The rising frequency of large-scale, destructive wildfires has significantly affected not only natural ecosystems but also critical infrastructure, human lives, and properties. Given the devastating and often irreversible environmental and financial consequences, researchers from diverse fields are actively seeking solutions to improve resilience against wildfires. Fire suppression, one of the most effective strategies to mitigate wildfire damage, relies heavily on rapid and high-fidelity forecasting of fire spread. These predictions are essential for planning evacuations by state or local emergency management agencies, implementing preemptive de-energization strategies for electric utilities, and coordinating fire containment efforts by firefighting teams. However, a significant bottleneck across all these planning processes is the significant computational burden imposed by high-resolution wildfire modeling, the demand for improved predictive accuracy, and the need to integrate diverse and large-scale datasets. Since response time is crucial for wildfire risk management, this paper proposes a deep learning-based surrogate model to predict fire spread in just a fraction of a second. We developed and trained a convolutional neural network (CNN) model that efficiently predicts wildfire propagation. The proposed model demonstrates high efficiency, achieving an F1 score of 0.92. The contributions of this paper are twofold: (1) a fast, high-resolution CNN model that can support wildfire-related public safety power shutoff (PSPS) planning for electric utilities, and (2) a practical tool for firefighting and evacuation teams to support rapid and data-informed risk assessment.
Sex-specific variation can critically shape species' physiological responses to environmental change, with potentially strong implications during reproduction. Yet this source of variation is often overlooked. To help address this paucity, we investigated fitness and whole-organism traits and metabolomic profiles to elevated temperature in males and females of Gammarus locusta, a keystone intertidal amphipod inhabiting thermally dynamic coastal environments. Individuals were gradually acclimated to four ecologically relevant temperatures (16°C, 21°C, 26°C and 31°C) and maintained at these conditions for 21 d under controlled laboratory settings. Survival declined at 26°C and 31°C in both sexes, and females exhibited consistently higher upper thermal limits, broader thermal safety margins, but lower thermal acclimation capacity compared to males. Whilst reproductive output was lower in elevated temperatures (26°C and 31°C), juveniles' body length increased, indicating a "quality-over-quantity" reproduction strategy. At the metabolomic level, females exhibited greater plasticity, with enhanced pathways linked to energy metabolism, amino acid biosynthesis, and cellular stress defence, suggesting adaptive activation rather than heightened vulnerability, consistent with their higher thermal tolerance and survival. Elevated temperatures also impaired amphipods' energetic status, with both glucose and ATP:ADP ratio decreasing, particularly at 31°C. Overall, we show that sex-specific metabolomic strategies define different sex thermal tolerance levels in G. locusta. As ocean warming intensifies, males' greater vulnerability could skew population sex ratios toward females. This imbalance may limit mating opportunities and alter population operational sex ratio and therefore dynamics, if the number of males is insufficient to fertilise all females. Our study highlights the importance of considering sex-specific physiological responses by integrating them with life-history and fitness measures to build a mechanistic understanding of how thermal stress may shape reproductive dynamics and, ultimately, affect population viability. By linking cellular metabolism with organismal fitness, we provide the cross-scale insight needed to more accurately forecast marine invertebrates' responses to global change.
Accurate prediction of temporal reservoir landslide displacement using key environmental variables, such as rainfall depth, reservoir water level, and groundwater level, is crucial for early warning. These variables exhibit distinct, time-varying influences on displacement, yet conventional deep learning models often overlook such dynamics by adopting stationary sliding windows and assigning equal importance to all variables and timesteps, thereby limiting forecasting accuracy. To address this limitation, we propose a novel Dynamic Weight Generator (DWG) that learns per-variable, per-timestep weights within a user-defined historical window. DWG generates a weight map applied element-wise to normalized inputs before they are fed into a prediction backbone, enabling the model to emphasize critical variables and influential timesteps while down-weighting less relevant ones. DWG is integrated into representative deep-learning models, including convolutional neural networks, long short-term memory networks, transformers, and Mamba architectures. All models are trained end-to-end, allowing the weight map to be optimized directly for displacement prediction. Compared with conventional stationary approaches, the proposed framework consistently improves accuracy across all backbones. Overall, DWG effectively captures dynamic, variable-dependent lag effects, enhancing temporal landslide displacement prediction.
This study examines the influence of convectively coupled equatorial waves (CCEWs) on extreme rainfall over the west coast of India across all seasons. The results show that CCEWs substantially amplify extreme rainfall, with Rossby waves exerting the strongest impact, followed by Mixed Rossby-Gravity (MRG) and Kevin waves. Overall, these waves enhance land rainfall extremes by about 20-60 %, while amplification over adjacent oceanic regions can exceed 150 %. Wave activity associated with Kelvin and Rossby waves remains relatively uniform throughout the year, whereas MRG waves exhibit pronounced seasonality, peaking during June-September. During summer, the enhancement in extreme rainfall linked to MRG waves is comparable to that of Rossby waves, while in winter the influence of all wave types is largely confined to the southernmost part of India. The amplification of rainfall primarily arises from wave-induced increases in moisture convergence and the subsequent development of deep convective systems, further supported by strong interactions between the dynamical waves and the orography of the Western Ghats. The waves are not only instrumental in magnifying extreme rainfall but also in increasing its probability, with Rossby and MRG waves playing a major role in this process, while Kelvin wave's effect is insignificant. The study observes that the devastating extreme rainfall events over the southwest coast in 2018, 2019, and 2024 are linked to strong wave activity. These findings provide new insight into the mechanisms that intensify rainfall extremes over the west coast of India and highlight the potential for improving extreme rainfall forecasts using the predictable nature of equatorial wave activity.
Wastewater-based epidemiology (WBE) is a promising tool for monitoring respiratory pathogens, yet the rapid in-sewer decay of viruses limits its application in low-incidence settings. This study investigates the decay rates of respiratory syncytial virus (RSV) in wastewater under various conditions to enhance the utility of WBE for forecasting RSV outbreaks. Laboratory-scale recirculating sewer systems were employed to simulate in-sewer decay of RSV at different temperatures, organic concentrations, and total suspended solids (TSS) levels. A first-order decay model revealed a significant temperature dependence, with the decay rate increasing approximately 30-fold from 4 to 35 °C. Conversely, higher TSS concentrations provided substantial protection to RSV particles, extending the T90 by up to sevenfold, whereas SCOD exhibited minimal impact. Based on these findings, a multiple linear regression model was established to identify key predictors. This study underscores the importance of understanding RSV decay kinetics for accurate WBE and highlights the value of kinetic modelling in correcting in-sewer viral loss for optimized wastewater surveillance.
We extend the quantification of the Shared Socioeconomic Pathways (SSPs) narratives by projecting 188 socioeconomic indicators for 188 countries to 2150 using the International Futures model. The variables span demographics, conflict, economics, education, health, infrastructure, and governance using a fully integrated model. We address existing limitations related to a) misalignment between quantification and scenario narratives; b) inconsistencies driven by use of unconnected models; c) the omission of indicators; and d) absence of projections into the 22nd century. Here we show projections highly correlated with existing forecasts that also lead to lower economic growth because they are driven by broader factors including conflict, governance, infrastructure, and human development, which in turn leads to lower resource demand. We also introduce government spending measures by sector that can be used to connect the SSPs to broader theoretical understandings of future patterns of mitigation and adaptation in the face of climate change.
The rapid growth of municipal solid waste (MSW) driven by urbanization, population expansion, and economic development has emerged as a critical global environmental challenge, thereby requiring effective planning and resource management. However, accurate prediction of MSW generation and composition-which is essential for effective governance-remains hindered by inconsistent and highly heterogeneous global data. To address this gap, this study proposes a data-driven framework using multi-linear regression (MLR) and artificial neural networks (ANN) to forecast MSW across 217 countries. These models incorporate socioeconomic and demographic parameters, including GDP, population, literacy rate, urbanization, and household size. The results indicated that ANN outperformed MLR in terms of predictive accuracy, achieving R² of 0.94 for total MSW generation, compared to ~ 0.57 for MLR and ~ 0.68 reported in the existing global model. While prediction of waste generation exhibited strong accuracy, composition prediction remained challenging (R² up to 0.15), indicating the influence of unaccounted behavioral and regional factors. Despite limitations in accuracy due to data heterogeneity and compositional complexity, the findings of this study can support policymakers and planners in enhancing waste management strategies, optimizing resource recovery, and enabling data-driven decision-making toward sustainable and circular waste management systems aligned with the Sustainable Development Goals.
To develop and validate a multivariate Long Short-Term Memory (LSTM) model that integrates multi-source surveillance data for forecasting influenza activity. This study aimed to identify the most predictive variables and establish an optimized data fusion framework to enhance public health surveillance. We collected influenza case data, influenza-like illness (ILI) reports, and symptom monitoring data, along with corresponding meteorological data and Baidu Index data in Baoshan city from January 2022 to June 2025. Spearman correlation analysis was used to verify the relationship between each dataset and influenza case numbers. Furthermore, the SHapley Additive exPlanations (SHAP) was employed to quantify feature importance. A LSTM model was constructed for predictive research, to identify in the optimal multi-source dataset. The prediction model based on this optimal dataset utilized the moving percentile method to determine the best early warning threshold. Influenza activity in Baoshan City exhibited distinct seasonality, with outbreaks peaking in winter and spring. ILI reports demonstrated the strongest correlation with confirmed cases (rs = 0.56, p < 0.001). Among 43 Baidu Index keywords, four, including "H1N1 flu symptoms"(S2) showed higher correlations (rs > 0.40, p < 0.001), whereas meteorological and symptom surveillance data were weak correlation. In predictive modeling, the LSTM achieved peak performance using ILI data alone (test set R2 = 0.79, MSE = 24.82, MAE = 3.14). Moreover, refined feature selection consistently outperformed models using full feature sets. The combination of ILI and the Baidu keyword "Is the Flu Shot Necessary?(P2)" provided the balanced and practical model (R2 = 0.79, MSE = 25.15, MAE = 3.18), matching the accuracy of the standalone ILI model. Based on the LSTM model for ILI predictions, the optimal early warning threshold was determined as P70, with the optimal threshold value being 8.10. This study demonstrates that a strategically simplified LSTM model, leveraging refined multi-source data, can achieve high accuracy and robustness, providing solutions for public health surveillance scenarios. The threshold value of influenza epidemic warning in Baoshan city demonstrates reasonable sensitivity and specificity, and can be recommended as an early warning index of the influenza epidemic in Baoshan city. Not applicable.
Ecological risk assessment (ERA) and health risk assessment (HRA) of metal(loid)-contaminated soils are essential for the achievement of risk mitigation and sustainable soil management. While conventional risk assessment methodologies are constrained by static modeling and a lack of multivariate considerations, machine learning (ML) has demonstrated significant potential in soil metal(loid) ERA and HRA by leveraging robust data mining and pattern recognition capabilities. Following the PRISMA guidelines, the review included 54 peer-reviewed studies regarding ML-based ERA and HRA in metal(loid)-contaminated soils from the Web of Science and Scopus databases. The analysis of 54 reviewed studies reveals an upward trend in research within this field from 2021 to 2025, focusing on Cd, Pb, As, Cr, Cu, Zn, Ni, and Hg. Input data for ML approaches include environmental covariates (94.44%) and hyperspectral data (5.56%). ML applications in risk assessment comprise four distinct pathways: risk assessment based on direct modeling of risk indicators (29.23%), risk assessment based on predicted concentrations (32.31%), source-oriented risk assessments (32.31%), and risk assessments improved by environmental factors (6.15%). Specifically, ERA is mainly applied through direct modeling of risk indicators (42.86%), whereas source-oriented risk assessment (46.67%) is the primary pathway for HRA. This review provides a comprehensive synthesis of the current status and developmental trends, an overview and selection guidelines for ML approaches, a summary of common input variables, and a detailed analysis of application pathways, while addressing existing challenges and future perspectives.
Pseudomyxoma peritonei is a rare, heterogeneous appendiceal cancer characterized by mucus-secreting tumor cells. Evidence suggests a microbial association, though its significance remains unclear. This forum article synthesizes links among microbial communities, mucin biology, and tumor behavior, highlighting key research challenges and how spatial multiomics may clarify disease mechanisms and therapeutic opportunities.
Diabetes is a major contributor to premature mortality, but whether declining mortality trends in the general population have been matched among people with diabetes remains uncertain in China. Retrospective cohort study based on hospitalisation records from 207 public general hospitals in Beijing (excluding community-level primary care facilities and speciality hospitals), serving the city's 21.8 million registered residents, 2012-2024. Temporal trends by Joinpoint regression, and life expectancy using abridged life tables. Among 452,186 in-hospital deaths, 146,282 (32.3%) had diabetes. In-hospital mortality was 32.0 per 1000 admissions in patients with diabetes and 13.4 in those without diabetes. During 2012-2019, among adults ≥75 years, mortality declined in patients without diabetes (AAPC, -1.2%; p = 0.026) but remained stable in those with diabetes (AAPC, 0.9%; p = 0.082); among adults <75 years, declines were smaller in patients with diabetes (-2.4% vs. -4.0%). Life expectancy-estimated from hospitalisation records as a comparative measure of survival among admitted patients-was 5.0 years lower in patients with diabetes than in those without diabetes. Loss was greater with earlier diagnosis; diagnosis at ages 20-24 years was associated with a 4.8-year reduction. Loss was larger in women than in men (5.8 vs. 2.4 years). Diabetes was associated with substantially higher in-hospital mortality and smaller mortality improvements. Earlier diagnosis was linked to greater life expectancy loss, with disproportionate losses among women.
Rising anti-2SLGBTQIA+ hostility creates a crisis of exclusion in science. Queer and Trans microbiologists often navigate their careers in isolation, facing systemic barriers that limit their retention. To counter this, we established the Pride in Microbiology Network in 2023. Here, we share strategies forbuilding safe, decentralized networks beyond institutional and geographic borders and argue that resilient scientific ecosystems depend on diversity, inclusion, and support structures that enable 2SLGBTQIA+ scientists to remain, connect, and lead.
Alternative lengthening of telomeres (ALT) is a recombination-mediated telomere maintenance mechanism. Although the core ALT machinery is defined, the initiating events remain unresolved. Taylor et al. demonstrate that telomeric heterochromatin enrichment drives nuclear compartmentalization, promyelocytic leukemia body nucleation, and telomere clustering, establishing a chromatin-defined environment that is permissive for recombination.
Phlebotomine sand flies are the sole vector of leishmaniasis and other sand fly-borne diseases, and their populations are shaped by climate and human-driven environmental change. While warming is expected to expand sand fly ranges, the joint effects of long-term climatic variability and land-use change on population dynamics remain poorly understood, largely owing to limited long-term monitoring. We analyzed an 18-year dataset (2005-2021) from a site in semi-arid Ma'ale Adumim, Israel, using CO2-baited traps. Land-cover change was quantified using remote sensing, focusing on the establishment of a managed urban park near the site. Climatic variables from the Israel Meteorological Service and ERA5 were related to interannual variation in abundance and seasonal phenology characters (median seasonal timing and peak timing). A marked land-cover transformation associated with an irrigated park was identified, increasing dry-season vegetation. Sand fly species showed contrasting long-term trends and phase-dependent responses: Phlebotomus sergenti and P. papatasi were most abundant immediately after landscape modification and declined later, whereas P. tobbi and P. syriacus increased after park establishment. Seasonal activity was strongly species-specific with limited interspecific synchrony. Climatic variability explained little interannual variation overall; only P. tobbi maximum abundance and timing of within-season population increase were associated with the timing of thermal onset. Anthropogenic land-use change dominated long-term sand fly population dynamics, often exceeding climatic effects, and altered habitat suitability in species-specific, phase-dependent ways. Integrating land management and spatial planning into prevention strategies is critical for reducing leishmaniasis risk in human-modified landscapes.
The transition of engineering education toward competency-oriented training is critical for modern intelligent manufacturing, yet empirical models for resource-constrained local universities remain underexplored. Using a national-level automotive engineering experimental teaching demonstration center in China as a case, this study designed and evaluated a progressive four-level practical teaching system. The system integrates Outcome-Based Education (OBE) and the Conceive-Design-Implement-Operate (CDIO) approach and is supported by a Teaching-Learning-Competition-Innovation (TLCI) linkage mechanism. To evaluate its operation, this study conducted a design-based longitudinal investigation using graduate administrative tracking, competency self-assessment, and employer feedback. Multi-source descriptive evidence showed that, during implementation, the graduate employment rate remained around 95% for four consecutive years, and job-major alignment was approximately 80.0%. In addition, the average starting salary of the 2023 cohort increased by 42% compared with the 2020 cohort, students reported relatively high perceived engineering problem-solving ability, and national competition performance also showed positive trends. These findings suggest that combining industry practice with a competition-driven closed-loop mechanism may provide practical reference for improving the quality of engineering talent cultivation and offer an implementable pathway for educational reform in resource-constrained universities.
The present study investigates the characteristics of long-term PM10 pollution in the proximity of Afşin-Elbistan lignite-fired thermal power plant complex, one of Türkiye's largest coal-based energy production regions. Daily measurements obtained from the Elbistan monitoring station are analyzed from 2011 to 2024 using descriptive statistics, exceedance analysis for annual and monthly data, and Air Quality Index-Integrated Innovative Trend Analysis (AQI-ITA). The obtained results show that the annual average PM10 concentration values exceed the national limit for the whole period and are several-fold higher than the WHO guideline value. In addition, the lowest annual averages are recorded in 2020 and 2022, while the highest annual averages are found in 2017 and 2018. For some years, the number of days when the daily limit is exceeded approaches the total number of days in the year. High PM10 concentration values are registered in winter months due to emissions related to lignite combustion and residential heating. According to AQI-ITA results, the trendless behavior dominates the process under analysis at low and moderate concentration values, while increasing trends occur at high pollution values in winter and autumn. Overall, PM10 pollution in Elbistan can be considered persistent during the entire period under consideration and may be influenced by the nearby energy production complex, residential heating activities, and various environmental factors. Therefore, further improvements in air quality management policies are also expected to contribute to more effective pollution control in the region.
Arthritis is a collective term for joint disorders, which are characterised by inflammation, pain, stiffness in joints and can progressively lead to disabilities. It is a worldwide problem affecting millions of people. The two most prevalent forms of inflammatory disease include rheumatoid arthritis and osteoarthritis, based on the disease etiology, pathogenesis and clinical therapy. Symptoms like pain in the joint, stiffness, redness, and swelling are most often seen in this case. This review emphasises the current idea about arthritis, highlighting therapeutic interventions including key herbal remedies and their marketed formulations, and synthetic drugs. Also, we discuss about emerging trends and future directions aimed at enhancing patient outcomes through personalized medication, biotechnology, and integrative approaches.
Efficient targeting of the olfactory cleft remains a key barrier to olfactory-targeted intranasal therapy and emerging nose-to-brain (N2B) delivery strategies. However, the upstream aerodynamic mechanisms governing aerosol access to the olfactory cleft during natural inhalation remain insufficiently characterized. A standardized representative sinonasal model reconstructed from high-resolution CT data of 32 healthy adults was used to evaluate the effects of administration plane, aerosol particle size, and administration angle on olfactory cleft deposition. Airflow and particle transport were simulated using a lattice Boltzmann-discrete particle method (LBM-DPM) framework across 108 parameterized conditions under natural inhalation. A geometry-consistent 3D-printed nasal model combined with radiotracer-based SPECT/CT imaging was used to experimentally validate deposition trends across administration planes. Under nebulized delivery during natural inhalation, administration plane and aerosol particle size were the primary determinants of olfactory deposition efficiency, whereas administration angle exerted minimal influence. Shallow insertion facilitated upstream aerosol transport toward the olfactory cleft, with particles of approximately 7 μm achieving the highest and stable deposition efficiency across conditions. A modest interaction between insertion depth and particle size was observed without altering the optimal delivery configuration. In vitro radiotracer experiments demonstrated consistent deposition trends across administration planes compared with numerical simulations, supporting the model predictions. Under physiological inhalation, shallow nozzle positioning combined with intermediate-sized aerosol particles represents an optimal parameter configuration for olfactory-targeted intranasal aerosol delivery. These findings provide quantitative guidance for optimizing intranasal administration parameters and may support the development of nebulized delivery systems for olfactory-targeted therapies.
Androgen deprivation is assumed to boost antitumor immunity-but Lee et al. overturn this logic in glioblastoma, showing that androgen loss activates a microglial inflammasome-hypothalamic-pituitary-adrenal axis cascade that elevates glucocorticoids and attenuates T cell function. The study reveals how organ context reverses endocrine control of tumor immunity.
We aimed to evaluate 15-year trends in the prevalence and healthcare expenditure of spinal diseases in South Korea, encompassing the COVID-19 pandemic and the transition to a super-aged society. Claims records registered in the Health Insurance Review and Assessment Service between 2010 and 2024 served as the analytical source for this population-level investigation. All patients diagnosed with spinal diseases (ICD-10: M40-M54) were identified. We calculated age-standardized rates (ASRs) per 100,000 population using the WHO 2000 World Standard Population. Joinpoint regression analysis was performed to identify significant trend changes by sex and disease subgroup. A total of 9.50 million patients in 2010 increased to 13.26 million in 2024 (+ 39.6%). Male ASR rose at an average annual percent change (AAPC) of 1.73% (95% CI: 1.47-1.99; p < 0.001), while female ASR remained stable (AAPC: 0.05%; 95% CI: -0.24 to 0.35; p = 0.706). The female-to-male ASR ratio declined from 1.42 to 1.13. Back pain (M54) showed significant increases in both sexes. Total healthcare expenditure grew from 1.42 to 3.26 billion USD (+ 130%). Males, not females, accounted for most of the 39.6% growth. The sex ratio compressed (1.42 to 1.13). A 130% cost surge against 39.6% patient growth signals escalating treatment intensity per episode; sex-tailored prevention and expenditure governance may warrant consideration.