycle (ATC) model and the Normalized Difference Vegetation Index (NDVI). The Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Temperature-Vegetation Drought Index (TVDI), which are four typical remote sensing drought indices, were calculated. Then, the air temperature, precipitation, and soil moisture data were used to evaluate the applicability of each drought index to different land types. Finally, this study characterized the spatial and temporal patterns of drought in the Yellow River Basin from 2003 to 2019. The results show that: (1) Using the LST reconstructed by the ATC model to calculate the drought index can effectively improve the accuracy of drought monitoring. In most areas, the reconstructed TCI, VHI, and TVDI are more reliable for monitoring drought conditions than the unreconstructed VCI. (2) The four drought indices (TCI, VCI, VH, TVDI) represent the same temporal and spatial patterns throughout the study area. However, in some small areas, the temporal and spatial patterns represented by different drought indices are different. (3) In the Yellow River Basin, the drought level is highest in the northwest and lowest in the southwest and southeast. The dry conditions in the Yellow River Basin were stable from 2003 to 2019. The results in this paper provide a basis for better understanding and evaluating the drought conditions in the Yellow River Basin and can guide water resources management, agricultural production, and ecological protection of this area.

Double cropping is an important cropping system in China, with more than half of China’s cropland adopting the practice. Under the background of global climate change, agricultural policies, and changing farming practices, double-cropping area has changed substantially. However, the spatial-temporal dynamics of double cropping is poorly understood. A better understanding of these dynamics is necessary for the northern limit of double cropping (NLDC) to ensure food security in China and the world and to achieve zero hunger, the second Sustainable Development Goal (SDG). Here, we developed a phenology-based algorithm to identify double-cropping fields by analyzing time-series Moderate Resolution Imaging Spectroradiometer (MODIS) images during the period 2000–2020 using the Google Earth Engine (GEE) platform. We then extracted the NLDC using the kernel density of pixels with double cropping and analyzed the spatial-temporal dynamics of NLDC using the Fishnet method. We found that our algorithm accurately extracted double-cropping fields, with overall, user, and producer accuracies and Kappa coefficients of 95.97%, 96.58%, 92.21%, and 0.91, respectively. Over the past 20 years, the NLDC generally trended southward (the largest movement was 66.60 km) and eastward (the largest movement was 109.52 km). Our findings provide the scientific basis for further development and planning of agricultural production in China.

A record-breaking precipitation event, with a maximum 24-h (1-h) precipitation of 624 mm (201.9 mm) observed at Zhengzhou Weather Station, occurred in Henan Province, China, in July 2021. However, all global operational forecast models failed to predict the intensity and location of maximum precipitation for this event. The unexpected heavy rainfall caused 398 deaths and 120.06 billion RMB of economic losses. The high-societal-impact of this event has drawn much attention from the research community. This article provides a research review of the event from the perspectives of observations, analysis, dynamics, predictability, and the connection with climate warming and urbanization. Global reanalysis data show that there was an anomalous large-scale circulation pattern that resulted in abundant moisture supply to the region of interest. Three mesoscale systems (a mesoscale low pressure system, a barrier jet, and downslope gravity current) were found by recent high-resolution model simulation and data assimilation studies to have contributed to the local intensification of the rainstorm. Furthermore, observational analysis has suggested that an abrupt increase in graupel through microphysical processes after the sequential merging of three convective cells contributed to the record-breaking precipitation. Although these findings have aided in our understanding of the extreme rainfall event, preliminary analysis indicated that the practical predictability of the extreme rainfall for this event was rather low. The contrary influences of climate warming and urbanization on precipitation extremes as revealed by two studies could add further challenges to the predictability. We conclude that data sharing and collaboration between meteorological and hydrological researchers will be crucial in future research on high-impact weather events.

Understanding how hailstorm trends have changed in the context of climate change is a persistent challenge, mainly because of the lack of long-term consistent observations of hailstorms. Here, we leverage hail damage records from Chinese historical books and extend hailstorm records to approximately 2890 years ago, exploring variations in the number of hailstorm days between 1500 and 1949 based on reliable and consistent data. We show that the number of hailstorm days was constant before 1850, but has increased significantly afterwards. This increase in hailstorm days seems to be associated with the increase in surface temperature after the population effect is removed. In addition to the trend, hailstorm activity is found to display both quasicentennial and multidecadal variability, with the former (later) dominating before (after) the 1850s, driven by the Pacific Decadal Oscillation (PDO). These results suggest that long-term changes in hailstorm days in China are modulated by climate warming and natural variability, via the PDO. Future projections based on different climate change scenarios and a convolutional neural network model show a further increase in the number of hailstorm days in the 21 st century. The authors use historical hailstorm records in China dating back to 2980 years ago and show an increase in hailstorm days after 1850 attributed to global warming and internal climate variability. Future projections indicate further increases under sustained warming.

Primary myelofibrosis (PMF) is a heterogeneous bone marrow disorder, and substantial evidence indicates the involvement of inflammatory mediators in its progression. However, a diagnostic model based on inflammation-related genes has not yet been established. The aim of this study was to identify specific inflammation-related genes (IRGs) with potential value in myelofibrosis diagnosis and risk prediction. Transcriptomic data from the Gene Expression Omnibus (GEO) database were analysed to identify inflammation-related differentially expressed genes (DEGs). Machine learning approaches, including the least absolute shrinkage and selection operator (LASSO) and random forest, were used to select hub genes. A nomogram was constructed and validated externally using independent GEO datasets and local sequencing data. Immune cell infiltration and functional enrichment were also investigated. HBEGF , TIMP1 and PSEN1 show significant differences in expression between normal individuals and those with PMF. A nomogram based on three genes was established to assess the risk of PMF in healthy individuals. The ROC curve revealed that the three hub genes have outstanding diagnostic value for this disease (AUC = 0.994; 95% CI: 0.985–1.000); the results were subsequently validated in an external validation set (AUC = 0.807; 95% CI: 0.723–0.891), and a sequencing dataset from the First Affiliated Hospital of Zhejiang University (AUC = 0.982; 95% CI: 0.841-1). Enrichment analyses implicated cancer-related and immune pathways, and the model genes correlated significantly with immune cell infiltration and function. We developed and validated a robust three gene diagnostic model for PMF based on inflammation-related genes, offering a noninvasive molecular tool with potential clinical utility for auxiliary diagnosis.

The rapid development of the web geographic information system (Web GIS) has promoted new vitality in high school geography education, relieved the stress of geography teachers caused by software and technical problems, and made it possible for teachers to devote more energy to geography teaching and research activities. Natural disaster education is not only an important part of the geography curriculum, but also an indispensable aspect of education for sustainable development (ESD) for high school students. The application of Web GIS in the dynamic monitoring, forecast, and early warning of natural disasters is becoming more experienced. Therefore, the application of Web GIS in natural disaster education is quite feasible. How to build a bridge between them is the purpose of this paper. Thus, the paper selects ArcGIS Online, which is not limited by time and space, and analyzes several functions that apply it to geography teaching. These include smart mapping, story maps, 3D web maps, and mobile GIS. Meanwhile, it analyzes the knowledge structure of “natural disasters” in Chinese geography textbooks to guide the subsequent case design. Then, the Web GIS inquiry-based teaching case is formed based on “7.20 Zhengzhou Torrential Rain”. It contains knowledge about natural disasters and designs from many aspects, such as the causes, manifestations, and prevention and control of disasters. The discussion identifies a range of specific educational benefits of applying Web GIS to natural disaster education for teachers and schools. Ultimately, it can provide some reference values for geography teachers and other developers to explore curriculum resources and create quality educational models.

Large language models (LLMs) have shown promising capabilities across diverse domains, yet their application to complex clinical prediction tasks remains limited. In this study, we present CARE-AD (Collaborative Analysis and Risk Evaluation for Alzheimer’s Disease), a multi-agent LLM-based framework for forecasting Alzheimer’s disease (AD) onset by analyzing longitudinal electronic health record (EHR) notes. CARE-AD assigns specialized LLM agents to extract signs and symptoms relevant to AD and conduct domain-specific evaluations—emulating a collaborative diagnostic process. In a retrospective evaluation, CARE-AD achieved higher accuracy (0.53 vs. 0.26–0.45) than baseline single-model approaches in predicting AD risk 10 years prior to the first recorded diagnosis code. These findings highlight the feasibility of using multi-agent LLM systems to support early risk assessment for AD and motivate further research on their integration into clinical decision support workflows.

Background Non-small cell lung cancer (NSCLC) is a prevalent form of cancer, often leading to brain metastases (BM) and a significant decline in patient prognosis. Whether immune checkpoint inhibitors (ICIs) combined with brain radiotherapy is superior to conventional chemotherapy combined with brain radiotherapy in those patients remains to be explored. Materials and methods Our study enrolled 161 NSCLC patients with BM who underwent either ICIs combined with brain radiotherapy or chemotherapy combined with brain radiotherapy. End points included overall survival (OS), progression-free survival (PFS), intracranial PFS (IPFS), and extracranial PFS (EPFS). Univariate and multivariate Cox regressions were employed to identify prognostic risk variables. Results Patients receiving ICIs combined with brain radiotherapy exhibited significantly longer OS compared to those receiving chemotherapy combined with brain radiotherapy (34.80 months vs. 17.17 months, P  = 0.005). In the Cox regression analysis, chemotherapy combined with brain radiotherapy (HR, 1.82; 95% CI, 1.09–3.05; P  = 0.023), smoking (HR, 1.75; 95% CI, 1.02–2.99; P  = 0.043) and squamous cell carcinoma (HR, 2.59; 95% CI, 1.31–5.13; P  = 0.006) were associated with a worse prognosis. After propensity score matching (PSM), this finding remained consistent with before PSM (43.73 months vs. 17.17 months, P  = 0.018). Squamous cell carcinoma (HR, 2.46; 95% CI, 1.15–5.26; P  = 0.021) and CT + RT (HR, 2.11; 95% CI, 1.15–3.88; P  = 0.016) were associated with a less favorable prognosis. Conclusion The study suggests that the combination of ICIs and brain radiotherapy provides superior OS for NSCLC patients with BM, compared to the chemotherapy combined with brain radiotherapy.

The morbidity and mortality of lung cancer are increasing, seriously threatening human health and life. Non-small cell lung cancer (NSCLC) has an insidious onset and is not easy to be diagnosed in its early stage. Distant metastasis often occurs and the prognosis is poor. Radiotherapy (RT) combined with immunotherapy, especially with immune checkpoint inhibitors (ICIs), has become the focus of research in NSCLC. The efficacy of immunoradiotherapy (iRT) is promising, but further optimization is necessary. DNA methylation has been involved in immune escape and radioresistance, and becomes a game changer in iRT. In this review, we focused on the regulation of DNA methylation on ICIs treatment resistance and radioresistance in NSCLC and elucidated the potential synergistic effects of DNA methyltransferases inhibitors (DNMTis) with iRT. Taken together, we outlined evidence suggesting that a combination of DNMTis, RT, and immunotherapy could be a promising treatment strategy to improve NSCLC outcomes.

The spatio-temporal change of the surface water is very important to agricultural, economic, and social development in the Hetao Plain, as well as the structure and function of the ecosystem. To understand the long-term changes of the surface water area in the Hetao Plain, we used all available Landsat images (7534 scenes) and adopted the modified Normalized Difference Water Index (mNDWI), Enhanced Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) to map the open-surface water from 1989 to 2019 in the Google Earth Engine (GEE) cloud platform. We further analyzed precipitation, temperature, and irrigated area, revealing the impact of climate change and human activities on long-term surface water changes. The results show the following. (1) In the last 31 years, the maximum, seasonal, and annual average water body area values in the Hetao Plain have exhibited a downward trend. Meanwhile, the number of maximum, seasonal, and permanent water bodies displayed a significant upward trend. (2) The variation of the surface water area in the Hetao Plain is mainly affected by the maximum water body area, while the variation of the water body number is mainly affected by the number of minimum water bodies. (3) Precipitation has statistically significant positive effects on the water body area and water body number, which has statistically significant negative effects with temperature and irrigation. The findings of this study can be used to help the policy-makers and farmers understand changing water resources and its driving mechanism and provide a reference for water resources management, agricultural irrigation, and ecological protection.