Search Results (211)

Background: Cancer staging, guided by anatomical and clinicopathologic factors, is essential for determining treatment strategies and patient prognosis. The current gold standard for prostate cancer is the American Joint Committee on Cancer (AJCC) Tumor, Lymph Node, and Metastasis (TNM) Staging System 9th Version (2024). This system incorporates five prognostic variables: tumor (T), spread to lymph nodes (N), metastasis (M), prostate-specific antigen (PSA) levels (P), and Grade Group/Gleason score (G). While effective, further refinement of prognostic systems may improve prediction of patient outcomes and support more individualized treatment. Methods: We applied the Ensemble Algorithm for Clustering Cancer Data (EACCD), an unsupervised machine learning approach. EACCD involves three steps: calculating initial dissimilarities, performing ensemble learning, and conducting hierarchical clustering. We first developed an EACCD model using the five AJCC variables (T, N, M, P, G). The model was then expanded to include two additional factors, age (A) and race (R). Prostate cancer patient data were obtained from the Surveillance, Epidemiology, and End Results (SEER) program from the National Cancer Institute. Results: The EACCD algorithm effectively stratified patients into distinct prognostic groups, each with well-separated survival curves. The five-variable model achieved a concordance index (C-index) of 0.8293 (95% CI: 0.8245–0.8341), while the seven-variable model, including age and race, improved performance to 0.8504 (95% CI: 0.8461–0.8547). Both outperformed the AJCC TNM system, which had a C-index of 0.7676 (95% CI: 0.7622–0.7731). Conclusions: EACCD provides a refined prognostic framework for primary localized prostate cancer, demonstrating superior accuracy over the AJCC staging system. With further validation in independent cohorts, EACCD could enhance risk stratification and support precision oncology. Full article

This study focuses on 120 representative river basins across China, utilizing CMIP6 multi-model climate data and CESM2-LE large ensemble climate data to develop a bias-correction framework for climate models that integrates statistical methods, with the aim of enhancing the spatiotemporal accuracy of climate model outputs. Building on this framework, the study simulates the evolution of hydrological drought characteristics in Chinese river basins during 2071–2100 under the SSP370 scenario and quantifies the relative contributions of internal climate variability (ICV), anthropogenic climate change (ACC), and inter-model uncertainty (IMU) to hydrological drought projections. Results reveal a pronounced south–north divergence in future drought risk. Southern China—especially the middle–lower Yangtze and Pearl River basins—exhibits a >10% increase in drought frequency, with event totals exceeding 30 per 30 years, yet individual droughts remain short and moderate in intensity. Conversely, northern basins—particularly the Songliao and Liao River systems—display pronounced lengthening and intensification of droughts, with mean duration surpassing 12 months and severity indices rising above 38, translating to 20~40% increases relative to the 1985~2014 baseline. Nationwide, ICV emerges as the dominant driver of projected changes: signal-to-noise ratios for frequency, intensity, and duration fall below unity across more than 70% of basins, indicating that unforced variability overshadows the anthropogenic trend. ACC signals only exceed ICV in southeastern coastal regions and parts of the Pearl River basin for intensity and duration. Inter-model spread rivals or exceeds ICV uncertainty in these same humid subtropical basins, underscoring the sensitivity of projections to model structure. Full article

Solanum rostratum Dunal is a highly invasive species with strong environmental adaptability and reproductive capacity, posing serious threats to agroforestry ecosystems and human health. In this study, we compiled occurrence records of S. rostratum in China from online databases and sources in the literature. We employed the Biomod2 ensemble modeling framework to predict the potential distribution of the species under current climatic conditions and four future climate scenarios (SSP126, SSP245, SSP370, and SSP585), and to identify the key environmental variables influencing its distribution. The ensemble model based on the committee averaging (EMca) approach achieved the highest predictive accuracy, with a true skill statistic (TSS) of 0.932 and an area under the curve (AUC) of 0.990. Under present climatic conditions, S. rostratum is predominantly distributed across northern China, particularly in Xinjiang, Inner Mongolia, and the northeastern provinces, covering a total suitable area of 1,191,586.55 km², with highly suitable habitats accounting for 50.37% of this range. Under future climate scenarios, the species’ suitable range is projected to expand significantly, particularly under the high-emissions SSP585 scenario, with the distribution centroid expected to shift significantly toward high-altitude regions in Gansu Province. Precipitation and temperature emerged as the most influential environmental factors affecting habitat suitability. These findings indicate that ongoing global warming may facilitate the survival, reproduction, and rapid spread of S. rostratum across China in the coming decades. Full article

Background/Objectives: Head and neck squamous cell carcinoma (HNSCC) diagnosis and treatment rely heavily on computed tomography (CT) imaging to evaluate tumor characteristics and lymph node (LN) involvement, crucial for staging, prognosis, and therapy planning. Conventional LN evaluation methods based on morphological criteria such as size, shape, and anatomical location often lack sensitivity for early metastasis detection. This study leverages radiomics to extract quantitative features from CT images, addressing the limitations of subjective assessment and aiming to enhance LN classification accuracy while potentially reducing reliance on invasive histopathology. Methods: We analyzed 234 LNs from 27 HNSCC patients, deriving 120 features per node, resulting in over 25,000 data points classified into reactive, pathologic, and pathologic with extracapsular spread classes. Considering the challenges of high dimensionality and limited dataset size, more than 44,000 experiments systematically optimized machine learning models, feature selection methods, and hyperparameters, including ensemble approaches to strengthen classification robustness. A Pareto front strategy was employed to balance diagnostic accuracy with significant feature reduction. Results: The optimal model, validated via 5-fold cross-validation, achieved a balanced accuracy of 0.90, an area under the curve (AUC) of 0.93, and an F1-score of 0.88 using only five radiomics features. Conclusions: This interpretable approach aligns well with clinical radiology practice, demonstrating strong potential for clinical application in noninvasive LN classification in HNSCC. Full article

The pepper weevil Anthonomus eugenii is a devastating pest native to Central America that can cause severe damage to over 35 pepper varieties. Global trade in peppers has significantly increased the risk of its spread and expansion. Moreover, future climate change may add more uncertainty to its distribution, resulting in considerable ecological and economic damage globally. Therefore, we employed an ensemble model combining Random Forests and CLIMEX to predict the potential global distribution of A. eugenii in historical and future climate scenarios. The results indicated that the maximum temperature of the warmest month is an important variable affecting global A. eugenii distribution. Under the historical climate scenario, the potential global distribution of A. eugenii is concentrated in the Midwestern and Southern United States, Central America, the La Plata Plain, parts of the Brazilian Plateau, the Mediterranean and Black Sea coasts, sub-Saharan Africa, Northern and Southern China, Southern India, Indochina Peninsula, and coastal area in Eastern Australia. Under future climate scenarios, suitable areas in the Northern Hemisphere, including North America, Europe, and China, are projected to expand toward higher latitudes. In China, the number of highly suitable areas is expected to increase significantly, mainly in the south and north. Contrastingly, suitable areas in Central America, northern South America, the Brazilian Plateau, India, and the Indochina Peninsula will become less suitable. The total land area suitable for A. eugenii under historical and future low- and high-emission climate scenarios accounted for 73.12, 66.82, and 75.97% of the global land area (except for Antarctica), respectively. The high-suitability areas identified by both models decreased by 19.05 and 35.02% under low- and high-emission scenarios, respectively. Building on these findings, we inferred the future expansion trends of A. eugenii globally. Furthermore, we provide early warning of A. eugenii invasion and a scientific basis for its spread and outbreak, facilitating the development of effective quarantine and control measures. Full article

This study investigates changes in Near-Surface Air Temperature (NSAT) over the South African region using weather station data, reanalysis products, and Coupled Model Intercomparison Project Phase 6 (CMIP6) model outputs. It is shown that, based on ERA5 reanalysis, the average NSAT increase in the region (45–10° S, 0–50° E) for the period 1940–2023 was 0.11 ± 0.04 °C. Weak multi-decadal changes in NSAT were observed from 1940 to the mid-1970s, followed by a rapid warming trend starting in the mid-1970s. Weather station data generally confirm these results, although they exhibit considerable inter-station variability. An ensemble of 33 CMIP6 models also reproduces these multi-decadal NSAT change characteristics. Specifically, the average model-simulated NSAT values for the region increased by 0.63 ± 0.12 °C between the periods 1940–1969 and 1994–2023. Based on the results of the comparison between weather station observations, reanalysis, and models, we utilize projections of NSAT changes from the analyzed ensemble of 33 CMIP6 models until the end of the 21st century under various Shared Socioeconomic Pathway (SSP) scenarios. These projections indicate that the average NSAT of the South African region will increase between 1994–2023 and 2070–2099 by 0.92 ± 0.36 °C under the SSP1-2.6 scenario, by 1.73 ± 0.44 °C under SSP2-4.5, by 2.52 ± 0.50 °C under SSP3-7.0, and by 3.17 ± 0.68 °C under SSP5-8.5. Between 1994–2023 and 2025–2054, the increase in average NSAT for the studied region, considering inter-model spread, will be 0.49–1.15 °C, depending on the SSP scenario. Furthermore, climate warming in South Africa, both in the next 30 years and by the end of the 21st century, is projected to occur according to all 33 CMIP6 models under all considered SSP scenarios. The main spatial feature of this warming is a more significant increase in NSAT over the landmass of the studied region compared to its surrounding waters, due to the stabilizing role of the ocean. Full article

Dutch elm disease is one of the most devastating plant diseases, primarily spread through bark beetles. Scolytus scolytus is a key vector of this disease. In this study, distribution data of S. scolytus were collected and filtered. Combined with environmental and climatic variables, an ensemble model was developed using the Biomod2 platform to predict its potential geographical distribution in China. The selection of climate variables was critical for accurate prediction. Eight bioclimatic factors with high importance were selected from 19 candidate variables. Among these, the three most important factors are the minimum temperature of the coldest month (bio6), precipitation seasonality (bio15), and precipitation in the driest quarter (bio17). Under current climate conditions, suitable habitats for S. scolytus are mainly located in the temperate regions between 30° and 60° N latitude. These include parts of Europe, East Asia, eastern and northwestern North America, and southern and northeastern South America. In China, the low-suitability area was estimated at 37,883.39 km², and the medium-suitability area at 251.14 km². No high-suitability regions were identified. However, low-suitability zones were widespread across multiple provinces. Under future climate scenarios, low-suitability areas are still projected across China. Medium-suitability areas are expected to increase under SSP370 and SSP585, particularly along the eastern coastal regions, peaking between 2041 and 2060. High-suitability zones may also emerge under these two scenarios, again concentrated in coastal areas. These findings provide a theoretical basis for entry quarantine measures and early warning systems aimed at controlling the spread of S. scolytus in China. Full article

Part I of this two-part documentation of the ECMWF ensemble (ECEPS) new tropical cyclone track and intensity forecasting challenges during the 2024 western North Pacific season described four typhoons that started well to the south of an unfavorable external environment north of 20° N. In this Part II, five other 2024 season typhoons that formed and intensified near and north of 20° N are documented. One change is that the Cooperative Institute for Meteorological Satellite Studies ADT + AIDT intensities derived from the Himawari-9 satellite were utilized for initialization and validation of the ECEPS intensity forecasts. Our first objective of providing earlier track and intensity forecast guidance than the Joint Typhoon Warning Center (JTWC) five-day forecasts was achieved for all five typhoons, although the track forecast spread was large for the early forecasts. For Marie (06 W) and Ampil (08 W) that formed near 25° N, 140° E in the middle of the unfavorable external environment, the ECEPS intensity forecasts accurately predicted the ADT + AIDT intensities with the exception that the rapid intensification of Ampil over the Kuroshio ocean current was underpredicted. Shanshan (11 W) was a challenging forecast as it intensified to a typhoon while being quasi-stationary near 17° N, 142° E before turning to the north to cross 20° N into the unfavorable external environment. While the ECEPS provided accurate guidance as to the timing and the longitude of the 20° N crossing, the later recurvature near Japan timing was a day early and 4 degrees longitude to the east. The ECEPS provided early, accurate track forecasts of Jebi’s (19 W) threat to mainland Japan. However, the ECEPS was predicting extratropical transition with V_max ~35 kt when the JTWC was interpreting Jebi’s remnants as a tropical cyclone. The ECEPS predicted well the unusual southward track of Krathon (20 W) out of the unfavorable environment to intensify while quasi-stationary near 18.5° N, 125.6° E. However, the rapid intensification as Krathon moved westward along 20° N was underpredicted. Full article

Fire spread between buildings is the primary cause of extensive fire damage in traditional village timber structure clusters. Accurately assessing fire spread risk is crucial for the preservation of these architectural ensembles. During the development and conservation of traditional villages, fire risk dynamics may shift due to fire-resistant retrofits or layout modifications, necessitating repeated risk reevaluations. To address challenges such as the computational intensity of fire spread simulations, high costs, and data acquisition difficulties, this study proposes a directed graph-based method for fire spread risk analysis and risk level classification in timber structure clusters, accounting for their unique fire propagation characteristics. First, localized fire spread paths and propagation times between nodes (buildings) are determined through fire spread simulations, constructing an adjacency matrix for the directed graph of the building cluster. Path search algorithms then identify the spread range and velocity under specific fire scenarios. Subsequently, a zoned risk assessment model for individual buildings is developed based on critical fire spread loss and velocity, integrating each building’s fire resistance and its probability of exposure to different risk zones to determine the overall cluster’s fire spread risk level. The method is validated using a case study of a typical village in Yunnan Province. Results demonstrate that the approach efficiently computes fire spread characteristics across different scenarios and quantitatively evaluates risk levels, enabling targeted fire safety interventions based on village-specific spread patterns. Case analysis reveals significant variations in fire spread behavior: Village 1, Village 2, and Village 3 exhibit fire resistance indices of 0.59, 0.757, and 0.493, corresponding to high, moderate, and high fire spread risk levels, respectively. Full article

Vapour pressure deficit (VPD) is frequently used to assess the impact of climate change on wildfires, vegetation, and other phenomena dependent on atmospheric moisture. A common aim of projection studies is to sample the full range of changes projected by climate models. Although characterization of model spread in projected temperature and rainfall is common, similar analyses are lacking for VPD. Here, we analyze the range of change in projected VPD from a 15-member CMIP6 model ensemble using the SSP-370 scenario. Projected changes are calculated for 2015–2100 relative to the historical period 1850–2014, and the resulting changes are analyzed on a seasonal and regional basis, the latter using continents based on IPCC reference regions. We find substantial regional differences including higher increases in VPD in areas towards the equatorial regions, indicating increased vulnerability to climate change in these areas. Seasonal assessments reveal that regions in the Northern Hemisphere experience peak VPD changes in summer (JJA), correlating with higher temperatures and lower relative humidity, while Southern Hemisphere areas like South America see notable increases in all seasons. We find that the mean projected change in seasonal VPD ranges from 0.02–0.23 kPa in Europe, 0.04–0.19 kPa in Asia, 0.02–0.16 kPa in North America, 0.15–0.33 kPa in South America, 0.10–0.18 kPa in Oceania, and 0.21–0.31 kPa in Africa. Our analysis suggests that for most regions, no two models span the range of projected change in VPD for all seasons. The overall projected change space for VPD identified here can be used to interpret existing studies and support model selection for future climate change impact assessments that seek to span this range. Full article

This study investigates an extreme squall line event that occurred in northern Jiangxi Province, China on 30–31 March 2024. Based on the WRF model, convection-permitting ensemble forecast experiments were conducted using two distinct initial perturbation approaches, namely, the Local Breeding of Growing Modes (LBGM) and the Ensemble Transform Kalman Filter (ETKF), to compare their perturbation structures, spatiotemporal evolution, and precipitation forecasting capabilities. The experiments demonstrated the following: (1) The LBGM method significantly improved the root mean square error (RMSE) of mid-upper tropospheric variables, particularly demonstrating superior performance in low-level temperature field forecasts, but the overall ensemble spread of the system was consistently smaller than that of ETKF. (2) The evolution of dynamical spread within the squall line system confirmed that ETKF generated greater spread growth in low-level wind fields, while LBGM exhibited better spatiotemporal alignment between mid-upper tropospheric wind field spread and the synoptic system evolution. (3) Vertical profiles of total moist energy revealed that ETKF initially exhibited higher total moist energy than LBGM. Both methods showed increasing total moist energy with forecast lead time, displaying a bimodal structure dominated by kinetic energy in upper layers (300–100 hPa) and balanced kinetic energy and moist physics terms in lower layers (1000–700 hPa), with ETKF demonstrating larger growth rates. (4) Kinetic energy spectrum analysis indicated that ETKF exhibited significantly higher perturbation energy than LBGM in the 100–1000 km mesoscale range and superior small- to medium-scale perturbation characterization at the 6–60 km scales initially. Precipitation and radar echo verification showed that ETKF effectively corrected positional biases in precipitation forecasts, while LBGM more accurately reproduced the bow-shaped echo structure near Nanchang due to its precise simulation of leading-edge vertical updrafts and rear-sector low pseudo-equivalent potential temperature regions. Full article

Background/Objectives: Surgical pathology of tubo-ovarian and peritoneal cancer carries a well-recognised diagnostic workload, partly due to the large amount of non-primary tumour-related tissue requiring assessment for the presence of metastatic disease. The lymph nodes and omentum are almost universally included in such resection cases and contribute considerably to this burden, principally due to volume rather than task complexity. To date, artificial intelligence (AI)-based studies have reported good success rates in identifying nodal spread in other malignancies, but the development of such time-saving assistive digital solutions has been neglected in ovarian cancer. This study aimed to detect the presence or absence of metastatic ovarian carcinoma in the lymph nodes and omentum. Methods: We used attention-based multiple-instance learning (ABMIL) with a vision-transformer foundation model to classify whole-slide images (WSIs) as either containing ovarian carcinoma metastases or not. Training and validation were conducted with a total of 855 WSIs of surgical resection specimens collected from 404 patients at Leeds Teaching Hospitals NHS Trust. Results: Ensembled classification from hold-out testing reached an AUROC of 0.998 (0.985–1.0) and a balanced accuracy of 100% (100.0–100.0%) in the lymph node set, and an AUROC of 0.963 (0.911–0.999) and a balanced accuracy of 98.0% (94.8–100.0%) in the omentum set. Conclusions: This model shows great potential in the identification of ovarian carcinoma nodal and omental metastases, and could provide clinical utility through its ability to pre-screen WSIs prior to histopathologist review. In turn, this could offer significant time-saving benefits and streamline clinical diagnostic workflows, helping to address the chronic staffing shortages in histopathology. Full article

COVID-19 was one of the most serious global public health emergencies in recent years, and its extremely fast spreading speed had a profound negative impact on society. A comprehensive analysis and prediction of COVID-19 could lay a theoretical foundation for monitoring and early warning systems. Since the outbreak of COVID-19, there has been an influx of research on predictive modelling, with artificial intelligence (AI) techniques, particularly machine learning (ML) methods, becoming the dominant research direction due to their superior capability in processing multidimensional datasets and capturing complex nonlinear transmission patterns. We systematically reviewed COVID-19 ML prediction models developed under the background of the epidemic using the PRISMA method. We used the selected keywords to screen the relevant literature of COVID-19 prediction using ML technology from 2020 to 2023 in the Web of Science, Springer and Elsevier databases. Based on predetermined inclusion and exclusion criteria, 136 eligible studies were ultimately selected from 5731 preliminarily screened publications, and the datasets, data preprocessing, ML models, and evaluation metrics used in these studies were assessed. By establishing a multi-level classification framework that included traditional statistical models (such as ARIMA), ML models (such as SVM), deep learning (DL) models (such as CNN, LSTM), ensemble learning methods (such as AdaBoost), and hybrid models (such as the fusion architecture of intelligent optimization algorithms and neural networks), it revealed that the hybrid modelling strategy effectively improved the prediction accuracy of the model through feature combination optimization and model cascade integration. In addition, we compared the performance of ML models with other models in the COVID-19 prediction task. The results showed that the propagation of COVID-19 is affected by multiple factors, including meteorological and socio-economic conditions. Compared to traditional methods, ML methods demonstrated significant advantages in COVID-19 prediction, especially hybrid modelling strategies, which showed great potential in optimizing accuracy. However, these techniques face challenges and limitations despite their strong performance. By reviewing existing research on COVID-19 prediction, this study provided systematic theoretical support for AI applications in infectious disease prediction and promoted technological innovation in public health. Full article

Planococcus minor is an invasive pest of significant economic importance that has attracted international attention. Predicting the potential geographic distribution of P. minor under climate change is crucial to developing effective prevention and control strategies for safeguarding agricultural productivity. In this study, we selected four species distribution models (GBM, GLM, MARS, MAXENT) and utilized the Biomod2 package to construct an ensemble model for predicting the suitable habitats of P. minor under the averaged climate conditions of 1970–2000 and 2041–2060 (2050s), including a low-emission pathway (SSP1-2.6) and a high-emission pathway (SSP5-8.5). Among the 19 bioclimatic variables considered, precipitation of the wettest quarter and temperature seasonality were identified as the most influential factors affecting the distribution of P. minor. Under the averaged climate conditions of 1970–2000, suitable habitats for P. minor are mainly distributed in tropical and subtropical regions worldwide. In China, highly suitable zones are concentrated in Yunnan, Guangxi, Guangdong, Hainan, and Taiwan. In the future, the global range of P. minor is projected to expand, with some highly suitable areas transitioning toward medium and low suitability. Under the high-emission pathway (SSP5-8.5) scenario, suitable habitats in China are anticipated to exhibit a pronounced trend of inland expansion. Establishing an ensemble model to predict the potential geographic distribution of P. minor will facilitate the assessment of invasion and spread risks, thereby providing a scientific foundation for developing targeted prevention and monitoring strategies for relevant regions. Full article

Predictive uncertainty analysis focuses on defensible variability in model projected values after estimation of the posterior parameter distribution. Inverse-style parameter estimation selects posterior parameters through history matching where parameters are varied and resulting model simulation values are compared to observations, and parameters are selected balancing goodness-of-fit between simulated and observed values and expert knowledge. When inverse-style parameter estimation approaches are used, parameter sensitivity, which is the change in simulated outputs relative to the change in parameter values, is an important consideration. Variation in null space parameters has a limited impact on history matching skill; however, these parameters become important when they impact predictions. A new null space sensitivity analysis for ensemble methods of data assimilation (DA) using observation error models is developed and implemented for an integrated hydrological model. Empirical parameter sensitivity is estimated by comparing the spreads of prior and posterior parameter distributions. Sensitivity analysis is generated by an ensemble of models with insensitive parameters varying across the prior parameter distribution and sensitive parameters fixed to best-fit model values. The result is identification of insensitive aquifer storage parameters that change storage-related model predictions by as much as two times. This null space analysis describes uncertainty from data insufficiency. Ensemble methods using observation error models also describe predictive uncertainty from noisy measurements and imperfect models. Full article