Search Results (665)

This paper proposes a novel methodology, based on machine learning and statistical models, for predicting hurricane-related losses to specific assets. Our approach uses three critical storm parameters typically tracked by meteorological agencies: maximum wind speed, minimum sea level pressure, and radius of maximum wind. The system categorizes potential damage events into three insurance-relevant classes: non-payable, partially payable, and fully payable. Three triggers for final payouts were designed: hybrid framework, standalone regression, and standalone non-linear regression. The hybrid framework combines two classification models and a non-linear regression model in an ensemble specifically designed to minimize the absolute differences between predicted and actual payouts (Total Absolute Error or TAE), addressing highly imbalanced and partially compensable events. Although this complex approach may not be suitable for all current contracts due to limited interpretability, it provides an approximate lower bound for the minimization of the absolute error. The standalone non-linear regression model is structurally simpler, yet it likewise offers limited transparency. This hybrid framework is not intended for direct deployment in parametric insurance contracts, but rather serves as a benchmarking and research tool to quantify the achievable reduction in basis risk under highly imbalanced conditions. The standalone linear regression provides an interpretable linear regression model optimized for feature selection and interaction terms, enabling direct deployment in parametric insurance contracts while maintaining transparency. These three approaches allow analysis of the trade-off between model complexity, predictive performance, and interpretability. The three approaches are compared using comprehensive hurricane simulation data from an industry-standard catastrophe model. The methodology is particularly valuable for parametric insurance applications, where rapid assessment and claims settlement are essential. Full article

The Caribbean Coastal Marine Productivity (CARICOMP) program was conceptualized in 1985 to monitor coral reefs, seagrass beds, and mangrove forests at multiple sites across the wider Caribbean. Mangrove monitoring was focused on the dominant Caribbean species, red mangrove (Rhizophora mangle). Forest structure and productivity were monitored at 21 sites (18 countries) across different geomorphological settings, from tropical to subtropical mainland and island systems. Here, we provide the key findings from the CARICOMP mangrove data collected, mostly from 1992 to 2013, to assess spatial and temporal variability across the region. Red mangrove above-ground biomass averaged 190 t ha⁻¹ (far higher than previously reported) but ranged widely across sites from 33 to 590 t ha⁻¹, equating to an average above-ground ‘blue carbon’ of 84 t ha⁻¹ (range 15–260 t ha⁻¹). Tree density averaged 3237 trees ha⁻¹, tree basal area averaged 19.7 m² ha⁻¹, tree height averaged 6.1 ± 2.8 m, and seedling density varied from 1.2 to 74 seedlings m⁻² across the sites. Among the environmental factors that influence mangroves, local temperature and rainfall explained 48% of the variability in measured tree structure parameters. Annual litterfall, as a proxy for productivity, measured on average 1.24 ± 0.70 kg m⁻² yr⁻¹, with 60% of the total litterfall composed of leaves. Litterfall varied seasonally by 42%. No relationship was apparent between litterfall and seasonal ocean–atmosphere climate indices (ONI and AMM). With exception of the three most southwesterly CARICOMP sites, hurricanes and tropical storms impacted the mangrove sites repeatedly, resulting in considerable damage. A direct strike by a category-4 hurricane in 1998 in Dominican Republic killed 67% of the red mangrove trees, lowered above-ground biomass by 91%, basal area by 89%, litterfall by 63%, and resulted in the subsequent growth of many tall and thin saplings, totally changing the structure of the forest ecosystem in the first few years after the hurricane. In comparing mangrove systems, major differences may be explained by time elapsed since the last destructive event (hurricane) affecting each site. This highlights the fact that despite an increasing focus on preserving these valuable ecosystems, they are still highly vulnerable to natural hazards and likely to face a poor outcome under ongoing climate change. Full article

Background/Objectives: Sepsis-associated liver injury (SALI) is a critical and often early complication of sepsis, defined by distinct hyper-inflammatory and immunosuppressive phases that shape patient phenotypes. Methods: Characterizing these phases establishes a foundation for immunomodulation strategies tailored to individual immune responses, as discussed subsequently. Results: The initial inflammatory response activates pathways such as NF-κB and the NLRP3 inflammasome, leading to a cytokine storm that damages hepatocytes and is frequently associated with higher SOFA scores and a higher risk of 28-day mortality. Kupffer cells and infiltrating neutrophils exacerbate hepatic injury by releasing proinflammatory cytokines and reactive oxygen species, thereby causing cellular damage and prolonging ICU stays. During the subsequent immunosuppressive phase, impaired infection control and tissue repair can result in recurrent hospital-acquired infections and a poorer prognosis. Concurrently, hepatocytes undergo significant metabolic disturbances, notably impaired fatty acid oxidation due to downregulation of transcription factors such as PPARα and HNF4α. This metabolic alteration corresponds with worsening liver function tests, which may reflect the severity of liver failure in clinical practice. Mitochondrial dysfunction, driven by oxidative stress and defective autophagic quality control, impairs cellular energy production and induces hepatocyte death, which is closely linked to declining liver function and increased mortality. The gut-liver axis plays a central role in SALI pathogenesis, as sepsis-induced gut dysbiosis and increased intestinal permeability allow bacterial products, including lipopolysaccharides, to enter the portal circulation and further inflame the liver. This process is associated with sepsis-related liver failure and greater reliance on vasopressor support. Protective microbial metabolites, such as indole-3-propionic acid (IPA), decrease significantly during sepsis, removing key anti-inflammatory signals and potentially prolonging recovery. Clinically, SALI most commonly presents as septic cholestasis with elevated bilirubin and mild transaminase changes, although conventional liver function tests are insufficiently sensitive for early detection. Novel biomarkers, including protein panels and non-coding RNAs, as well as dynamic liver function tests such as LiMAx (currently in phase II diagnostics) and ICG-PDR, offer promise for improved diagnosis and prognostication. Specifying the developmental stage of these biomarkers, such as identifying LiMAx as phase II, informs investment priorities and translational readiness. Current management is primarily supportive, emphasizing infection control and organ support. Investigational therapies include immunomodulation tailored to immune phenotypes, metabolic and mitochondrial-targeted agents such as pemafibrate and dichloroacetate, and interventions to restore gut microbiota balance, including probiotics and fecal microbiota transplantation. However, translational challenges remain due to limitations of animal models and patient heterogeneity. Conclusion: Future research should focus on developing representative models, validating biomarkers, and conducting clinical trials to enable personalized therapies that modulate inflammation, restore metabolism, and repair the gut-liver axis, with the goal of improving outcomes in SALI. Full article

Enterovirus A71 (EV-A71) is a critical global pathogen, primarily causing Hand-Foot-and-Mouth Disease (HFMD) but frequently leading to severe neurological complications, including fatal neurogenic pulmonary edema (PE). This review elucidates the complex interplay between viral pathogenesis and the host immune response. EV-A71 utilizes receptors like SCARB2 and PSGL-1 for entry, while its proteases (2A^pro, 3C^pro) efficiently evade innate immunity by cleaving key signaling adaptors (MAVS, TRIF), suppressing Type I IFN response. Critical to disease progression is the age-dependent vulnerability in infants and the subsequent shift toward immunopathology. Severe disease is driven by a systemic cytokine storm and T cell dysregulation, characterized by a loss of control from Treg cells and a profound Th17/Treg imbalance, resulting in high levels of pathogenic cytokines (e.g., IL-17A, IFN-γ). Clinical progression is predicted by specific biomarkers, including Treg depletion, monocyte exhaustion (PD-1/PD-L1), and suppressed regulatory signaling (low cAMP). These findings highlight that effective therapeutic strategies must target host-mediated damage through immunomodulation (e.g., by exploring interventions against key pathogenic axes like IL-6 and IL-1β) and call for the development of next-generation vaccines capable of eliciting balanced cellular immunity to prevent immunopathology. Full article

In Portugal, increasing wildfire frequency and severe storm events have intensified the need for advanced monitoring tools to assess forest damage and recovery efficiently. This study explores the application of deep learning neural network techniques, specifically the Mask R-CNN architecture, for the automatic detection of trees in Pinus pinaster stands using RGB and multispectral imagery captured by a drone. The research addresses two distinct forest scenarios, resulting from disturbances intensified by climate change. The first concerns the detection of fallen trees following an extreme weather event to support damage assessment and inform post-disturbance forest management. The second focuses on segmenting individual trees in a newly established plantation after wildfire to evaluate the effectiveness of ecological restoration efforts. The collected images were processed to generate high-resolution orthophotos and orthomosaics, which were used as input for tree detection using Mask R-CNN. Results showed that integrating drone-based imagery with deep learning models can significantly enhance the efficiency of forest assessments, reducing the need for fieldwork effort and increasing the reliability of the collected data. Results demonstrated high performance, with average precision scores of 90% for fallen trees and 75% for recently planted trees, while also enabling the extraction of spatial metrics relevant to forest monitoring. Overall, the proposed methodology shows strong potential for rapid response in post-disturbance environments and for monitoring the early development of forest plantations. Full article

Dengue virus serotype 2 (DENV-2) is a predominant cause of severe dengue and a key determinant of dengue-associated liver injury. This review integrates recent findings on the molecular and cellular mechanisms of DENV-2 hepatotropism, focusing on viral replication, cellular stress responses, and immune-mediated damage. The interplay between hepatocytes, Kupffer cells, and innate and adaptive immune responses, culminating in cytokine storm and immune-mediated hepatocyte apoptosis, is dissected. Integrating in vitro and in vivo findings, this review highlights how viral replication and immune dysregulation converge to cause hepatic injury. Future research should prioritize antiviral, immunomodulatory, and hepatoprotective approaches aimed at reducing the risk of dengue-associated liver failure. Full article

This paper presents the hydrological and meteorological parameters of the Wolfgang storm surge on the southern Baltic Sea coast and the storm’s impact on coastal areas with highly urbanised and developed zones. The surge emerged during a rare cyclonic system that was located over Western Europe in October 2023. A high difference in air pressure between the western and eastern parts of the Baltic coast led to the high-velocity wind blowing from the eastern direction to the centre of the cyclone located over Denmark. It caused high sea levels in the western part of the Baltic Sea. On the German and Danish coasts, the inflow of water at a high wind velocity perpendicular to the coast caused a very high surge of the sea and strong undulation. In this part of the Baltic Sea, the storm caused an increase in the sea level ranging from 1.5 to 2.2 m above average. It was lower on the eastern part of the Polish coast, exceeding 0.9 m above average sea level. The erosion of the base of cliffs ranged from 2 to 7 m, depending on the sea level. The dune erosion was larger but more varied, which resulted from different heights of the beach, at a maximum of up to 18 m. The water run-up reached 5.2 m above mean sea level (AMSL). The run-up parameter is a more accurate indicator of the potential threat than the sea level height. As a result of water run-up on the coast, lowlands situated even as far as 300 m from the shore were flooded. The storm caused significant damage to the coastal infrastructure and harbours. Research was conducted based on field studies and the analysis of digital documentation from websites, with the records of water run-up and the effects of the storm. Field studies were based on measures of coast retreat. Sea levels and wind were studied based on collected data. Full article

Tree-covered urban green spaces, including streets, parks, and other public areas, are vital for urban sustainability and people’s well-being. However, such trees face threats from the occurrence of extreme weather. In this study, we investigated wind damage to urban trees in the city of Debrecen, Hungary, during two severe windstorms in July 2025. Field surveys were conducted across three distinct urban zones, covering approximately 515,000 m² in total. We assessed 201 damaged and 325 undamaged trees and recorded the species, size, damage type, and contextual landscape features associated with them being damaged or not. Damage type to trees consisted primarily of broken branches, whilst uprooting and trunk breakage were recorded less often. Most tree characteristics (trunk circumference, height, systematic position, nativity) and the proximity and height of buildings upwind of focal trees were significant predictors of their vulnerability to windstorms. In addition, we surveyed 150 residents in person and received comments from 54 people via online questionnaires and explored their perceptions of storm frequency, the causes of storms, and mitigation measures. Most respondents noted increased storm frequency and attributed that to climate change, and they suggested mitigation measures focused on urban tree management and environmental protection. Some people expressed scepticism about the presence of climate change and/or their ability to address such damage on an individual basis. Our study is the first to integrate assessments of storm-related impacts on urban trees with the opinions of residents living in proximity to them. Our findings highlight the need for climate-adaptive and mechanically robust urban forestry planning and offer insights that guide the management of trees in urban areas globally. Specifically, we propose to undertake the following: (1) Prioritise structurally resilient, stress-tolerant tree species adapted to extreme weather conditions when planting new trees. (2) Integrate wind dynamics, microclimatic effects and artificial stabilisation techniques into urban design processes to optimise tree placement and their long-term stability. Urban planners, builders, developers, and homeowners should be informed about these stabilising practices and incorporate the needs of trees early in the design process, rather than as decorative additions. (3) Develop regionally calibrated risk models and early-warning systems to support proactive and data-driven tree management and public safety. (4) Promote climate literacy and public participation to strengthen collective stewardship and resilience of urban trees. Full article

The urban forest canopy provides critical ecosystem services, including carbon storage and sequestration. Healthy, well-managed trees in an urban setting can provide these services in a way comparable to forests managed for production or as nature preserves. Disturbance events threaten these benefits by reducing canopy cover and biomass. A tornado struck Ruston, Louisiana, on 25 April 2019, resulting in severe canopy damage through a swatch of the city. We used iTree Canopy to obtain estimates of ecosystem services (carbon sequestration, etc.) and converted this to a per-pixel value before interpolating for the study area. Fractional vegetation estimates obtained from spectral unmixing were obtained from pre- and post-tornado images using Sentinel-2 data and applied to weight damage. Pre- and post-tornado assessments revealed that Ruston’s urban forest canopy sequestered 85% of its pre-storm capability, with an estimated decline in social value of approximately $36,000. Assessing disturbance-based landscape changes, and subsequently calculating fractional changes in biomass and corresponding monetary impacts, will increasingly be looked to as ecosystem services and severe weather events are expected to become more commonplace in the future. The methodology employed demonstrates a cost-effective way to assess disturbance impacts in small urban areas, offering a framework to small municipalities to monitor canopy dynamics. Full article

Floods are among the most frequent and damaging natural hazards in the Mekong River Basin, where the interplay of monsoon-driven climate variability, complex topography, and rapid socio-economic change creates high exposure and vulnerability. This study presents a comprehensive assessment of flood disaster patterns, loss distribution, and regional disparities across five countries in the Lower Mekong Basin—Cambodia, Laos, Myanmar, Thailand, and Vietnam. Using multivariate spatiotemporal analysis based on EM-DAT, MRC, and national government datasets, the study quantifies flood frequency, casualties, and affected population to reveal cross-country differences in disaster impact and timing. Results show that while Vietnam and Thailand experience high flood frequency and storm-induced events, Laos and Cambodia face riverine flooding under constrained economic and infrastructural conditions. The findings highlight a basin-wide increase in flood frequency over recent decades, driven by climate change, land use transitions, and uneven development. The analysis identifies critical gaps in adaptive governance, particularly the need for dynamic policy frameworks that can adjust to spatial disparities in flood typologies (e.g., Vietnam’s storm floods vs. Cambodia’s riverine floods) and improve transboundary coordination of reservoir operations. Despite the region’s extensive reservoir capacity, most infrastructure prioritizes hydropower over flood mitigation. The study evaluates the role of regional cooperation frameworks such as the Lancang–Mekong Cooperation (LMC), demonstrating how strengthened institutional flexibility and knowledge-sharing mechanisms could enhance progress toward Sustainable Development Goals (SDGs) related to water governance (SDG 6), resilient infrastructure (SDG 9), and disaster risk reduction (SDG 11). By constructing the first integrated national-level flood disaster database for the basin and conducting comparative analysis across countries, this research provides empirical evidence to support differentiated yet coordinated flood risk governance strategies at both national and transboundary levels. Full article

Roof damage caused by hurricanes and other storms needs to be rapidly identified and repaired to help communities recover from catastrophic events and support the well-being of residents. Traditional, ground-based inspections are time-consuming but have recently been expedited via manual interpretation of remote sensing imagery. To potentially accelerate the process, automated methods involving artificial intelligence (i.e., deep learning) can be applied. Here, we present an end-to-end workflow for training and evaluating deep learning image segmentation models that detect and delineate two classes of post-storm roof damage: roof decking and roof holes. Mask2Former models were trained using 2500 roof decking and 2500 roof hole samples from drone RGB orthomosaics (0.02–0.08 m ground sample distance [GSD]) captured in Sint Maarten and Dominica following Hurricanes Irma and Maria in 2017. The trained models were evaluated using 1440 reference samples from 10 test images, including eight drone orthomosaics (0.03–0.08 m GSD) acquired outside of the training areas in Sint Maarten and Dominica, one drone orthomosaic (0.05 m GSD) from the Bahamas, and one orthomosaic (0.15 m GSD) captured in the US Virgin Islands with a crewed aircraft and different sensor. Accuracies increased with a single-class modeling approach (instead of training one dual-class model) and expansion of the training datasets with 500 roof decking and 500 roof hole samples from external areas in the Bahamas and US Virgin Islands. The best-performing models reached overall F1 scores of 0.88 (roof decking) and 0.80 (roof hole). In this study, we provide: our end-to-end deep learning workflow; a detailed accuracy assessment organized by modeling approach, damage class, and test location; discussion of implications, limitations, and future research; and access to all data, tools, and trained models. Full article

Novel coronavirus (SARS-CoV-2) is highly infectious and pathogenic. Novel coronavirus infection can not only cause respiratory diseases but also lead to multiple organ damage through direct or indirect mechanisms, in which the liver is one of the most frequently affected organs. It has been reported that 15–65% of coronavirus disease 2019 (COVID-19) patients experience liver dysfunction, mainly manifested as mild to moderate elevation of alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Severe patients may progress to liver failure, develop hepatic encephalopathy, or have poor coagulation function. The mechanisms underlying this type of liver injury are complex. Pathways—including direct viral infection (via ACE2 receptors), immune-mediated responses (e.g., cytokine storm), ischemic/hypoxic liver damage, thrombosis, oxidative stress, neutrophil extracellular trap formation (NETosis), and the gut–liver axis—remain largely speculative and lack robust clinical causal evidence. In contrast, drug-induced liver injury (DILI) has been established as a well-defined causative factor using the Roussel Uclaf Causality Assessment Method (RUCAM). Treatment should simultaneously consider antiviral therapy and liver protection therapy. This article systematically reviewed the mechanism, clinical diagnosis, treatment, and management strategies of COVID-19-related liver injury and discussed the limitations of current research and the future directions, hoping to provide help for the diagnosis and treatment of such patients. Full article

Recovery from disasters is the complex process requiring coordinated measures to restore infrastructure, services and quality of life. While remote sensing is a well-established means for damage assessment, so far very few studies have shown how satellite imagery can be used by technical officers of affected countries to provide crucial, up-to-date information to monitor the reconstruction progress and natural restoration. To address this gap, the present study proposes a multi-temporal observatory method relying on GIS, change detection techniques and open and free multi-sensor satellite imagery to generate thematic maps documenting, over time, the impact and recovery from hydrological disasters such as hurricanes, tropical storms and induced flooding. The demonstration is carried out with regard to Hurricane Matthew, which struck Haiti in October 2016 and triggered a humanitarian crisis in the Sud and Grand’Anse regions. Synthetic Aperture Radar (SAR) amplitude change detection techniques were applied to pre-, cross- and post-disaster Sentinel-1 image pairs from August 2016 to September 2020, while optical Sentinel-2 images were used for verification and land cover classification. With regard to inundated areas, the analysis allowed us to determine the needed time for water recession and rural plain areas to be reclaimed for agricultural exploitation. With regard to buildings, the cities of Jérémie and Les Cayes were not only the most impacted areas, but also were those where most reconstruction efforts were made. However, some instances of new settlements located in at-risk zones, and thus being susceptible to future hurricanes, were found. This result suggests that the thematic maps can support policy-makers and regulators in reducing risk and making the reconstruction more resilient. Finally, to evaluate the replicability of the proposed method, an example at a country-scale is discussed with regard to the June 2023 flooding event. Full article

On September 7, 2024, a deep convection event was observed in Northern and Central Greece, and based on radar data analysis, three supercells were identified. One of these, the most intense with maximum radar reflectivity of 68 dBZ, had a lifetime of almost 7 h and covered a distance of more than 200 km, producing damaging winds and large hail along its track. The goal of this study was to analyze this case using radar data and to evaluate the predictability of such a high-impact event using a numerical weather prediction model. The Weather Research and Forecasting (ARW-WRF) model was used to perform an array of simulations, and using multiple initialization times, the influence of lead time was examined. Furthermore, the dependence of the results on the choice of parameterization scheme used in the model is assessed below. The model performed satisfactorily in predicting intense storm activity, without reaching the extreme values observed by the radar. Full article

Climate change is intensifying droughts, heatwaves, dust storms, and rainfall variability across Eastern Europe, undermining yields and soil stability. In Ukraine, decades of underinvestment and wartime damage have led to widespread degradation of field shelterbelts, while the adoption of agroforestry remains constrained by tenure ambiguity, fragmented responsibilities, and limited access to finance. This study develops a policy-and-technology framework to restore agroforestry at scale under severe fiscal and institutional constraints. We apply a three-stage approach: (i) a national baseline (post-1991 legislation, statistics) to diagnose the biophysical and legal drivers of shelterbelt decline, including wartime damage; (ii) a comparative synthesis of international support models (governance, incentives, finance); and (iii) an assessment of transferability of digital monitoring, reporting, and verification (MRV) tools to Ukraine. We find that eliminating tenure ambiguities, introducing targeted cost sharing, and enabling access to payments for ecosystem services and voluntary carbon markets can unlock financing at scale. A digital MRV stack—Earth observation, UAV/LiDAR, IoT sensors, and AI—can verify tree establishment and survival, quantify biomass and carbon increments, and document eligibility for performance-based incentives while lowering transaction costs relative to field-only surveys. The resulting sequenced policy package provides an actionable pathway for policymakers and donors to finance, monitor, and scale shelterbelt restoration in Ukraine and in similar resource-constrained settings. Full article