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This paper uses the GJRSK model to estimate the high-order moments of energy (oil, natural gas, and coal), the carbon market, and tourism stocks. Then, it utilizes a novel TVP-VAR time-frequency connectedness approach to examine higher-order moments spillovers among them. The results show a strong connectedness among the three markets. The energy market is the emitter of volatility, skewness and kurtosis spillovers; tourism stock is the receiver; and the carbon market is the transmitter. From a time-domain perspective, the higher-order moments spillovers of the three markets are time-varying, especially during extreme periods, where the energy market’s spillover effects on tourism stocks are significantly enhanced, indicating that tourism stocks bear a greater risk at leptokurtosis and fat-tail moment. From a frequency-domain perspective, the long-term asymmetric spillovers of oil, natural gas, and tourism markets on the carbon market are more pronounced than the short-term. Moreover, the COVID-19 pandemic exacerbated the higher-moment spillovers of energy and tourism stocks on the carbon market. Meanwhile, the Russia-Ukraine conflict led to extreme risk transmission within the energy market. These findings have significant implications for cross-industry investors and green finance risk management.

Leaf spot disease is an extremely common disease in the growth process of maize in Northern China and its degree of harm is quite significant. Therefore, the rapid and accurate identification of maize leaf spot disease is crucial for reducing economic losses in maize. In complex field environments, traditional identification methods are susceptible to subjective interference and cannot quickly and accurately identify leaf spot disease through color or shape features. We present an advanced disease identification method utilizing YOLOv8. This method utilizes actual field images of diseased corn leaves to construct a dataset and accurately labels the diseased leaves in these images, thereby achieving rapid and accurate identification of target diseases in complex field environments. We have improved the model based on YOLOv8 by adding Slim-neck modules and GAM attention modules and introducing them to enhance the model’s ability to identify maize leaf spot disease. The enhanced YOLOv8 model achieved a precision (P) of 95.18%, a recall (R) of 89.11%, an average recognition accuracy (mAP50) of 94.65%, and an mAP50-95 of 71.62%, respectively. Compared to the original YOLOv8 model, the enhanced model showcased enhancements of 3.79%, 4.65%, 3.56%, and 7.3% in precision (P), recall (R), average recognition accuracy (mAP50), and mAP50-95, respectively. The model can effectively identify leaf spot disease and accurately calibrate its location. Under the same experimental conditions, we compared the improved model with the YOLOv3, YOLOv5, YOLOv6, Faster R-CNN, and SSD models. The results show that the improved model not only enhances performance, but also reduces parameter complexity and simplifies the network structure. The results indicated that the improved model enhanced performance, while reducing experimental time. Hence, the enhanced method proposed in this study, based on YOLOv8, exhibits the capability to identify maize leaf spot disease in intricate field environments, offering robust technical support for agricultural production.

Hepatocellular carcinoma (HCC) is a solid tumor prone to chemotherapy resistance, and combined immunotherapy is expected to bring a breakthrough in HCC treatment. However, the tumor and tumor microenvironment (TME) of HCC is highly complex and heterogeneous, and there are still many unknowns regarding tumor cell stemness and metabolic reprogramming in HCC. In this study, we combined single-cell RNA sequencing data from 27 HCC tumor tissues and 4 adjacent non-tumor tissues, and bulk RNA sequencing data from 374 of the Cancer Genome Atlas (TCGA)-liver hepatocellular carcinoma (LIHC) samples to construct a global single-cell landscape atlas of HCC. We analyzed the enrichment of signaling pathways of different cells in HCC, and identified the developmental trajectories of cell subpopulations in the TME using pseudotime analysis. Subsequently, we performed transcription factors regulating different subpopulations and gene regulatory network analysis, respectively. In addition, we estimated the stemness index of tumor cells and analyzed the intercellular communication between tumors and key TME cell clusters. We identified novel HCC cell clusters that specifically express HP (HCC_HP), which may lead to higher tumor differentiation and tumor heterogeneity. In addition, we found that the HP gene expression-positive neutrophil cluster (Neu_AIF1) had extensive and strong intercellular communication with HCC cells, tumor endothelial cells (TEC) and cancer-associated fibroblasts (CAF), suggesting that clearance of this new cluster may inhibit HCC progression. Furthermore, ErbB signaling pathway and GnRH signaling pathway were found to be upregulated in almost all HCC tumor-associated stromal cells and immune cells, except NKT cells. Moreover, the high intercellular communication between HCC and HSPA1-positive TME cells suggests that the immune microenvironment may be reprogrammed. In summary, our present study depicted the single-cell landscape heterogeneity of human HCC, identified new cell clusters in tumor cells and neutrophils with potential implications for immunotherapy research, discovered complex intercellular communication between tumor cells and TME cells.

Aim Our aims were to provide new pollen data for establishing a sub-continental surface pollen database (East Asian Pollen Database, EAPD) and to study relationships between vegetation and climate. Location The sample sites covered most regions of East Asia, including China, Mongolia, the Russian Far East, Vietnam, Cambodia and Thailand. Methods Data quality control procedures were applied, including taxonomic standardization, removal of duplicates, and adjustment of geographical coordinates. Vegetation types and climate parameters were assigned to each sample. Modern pollen distribution maps were drawn using circle scattergrams. The plots of pollen percentages versus climate variables allowed quantitative estimates of climate values. The modern analogue technique (MAT) was used to predict modern biomes and climate parameters. Results Pollen assemblages extracted from 2858 sites were used to model the geographical distribution of selected taxa and their relationships with climate. For most taxa, the reconstructed range fitted the observed geographical distribution rather well. Arboreal pollen (AP) and Pinus dominated the transition zone between forest and steppe. Use of the MAT revealed that the predicted and observed biomes matched in 71% of the cases. The warm temperate evergreen broadleaf forest had the best agreement between predictions and observations. Climate values reconstructed using MAT were highly correlated with observed values in January temperature. The correlation coefficient of the temperature variables ranged from 0.799 to 0.930 and was as high as 0.939 for precipitation. Main conclusions This paper documents a new modern pollen database for East Asia and makes the data readily available. The reconstructed biomes and climate variables are significantly correlated with the observed values, thus demonstrating the utility of the pollen database for future multiscale palaeoenvironmental studies.

Camellia harbors unique diversity along Sino-Vietnamese border. Some species of them are under threat due to human activity. Camellia hekouensis , a native of Hekou, Yunnan, China, was once considered extinct as the previously known “last living tree” died in 2024. Fortunately, 11 in-situ and 32 ex-situ trees have been protected and propagated by the staff of Hekou Administration Branch of Dawei Mountain National Nature Reserve in Yunnan with their great unpublicized efforts. Molecular phylogenetic analysis suggests that C. hekouensis is nested in the main clade CI of Camellia and forms a clade with C. corallina , C. gracilipes and C. pubicosta , which are generally distributed in Vietnam. Morphological characters of the capsule and seed of C. hekouensis are supplementally described. The leaves of C. hekouensis contain 1.18 mg/g theobromine, which disagrees with the previous chemotaxonomic claim. Though the economic and ecological values are little known for C. hekouensis , the species should be conserved and propagated effectively and promptly to prevent extinction.

Background The era of high throughput sequencing offers new paths to identifying species boundaries that are complementary to traditional morphology-based delimitations. De novo species delimitation using traditional or DNA super-barcodes serve as efficient approaches to recognizing putative species (molecular operational taxonomic units, MOTUs). Tea plants ( Camellia sect. Thea ) form a group of morphologically similar species with significant economic value, providing the raw material for tea, which is the most popular nonalcoholic caffeine-containing beverage in the world. Taxonomic challenges have arisen from vague species boundaries in this group. Results Based on the most comprehensive sampling of C. sect. Thea by far (165 individuals of 39 morphospecies), we applied three de novo species delimitation methods (ASAP, PTP, and mPTP) using plastome data to provide an independent evaluation of morphology-based species boundaries in tea plants. Comparing MOTU partitions with morphospecies, we particularly tested the congruence of MOTUs resulting from different methods. We recognized 28 consensus MOTUs within C. sect. Thea , while tentatively suggesting that 11 morphospecies be discarded. Ten of the 28 consensus MOTUs were uncovered as morphospecies complexes in need of further study integrating other evidence. Our results also showed a strong imbalance among the analyzed MOTUs in terms of the number of molecular diagnostic characters. Conclusion This study serves as a solid step forward for recognizing the underlying species boundaries of tea plants, providing a needed evidence-based framework for the utilization and conservation of this economically important plant group.

Background The rising mortality rates in sepsis highlight the current lack of reliable therapeutic biomarkers. This study aims to identify markers associated with biological functions to offer new strategies for sepsis diagnosis. Methods We conducted differential expression analysis to identify differentially expressed messenger RNAs (DEmRs), long non-coding RNA (DElncRs), and microRNAs (DEmiRs) in sepsis compared to healthy controls. Enrichment analysis was performed using DEmRs, and a lncRNA-miRNA-mRNA competing endogenous RNA network was constructed. Least absolute shrinkage and selection operator (LASSO) and random forest models were applied to identify diagnostic mRNAs. The optimal diagnostic model was determined through decision curve analysis, resulting in the identification of seven hub genes. The key gene, determined by its highest importance and the largest area under the receiver operating characteristics curve (AUC) value, was further validated. Additionally, we analyzed the correlation of the key gene with microenvironment cell infiltration and immune genes. Results A total of 4,450 intersected DEmRs (GSE66099, GSE13904, GSE154918, GSE8121) that were significantly involved in the cell cycle. We obtained 13 mRNAs, and further screened seven hub genes, including PPARD, ZSCAN2, ABI2, ENPP5, FMNL3, CD3E, and CAMK4. Subsequently, ENPP5 was as the key gene based on importance and AUC value. Moreover, Neutrophil cells and macrophages had a high abundance in sepsis patients. ENPP5 was positively associated with T cells but negatively associated with mast cells. Conclusion ENPP5, identified as a key gene, exhibits significant associations with immune cell infiltration and immune-related genes. This suggests its potential role as a biomarker for novel therapeutic strategies in sepsis.

Coastal vegetation both mitigates the damage inflicted by marine disasters on coastal areas and plays an important role in the global carbon cycle (i.e., blue carbon). Nevertheless, detailed records of changes in coastal vegetation composition and diversity in the Holocene, coupled with climate change and river evolution, remain unclear. To explore vegetation dynamics and their influencing factors on the coastal area of the Bohai Sea (BS) during the Holocene, we present high-resolution pollen and sediment grain size data obtained from a sediment core of the BS. The results reveal that two rapid and abrupt changes in salt marsh vegetation are linked with the river system changes. Within each event, a recurring pattern – starting with a decline in Cyperaceae, followed by an increase in Artemisia and Chenopodiaceae – suggests a successional process that is determined by the close relationship between Yellow River (YR) channel shifts and the wetland community dynamics. The phreatophyte Cyperaceae at the base of each sequence indicate lower saline conditions. Unchannelized river flow characterized the onset of the YR channel shift, caused a huge river-derived sediment accumulation in the floodplain and destroyed the sedges in the coastal depression. Along with the formation of a new channel, lateral migration of the lower channel stopped, and a new intertidal mudflat was formed. Pioneer species (Chenopodiaceae, Artemisia) were the first to colonize the bare zones of the lower and middle marsh areas. In addition, the pollen results revealed that the vegetation of the BS land area was dominated by broadleaved forests during the Early Holocene (8500–6500 BP) and by conifer and broadleaved forests in the Middle Holocene (6500–3500 BP), which was followed by an expansion of broadleaved trees (after 3500 BP). The pollen record indicated that a warmer Early and Late Holocene and colder Middle Holocene were consistent with previously reported temperature records for East Asia. The main driving factors of temperature variation in this region are insolation, the El Niño–Southern Oscillation and greenhouse gases forcing.

Background Chronic obstructive pulmonary disease (COPD) is a chronic and progressive lung disease. Disulfidptosis-related genes (DRGs) may be involved in the pathogenesis of COPD. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of disulfidptosis in the development of COPD could provide a opportunity for primary prediction, targeted prevention, and personalized treatment of the disease. Methods We analyzed the expression profiles of DRGs and immune cell infiltration in COPD patients by using the GSE38974 dataset. According to the DRGs, molecular clusters and related immune cell infiltration levels were explored in individuals with COPD. Next, co-expression modules and cluster-specific differentially expressed genes were identified by the Weighted Gene Co-expression Network Analysis (WGCNA). Comparing the performance of the random forest (RF), support vector machine (SVM), generalized linear model (GLM), and eXtreme Gradient Boosting (XGB), we constructed the ptimal machine learning model. Results DE-DRGs, differential immune cells and two clusters were identified. Notable difference in DRGs, immune cell populations, biological processes, and pathway behaviors were noted among the two clusters. Besides, significant differences in DRGs, immune cells, biological functions, and pathway activities were observed between the two clusters.A nomogram was created to aid in the practical application of clinical procedures. The SVM model achieved the best results in differentiating COPD patients across various clusters. Following that, we identified the top five genes as predictor genes via SVM model. These five genes related to the model were strongly linked to traits of the individuals with COPD. Conclusion Our study demonstrated the relationship between disulfidptosis and COPD and established an optimal machine-learning model to evaluate the subtypes and traits of COPD. DRGs serve as a target for future predictive diagnostics, targeted prevention, and individualized therapy in COPD, facilitating the transition from reactive medical services to PPPM in the management of the disease.

Objective To develop and validate a machine learning model for predicting mortality-associated prognostic factors in order to reduce in-hospital mortality rates among HIV/AIDS patients with Cryptococcus infection in Guangxi, China. Methods This retrospective prognostic study included HIV/AIDS patients with cryptococcosis in the Fourth People’s Hospital of Nanning from October 2011 to June 2019. Clinical features were extracted and used to train ten machine learning models, including Logistic Regression, KNN, DT, RF, Adaboost, Xgboost, LightGBM, Catboost, SVM, and NBM, to predict the outcome of HIV patients with cryptococcosis infection. The sensitivity, specificity, AUC, and F1 value were applied to assess model performance in both the testing and training sets. The optimal model was selected and interpreted. Results A total of 396 patients were included in the study. The average in-hospital mortality of HIV/AIDS patients with cryptococcosis was 12.9% from 2012 to 2019. After feature screening, 20 clinical features were selected for model construction, accounting for 93.8%, including ART, Electrolyte disorder, Anemia, and 17 laboratory tests. The RF model (AUC 0.9787, Sensitivity 0.9535, Specificity 0.8889, F1 0.7455) and the SVM model (AUC 0.9286, Sensitivity 0.7907, Specificity 0.9786, F1 0.8293) had excellent performance. The SHAP analysis showed that the primary risk factors for prognosis prediction were identified as BUN/CREA, Electrolyte disorder, NEUT%, Urea, and IBIL. Conclusions RF and SVM machine learning models have shown promising predictive abilities for the prognosis of hospitalized HIV/AIDS patients with cryptococcosis, which can aid clinical assessment and treatment decisions for patient prognosis.