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To address the prevailing scenario where comprehensive susceptibility assessments of ground deformation disasters primarily rely on knowledge-driven models, with weight judgments largely founded on expert subjective assessments, this study initially explores the feasibility of integrating data-driven models into the evaluation of urban ground collapse and subsidence. Hangzhou city, characterized by filled soil and silty sand, was selected as the representative study area. Nine pertinent evaluation factors were identified, and the RF-BP neural network coupling model was employed to assess the susceptibility of ground collapse and subsidence in the study area, the results indicate that the stacked model achieved a 7% increase in AUC value compared to the single model. Subsequently, this study utilized the advanced large language model (LLM), ChatGPT-4, to supplant expert judgment in the weight determination of ground deformation disasters. The advantages of ChatGPT-4, such as its ability to process vast amounts of data and provide consistent, unbiased judgments, were highlighted. ChatGPT-4’s assessments were validated by geological experts in the study area through the analytic hierarchy process. The results show that, by analyzing the same textual materials, the weights determined by experts differed by only 3% from those judged by ChatGPT, demonstrating the reliability and human-expert-like logic of ChatGPT-4’s judgments. Finally, a comprehensive susceptibility assessment of ground deformation disasters was conducted utilizing ChatGPT-4’s judgment results, yielding favorable outcomes.

The survival of ovary granulosa cells (GC) is critical in the initiation and progression of polycystic ovary syndrome (PCOS) in females. Here, we found that the PCOS process is accompanied by massive GC pyroptosis resulting from Caspase-1 inflammasome activation. Administration of plumbagin, an effective compound isolated from plant medicine, can prevent the pyroptosis of GC and the onset of PCOS. Mechanistic study indicates the over-activation of the inflammasome in GC is due to the upregulation of WTAP, a key regulator of the RNA N6-methylase complex. WTAP mediates the mRNA N6-methylation of NLRP3 inflammasome component ASC and enhances ASC RNA stability, which results in the overactivation of the inflammasome in GCs from the PCOS model. Plumbagin treatment suppresses the WTAP-mediated N6-methylation of ASC mRNA and reduces the pyroptosis of GCs. This study supports the profound potential of plumbagin in PCOS treatment. Graphical Abstract

Dietary fiber (DF) was extracted from navel orange peel residue by enzyme (E-DF) and ultrasound-assisted deep eutectic solvent (US-DES-DF), and its physicochemical and prebiotic properties were characterized. Based on Fourier-transform infrared spectroscopy, all DF samples exhibited typical polysaccharide absorption spectra, indicating that DES could separate lignin while leaving the chemical structure of DF unchanged, yielding significantly higher extraction yields (76.69 ± 1.68%) compared to enzymatic methods (67.27 ± 0.13%). Moreover, ultrasound-assisted DES extraction improved the properties of navel orange DFs by significantly increasing the contents of soluble dietary fiber and total dietary fiber (3.29 ± 1.33% and 10.13 ± 0.78%, respectively), as well as a notable improvement in the values of water-holding capacity, oil-holding capacity, and water swelling capacity. US-DES-DF outperformed commercial citrus fiber in stimulating the proliferation of probiotic Bifidobacteria strains in vitro. Overall, ultrasound-assisted DES extraction exhibited potential as an industrial extraction method, and US-DES-DF could serve as a valuable functional food ingredient. These results provide a new perspective on the prebiotic properties of dietary fibers and the preparation process of prebiotics.

Geohazards such as collapses, landslides, and debris flows result from complex interactions between human activities and environmental conditions. However, a quantitative understanding of their coupling mechanisms remains challenging. This study developed a machine learning-based classification framework​ for multi-geohazard susceptibility mapping (GSM) in Zhejiang Province, China, to address this gap. The study employed XGBoost, AdaBoost, and Random Forest to construct individual models for each geohazard, using a comprehensive set of geomorphologic, geological, environmental, hydrological, and anthropogenic factors. The XGBoost model achieved Area Under the Curve (AUC) values greater than 0.9 for all geohazards, and was selected as the optimal model for GSM. Results show: (1) Topographic position index (TPI) and distance to roads are the most influential factors, with dominant roles varying by geohazard—TPI primarily controls debris flows, while collapses are more driven by road proximity. (2) Anthropogenic factors account for 15.9%–33.8% of importance across geohazards. (3) The dependence plots and heatmap of interaction values reveal the impact of human–natural factor coupling mechanisms on geohazards. The study provides a quantitative and interpretable analysis of human–natural environment coupling, offering insights for risk management and spatial planning in densely populated coastal regions under climate change.

With the rapid development of modern technology and information systems, optical anti-counterfeiting and encryption have recently attracted considerable attention. The demand for optical materials is also constantly increasing, with new requirements proposed for performance and application fields. Currently, rare earth ion doped materials possess a unique electronic layer structure, underfilled 4f5d electronic configuration, rich electronic energy level, and long-life excited state, which can produce a variety of radiation absorption and emission. The distinctive properties of rare earth are beneficial for using in diverse optical output anti-counterfeiting. Design is essential for rare earth ion doped materials with multiple responsiveness and multi-channel optical information anti-counterfeiting in the field of information security. Therefore, this mini review summarizes the luminescent mechanisms, preparation methods, performance characteristics and anti-counterfeiting application of rare earth doped materials. In addition, we discuss some critical challenges in this field, and potential solutions that have been or are being developed to overcome these challenges.

The issue of weak interpretability in geological disaster susceptibility assessments using machine learning models has been a long-standing concern. Although SHAP (Shapley Additive Explanations) models have been extensively used in recent years to interpret the decision-making details of models, the specialized skills required and the non-intuitiveness of SHAP plots make their application challenging in practical decision-making environments. In response, our study introduces a map-based SHAP visualization framework to enhance the interpretability of susceptibility assessment results. Utilizing Optuna for hyperparameter tuning, we developed a high-performance XGBoost model to assess the susceptibility of the most impactful disaster in Hangzhou: urban road collapses. In addition to interpreting the contributions of evaluation factors through traditional SHAP summaries and bar plots, we displayed the SHAP values for each evaluation factor using map visualizations, and discussed the model's sensitivity to different values. To validate the alignment between model predictions and physical collapse mechanisms, our study selected typical collapse cases, interpreted these cases combining map visualizations, SHAP force plots at collapse points, and the physical mechanisms of collapse. Our research improves the interpretability of susceptibility assessments with machine learning by using map visualizations, providing new insights into spatial effects and robust support for urban decision-making applications.

Background and objective Polycystic ovary syndrome (PCOS) is a common endocrine-metabolic disease in women of reproductive age. One of its core pathologies is ovarian granulosa cell (GC) dysfunction, and ferroptosis, as a novel cell death mode dependent on iron ions and lipid peroxidation, may be involved in the PCOS process, but the exact mechanism is unknown. Plumbagin (PLB) shows potential in PCOS treatment due to its antioxidant properties. The present study aimed to elucidate the molecular mechanisms by which PLB ameliorates mitochondrial dysfunction and ferroptosis in PCOS GCs through the YTH N6-methyladenosine RNA binding protein 1/L-type amino acid transporter 1 (YTHDF1/SLC7A5) axis. Methods An in vitro model of PCOS was constructed by treating KGN cells with dihydrotestosterone (DHT), and PLB treatment, YTHDF1 knockdown (si-YTHDF1), and SLC7A5 overexpression (pcDNA 3.1-SLC7A5) were intervened respectively. Cell viability was measured by cell counting kit-8. Lactate dehydrogenase (LDH) release, adenosine triphosphate (ATP) level, iron ion, and lipid peroxidation (LPO) content were detected by commercial kits. Mitochondrial membrane potential (MMP) was analyzed by JC-1 staining combined with flow cytometry. Reactive oxygen species (ROS) levels were assessed by C11-BODIPY probe, oxidative stress indicators including malondialdehyde (MDA), superoxide dismutase (SOD), glutathione peroxidase were measured by kits, and Cytochrome C, Ferritin, mitochondrial transcription factor A (TFAM), glutathione peroxidase 4 (GPX4) and SLC7A5 expression were detected by Western blot. Fluorescence in situ hybridization, RNA immunoprecipitation, and m6A quantitative real-time polymerase chain reaction verified the interaction and translational regulation of YTHDF1 and SLC7A5. Results DHT treatment significantly decreased KGN cell viability, MMP and ATP levels, increased LDH release, ROS, MDA, iron ions and LPO content, up-regulated Cytochrome C expression, and down-regulated Ferritin, TFAM, and GPX4 expression. Both PLB treatment and YTHDF1 knockdown significantly reversed the above changes, but YTHDF1 overexpression reversed the protective effect of PLB. YTHDF1 co-localized with SLC7A5 mRNA and enhanced its translation through m6A modification. YTHDF1 knockdown reduced SLC7A5 protein levels without affecting mRNA expression. SLC7A5 overexpression weakened the protective effect of YTHDF1 knockdown, resulting in decreased cell viability, deterioration of mitochondrial function, and increased ferroptosis. Conclusion PLB ameliorates DHT-induced mitochondrial dysfunction and ferroptosis in KGN cells by inhibiting YTHDF1 expression, and its action is dependent on the mechanism by which YTHDF1 regulates SLC7A5 translation through m6A modification. Downregulating YTHDF1 or SLC7A5 significantly enhances GC survival and function.

This study utilized sprouted buckwheat as the main component and aimed to optimize its combination with other grains to produce reconstituted rice with enhanced taste and a reduced glycemic index (GI). The optimal blend comprised wheat flour, sprouted buckwheat flour, black rice flour, and purple potato flour in a ratio of 34.5:28.8:26.7:10.0. Based on this blend, the reconstituted rice processed through extrusion puffing exhibited a purple-black hue; meanwhile, the instant reconstituted rice, produced through further microwave puffing, displayed a reddish-brown color. both imparted a rich cereal flavor. The starch in both types of rice exhibited a V-shaped structure with lower relative crystallinity. Compared to commercial rice, the reconstituted rice and instant reconstituted rice contained higher levels of flavonoids, polyphenols, and other flavor compounds, along with 1.63-fold and 1.75-fold more proteins, respectively. The GI values of the reconstituted rice and the instant reconstituted rice were 68.86 and 69.47, respectively; thus, they are medium-GI foods that can alleviate the increase in blood glucose levels.

The following problems exist in the process of incremental learning of support vector machines. If the number of support vectors will increase with the increase of the increment sample, the time of training will become longer and longer; if the vector that has no effect on the hyperplane of the classification is abandoned, this part of the vector may become a support vector in the subsequent training. This will have an impact on the effect of the classification. In this paper, a support vector machine incremental learning algorithm based on fuzzy C mean clustering and central density is used to determine support vector set by fuzzy C mean clustering, and the selected non support vector sets are obtained by using the ratio of center density to determine non support vectors. The effect of non support vector on the robustness of support vector incremental learning is studied by comparing the classification efficiency of the four kinds of classification, and the conclusion of this paper is finally obtained.

ObjectiveThe clinical features of rheumatic patients with coronavirus disease 2019 (COVID-19) have not been reported. This study aimed to describe the clinical features of COVID-19 in rheumatic patients and provide information for handling this situation in clinical practice.MethodsThis is a retrospective case series study. Deidentified data, including gender, age, laboratory and radiological results, symptoms, signs, and medication history, were collected from 2326 patients diagnosed with COVID-19, including 21 cases in combination with rheumatic disease, in Tongji Hospital between 13 January and 15 March 2020.ResultsLength of hospital stay and mortality rate were similar between rheumatic and non-rheumatic groups, while the presence of respiratory failure was more common in rheumatic cases (38% vs 10%, p<0.001). Symptoms of fever, fatigue and diarrhoea were seen in 76%, 43% and 23% of patients, respectively. There were four rheumatic patients who experienced a flare of rheumatic disease during hospital stay, with symptoms of muscle aches, back pain, joint pain or rash. While lymphocytopaenia was seen in 57% of rheumatic patients, only one patient (5%) presented with leucopenia in rheumatic cases. Rheumatic patients presented with similar radiological features of ground-glass opacity and consolidation. Patients with pre-existing interstitial lung disease showed massive fibrous stripes and crazy-paving signs at an early stage. Five rheumatic cases used hydroxychloroquine before the diagnosis of COVID-19 and none progressed to critically ill stage.ConclusionsRespiratory failure was more common in rheumatic patients infected with COVID-19. Differential diagnosis between COVID-19 and a flare of rheumatic disease should be considered.Trial registration numberChiCTR2000030795.