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Medical and welfare facilities in the Noto region of Japan were severely affected by the 2024 Noto Peninsula earthquake and subsequent torrential rains. Staff working in these facilities were disaster survivors and frontline caregivers with limited psychological support. Nonverbal social robots may provide companionship and emotional comfort; however, their effects on the health-related quality of life (QoL) and well-being of care staff in disaster-affected settings remain unclear. This study explored whether introducing a nonverbal artificial intelligence communication robot was associated with changes in health-related QoL and well-being among care facility staff working under disaster conditions. Secondary objectives were to evaluate safety, acceptability, and intention to continue use. This pragmatic, exploratory pilot study used an ABAB design conducted between February 2025 and June 2025. After a 2-week baseline period, staff in dementia care, general care, and short-stay units underwent 2-week intervention, withdrawal, reintervention, and withdrawal phases. Questionnaires were administered at each phase end. The primary outcomes were health-related QoL (EQ-5D-5L), well-being (World Health Organization-5 Well‑Being Index), and positive mental health (Mental Health Continuum-Short Form). Friedman tests compared outcomes across the 5 phases, and effect sizes were expressed as Kendall W. Safety, acceptability, and intention to continue use were compared between the first and second intervention phases using Wilcoxon signed rank tests with Bonferroni adjustment and rank-biserial correlations as effect sizes. Of the 58 staff who completed the baseline assessment, 49 (84.5%) were included in the analytic sample (25 in dementia care, 12 in general care, and 12 in short-stay units). Among these participants, 40 (81.6%) were women, and 38 (77.6%) reported disaster-related damage to their homes or families. In the pooled analysis, no phase effect was observed for the EQ-5D-5L (P=.10; Kendall W=0.032, negligible), the World Health Organization-5 Well‑Being Index (P=.70; Kendall W=0.016, negligible), or the Mental Health Continuum-Short Form (P=.44; Kendall W=0.022, negligible). No robot-related adverse events were reported. In the dementia care unit, nominal unadjusted differences were observed for "made me feel calm" (P=.045; rank-biserial correlation r=0.571, large), "like" (P=.03; r=0.559, large), and "felt at peace" (P=.02; r=0.718, large); however, none remained statistically significant after Bonferroni correction. The short-term use of a nonverbal artificial intelligence communication robot did not measurably improve health-related QoL or well-being among staff in disaster-affected care facilities. Deployment appeared feasible and was not associated with reported adverse events, but efficacy as a mental health support intervention remains unproven. Exploratory acceptability and interaction signals may inform future adequately powered studies.
Air pollution remains a pressing concern in urban India, affecting human wellbeing and ecosystem sustainability. This investigation explores the spatial and temporal variations in 10 μm particulate matter (PM10), nitric oxides (NOX), and sulfur dioxide (SO2) air pollution in Navi Mumbai, India, from 2014-2023. Data from 23 monitoring points were analyzed using geographic information systems-based methods, including inverse distance weighting and weighted overlay analysis, to generate a comprehensive pollution index. Findings indicate high seasonal variation, with elevated PM10 and NOX levels during premonsoon and winter due to traffic, industrial activity, and unfavorable meteorological conditions. Monsoon rains significantly reduced pollutant levels. Industrial hotspots, particularly in Taloja and Kalamboli, and traffic-heavy corridors, such as Vashi and Nerul, remained persistent pollution sources. A noticeable drop in pollutant concentrations in 2020 coincided with the COVID-19 lockdown, although levels surged in subsequent years. The weighted overlay analysis proved effective in identifying pollution hotspots and offering a comprehensive understanding of air quality risks. Global comparisons highlight the specific challenges of coastal satellite cities, where industrial and harbor emissions contribute to seasonal smog. This study emphasizes the need for targeted emission controls and urban planning interventions to improve air quality and sustainability in rapidly growing regions.
Eight new genera, 28 new species, four epitypes, two lectotypes, and 21 interesting new host and / or geographical records are introduced in this study. New genera include: Amesomyces (based on Amesomyces atrobrunneus), Carteromyces (based on Carteromyces arctostaphyli), Scolecofusariella (based on Fusarium peltigerae), Nothoniesslia (based on Nothoniesslia solidaginis), Paraacanthostigma (based on Paraacanthostigma eucalypti), Paraphaeophleospora (based on Paraphaeophleospora tripteridis), Parapolyscytalum (based on Parapolyscytalum minutum) and Subverticillium (based on Subverticillium juncicola). New species include: Bisifusarium duo (from human cornea, India), Capronia parasitica (on Eutypella sorbi on branches of Sorbus aucuparia, Switzerland), Castanediella acericola on dead leaf of Acer cf. pseudoplatanus, Germany), Cladophialophora calamagrostidis (on culm of Calamagrostis arenaria, The Netherlands), Cladophialophora paramycetomatis (on culms of Elegia tectorum, South Africa), Cladophialophora yuccae (on dead leaf of Yucca sp., Germany), Cordana ligni (on dead wood, Germany), Curvularia moniliformis (on leaves of unidentified Poaceae, Chile), Davidhawksworthia rubi (on Rubus stems, Germany), Exophiala ligni (on dead wood, Germany), Fusarium aloetica (on symptomatic leaves of Aloe ferox, South Africa), Harzia cupressicola (on needles of Cupressus sp., The Netherlands), Hoehneliella falsiundulosetulata (on bark of woody host, Germany), Mjuua pseudoclavispora (in association with Fusarium paeoniae and a bacterium, on dead fruit of Alnus glutinosa, Germany), Monilinia yunnanensis (on fruit of Prunus persica, China), Niesslia goniomae (on leaf of Gonioma kamassi, South Africa), Nothoniesslia solidaginis (on dead stems of Solidago sp., Germany), Paraacanthostigma eucalypti (on bark of Eucalyptus globulus, Australia), Phaeococcomyces mesembryanthemi (on Mesembryanthemum schultzii, South Africa), Phialemonium parasulfureum (on algae, Germany), Phytophthora caput-medusae (from rhizosphere soil of Citrus × aurantium, Italy), Pleurophragmium fallopiae (on dead leaf Fallopia sp., Germany), Rhinotrichella carpini (on dead branches of Carpinus betulus, Ukraine), Stagonosporopsis citri (on peel of living fruit of Citrus latifolia, quarantine interception), Subverticillium juncicola (on culms of Juncus effusus, Netherlands), Veronaea parabrunnea (on Eutypella prunastri on twigs of Prunus spinosa, France), Veronaea parasiticola (on Euonymus europaeus, Germany), Verrucocladosporium mesembryanthemi (on Mesembryanthemum schultzii, South Africa). New combinations include: Amesomyces atrobrunneus (based on Chaetomium atrobrunneum), Amesomyces cymbiformis (based on Chaetomium cymbiforme), Amesomyces dreyfussii (based on Chaetomium dreyfussii), Amesomyces gelasinosporus (based on Chaetomium gelasinosporum), Amesomyces hispanicus (based on Amesia hispanica), Amesomyces khuzestanicus (based on Amesia khuzestanica), Amesomyces nigricolor (based on Chaetomium nigricolor), Amesomyces raii (based on Chaetomium raii), Carteromyces arctostaphyli (based on Carteria arctostaphyli), Carteromyces canariensis (based on Carteria canariensis), Capronia americana (based on Cadophora americana), Didymella conyzaphthora (based on Phoma conyzaphthora), Heterotruncatella watsoniae (based on Pestalotia watsoniae), Hoehneliella undulosetulata (based on Paramenisporopsis undulosetulata), Microascus stellatus (based on Humicola stellata), Neoceratosperma marasasii (based on Mycosphaerella marasasii), Paraphaeophleospora tripteridis (based on Septoria tripteridis), Parapolyscytalum minutum (based on Infundichalara minuta), Scolecofusariella peltigerae (based on Fusarium peltigerae) and Veronaea brunnea (based on Exophiala brunnea). Zygophiala is reduced to synonymy under Schizothyrium, Paramenisporopsis and Klebahnopycnis under Hoehneliella, and the descriptions of the order Comminutisporales and family Comminutisporaceae are emended. Citation: Crous PW, Akulov A, Alfenas AC, Alfenas RF, Aloi F, Balashov S, Barreto RW, Bensch K, Cacciola SO, Cantillo T, Castillo R, Conti Taguali S, Czachura P, da Silva NF, Delgado MA, Denman S, de Silva NI, de Vries RP, Figge M, Guarnaccia V, Guterres DS, Hongsanan S, Horta Jung M, Houbraken JA, Hülsewig T, Jung T, Jurjević Ž, La Spada F, Madrid H, Mombert A, Osieck ER, Pane A, Parlascino R, Pereira CM, Piątek M, Piattino V, Riolo M, Sandoval-Denis M, Scanu B, Starink-Willemse M, Stryjak-Bogacka M, Tennakoon DS, van Ingen-Buijs VA, van Iperen AL, Verkley GJM, Lamprecht SC, Wang XW, Braun U, Wingfield MJ, Groenewald JZ (2026). New and Interesting Fungi. 8. Persoonia 56: 457-547. doi: 10.3114/persoonia.2026.56.08.
Continuous alumina (Al2O3) fibers are critical reinforcement materials for ceramic matrix composites (CMCs) utilized in extreme high-temperature environments. While their baseline thermal and mechanical properties are well-documented, their long-term service reliability in complex, multi-field environments-specifically coupled thermal, hygral, and atmospheric conditions-remains insufficiently quantified. This study systematically investigates the degradation mechanisms of alumina ceramic fiber ropes subjected to simulated engine exhaust atmospheres and cyclic rain exposure. By integrating macroscopic tensile testing with rigorous multi-scale microstructural characterizations (SEM, XRD, TGA, and advanced surface chemical state analyses via EDS and XPS), a comprehensive degradation model is proposed. Our findings reveal a pronounced two-stage mechanical degradation behavior: an initial catastrophic strength collapse followed by a stabilization phase. We elucidate that the initial embrittlement is governed not merely by thermal damage, but fundamentally by the hydrothermal volatilization and depletion of the surface amorphous SiO2 binder, which annihilates the inter-fiber cooperative load-sharing capability. Concurrently, quantitative XPS and XRD analyses strongly suggest that the internal amorphous grain-boundary films undergo rapid structural rearrangement and crystallization, effectively homogenizing the microstructure and shifting the fracture mechanics from energy-dissipative crack deflection to unhindered brittle cleavage. After the preferential depletion of the amorphous silicate phase, the exposed α-Al2O3 core dictates a stabilized mechanical response. This research provides critical theoretical frameworks and experimental evidence for the life-cycle assessment and microstructural optimization of advanced oxide ceramic fibers in next-generation aerospace applications.
Climate change increasingly threatens public health in West Africa, with pregnant women and young children particularly vulnerable. Despite Nigeria's high exposure to climate risks, epidemiological evidence linking temperature and rainfall to maternal and child health remains limited. This study addresses this gap using nationally representative data. We analysed data from the 2024 Nigeria Demographic and Health Survey, including 27,783 mother-child pairs. Climate exposures, i.e., daytime land surface temperature (°C) and annual rainfall (mm), were derived from the 2020 Nigeria Geospatial Covariates dataset and linked to 2024 DHS cluster geolocations. Child health outcomes included stunting, wasting, underweight, and fever. Maternal outcomes included anaemia, postpartum distress, and a composite healthcare access index. Multilevel mixed-effects regression models with survey weights were applied. Higher temperatures were associated with increased odds of stunting (aOR 1.12, 95% CI 1.04-1.22), wasting (aOR 1.15, 95% CI 1.02-1.29), underweight (aOR 1.12, 95% CI 1.04-1.21), and fever (aOR 1.08, 95% CI 1.01-1.16) in children. Among mothers, higher temperatures were linked to greater postpartum distress (β = 0.03, p < 0.05) and reduced healthcare access (β = -0.07, p < 0.01), but not anaemia. A negative interaction between temperature and rainfall suggested attenuation of heat effects on underweight and healthcare access in wetter areas. Poverty and low maternal education amplified risks. Higher temperatures are associated with poorer maternal and child health outcomes in Nigeria, particularly among socioeconomically disadvantaged groups. Integrating climate adaptation into maternal and child health programmes is essential, especially in high-risk regions.
Indoor environments are primary settings of human activity and play a central role in shaping population health through microbial exposure. However, how short-duration climatic regimes influence indoor airborne microbiomes and resistomes remains largely unknown. Here, we investigated how the plum rain season, characterized by persistent rainfall and humid conditions, affects airborne bacterial communities, antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), and potential human pathogens in a dormitory building. The plum rain season significantly reduced airborne bacterial richness and altered community composition, with more pronounced effects indoors than outdoors. Airborne bacterial communities during this period also exhibited less complex co-occurrence networks and broader niche breadth, suggesting season-associated changes in bacterial ecological features. Although the number of detected ARGs in indoor air remained largely unchanged, the plum rain season significantly increased the relative abundances of ARGs (3.02% vs. 1.52%) and MGEs (0.42% vs. 0.14%) indoors compared with the post-plum rain period, whereas such seasonal enrichment was not observed outdoors. Indoor air during the plum rain season also showed higher relative abundances of high-risk ARGs. Concurrently, the relative abundance of potential human pathogens increased from 7.34% to 13.38% indoors, with Achromobacter xylosoxidans being the dominant potential pathogen. Furthermore, indoor ARG enrichment was associated with higher MGE abundance, increased A. xylosoxidans abundance, and shifts in airborne bacterial community composition. Together, these findings suggest that the plum rain season promotes indoor airborne resistome enrichment and potential exposure to airborne microbial hazards, underscoring the need to incorporate regional climatic regimes into indoor environmental health assessment.
Background: Melioidosis correlates strongly with rainfall, and there is substantial diversity in climate between melioidosis-endemic locations. The Northern Territory of Australia epitomises the "wet/dry" tropics, with a prolonged dry season from May to October. We analysed dry season cases of melioidosis during 35 consecutive years and compared these with wet season cases. We aimed to provide insights into how dry season cases of melioidosis may occur in this region and explore non-rainfall exposures that are usually not considered in the wet season. Methods: Case epidemiological and clinical data were extracted from the Darwin Prospective Melioidosis Study. Weather parameters, including daily rainfall, were analysed using generalised additive models and conditional logistic regressions to assess associations between dry season cases and preceding rainfall. Results: Of 1520 melioidosis cases between 1989 and 2024, there were 325 (21%) in the dry season. While the well-recognised clinical diversity of melioidosis was also seen amongst dry season cases, pneumonia was proportionally less common and cutaneous melioidosis was more common than in the wet season. A total of 23% of dry season patients had no identified clinical risk factors for melioidosis, compared to 14% in the wet season. Mortality was 8% in the dry season and 11% in the wet season. There was a range of plausible explanations for many of the dry season cases, including unseasonal rainfall prior to infection. Infections in urban settings were notable, with anthropogenic factors such as irrigation and construction resulting in persistence of Burkholderia pseudomallei in the environment during the dry season. A total of 3% of cases remained unexplained. Conclusions: Not all dry season cases are explained by infection occurring the previous wet season or by unseasonal rainfall in the dry. Identification of cases in the dry season support the need for year-round prevention strategies during potential exposure to contaminated water or soil. Further prospective studies are needed to better define the infecting events resulting in melioidosis, especially in the dry season. These studies should include timely history taking from the case and their family and selected environmental sampling for B. pseudomallei.
Sludge-based biochar is increasingly considered for land application, yet the short-term mobility of its soluble-ion pool under environmental stress remains poorly understood. In this study, soil column experiments were used to compare two placement modes, mixed incorporation and stratified placement, under dry-wet cycling, freeze-thaw cycling, simulated acid rain, and a control condition. Na+, Cl-, Ca2+, and SO42- were monitored in soil layers and leachate. Soil-only columns were included to constrain background contributions. Mixed placement was generally associated with lower divergence among ion-specific release trajectories and earlier stabilization, whereas stratified placement produced clearer staged redistribution. In the stratified columns, Na+ and Cl- stabilized earlier than Ca2+ and SO42-, indicating clear ion-specific differences in mobility and retention. Among the tested stressors, dry-wet cycling showed the highest cumulative leaching of all examined ions, especially Ca2+ and SO42-, suggesting that water content fluctuation was the strongest driver of short-term salt mobilization in this system. Freeze-thaw cycles and simulated acid rain also altered ion transport, but their effects depended on ion type and application mode. Because the experiment used a high biochar loading (20%, w/w), the results should be interpreted as comparative evidence under intensified conditions rather than as direct field-scale prediction. These findings suggest that pre-application screening of soluble ions and careful selection of placement mode are important when sludge-based biochar is used in salt-sensitive soils.
Acute lymphoblastic leukemia (ALL) is the leading cause of death in Mexican children, yet water-mediated environmental pathways remain largely unexplored as contributors to its spatial distribution. We analyzed ALL mortality records for individuals aged 0-19 years across all Mexican municipalities from 2003 to 2023, using publicly available national death statistics and population estimates. Age-specific mortality rates were calculated at the municipal level and aggregated to the state level to characterize broad geographic gradients. A discrete Poisson spatial scan statistic, implemented in SaTScan, identified 10 statistically significant mortality clusters, five with elevated risk and five with reduced risk, which were then compared against hydrogeological and industrial variables derived from global remote sensing and infrastructure databases. The national mortality rate averaged 1.64 per 100,000 children aged 0-19, with state-level rates ranging more than two-fold from 1.01 in Durango to 2.35 in Tabasco. High-mortality clusters are concentrated along the Gulf of Mexico coast and southeastern states; low-mortality zones lie predominantly in the arid west and northwest. High-mortality areas are systematically characterized by greater annual precipitation, lower aridity, permeable sedimentary geology, and higher forest cover-landscape conditions that collectively maximize infiltration and groundwater contamination risk. All five high-mortality clusters spatially overlap with oil and gas infrastructure, with three coinciding with zones of intensive extraction along the Gulf coast. These findings suggest that groundwater vulnerability and industrial contamination, rather than genetic predisposition, are primary spatial determinants of childhood ALL mortality in Mexico, pointing toward preventable, structurally driven disease burden. Childhood leukemia is the leading cancer among Mexican children, with death rates particularly high in some regions. While better treatments have helped more diagnosed children survive leukemia, we still don't know what in the environment is causing so many children to get sick in the first place. This study mapped where children are dying from leukemia across Mexico to see if patterns emerge. We analyzed data at a detailed geographic scale to avoid missing important local patterns that broader averages might hide. We found that areas with the most deaths tend to get more rain, sit on rock types that let water seep through easily, and have more forest cover, conditions that could help pollution spread through water systems like rivers and aquifers to which vulnerable children may be exposed. Many of these high‐risk areas also have a strong presence of oil and gas operations, which can release cancer‐causing chemicals. Our findings suggest that contaminated water may be an important and underappreciated reason why leukemia kills so many children in certain parts of Mexico. If confirmed, this would mean that a significant share of these deaths could be prevented by focusing prevention efforts where environmental risks are highest, not just treating the disease after it occurs.
The Operational Design Domain (ODD) defines the conditions under which automated driving and driver-assistance systems are expected to operate. This study evaluates the ODD of a camera-based Lane Support System (LSS) using direct Mobileye 6.0 lane-detection quality outputs. A large-scale hybrid factorial-observational field design covered 6 different Light × Weather combinations across 9,351 road sections on two-lane rural roads with wide variability in lane-marking retroreflectivity (RL) and road horizontal alignment and cross section characteristics. Statistical and machine-learning classification models were calibrated and compared to analyze the relationships between lane-marking quality and environmental, road, and traffic features. An AutoML-LightGBM pipeline with SMOTE-based class-imbalance treatment achieved the best accuracy of 0.81. SHAP analysis identified low RL, rain, night conditions, narrow lanes, and high curvature as contributors to critical detection conditions. Because standard ML is optimized for prediction rather than causal inference, Double Machine Learning was added to estimate adjusted effects from observational data. Higher RL, higher speed, and wider lanes were associated with better expected Mobileye quality scores, whereas rain, night conditions, and higher curvature were associated with lower detection quality. SHAP dependence and conditional SHAP analyses supported the identification of maintenance-mitigable infrastructure constraints and harder environmental/geometric ODD limits. One practical result is that RL transitions from low-quality detection mainly occur within 120-150 mcd/(m2·lx) across the majority of environmental and physical conditions, although this transition is less evident under sharp curvature or rain.
Urban rivers are facing severe water quality fluctuations and prolonged recovery challenges due to stormwater runoff pollution amid rapid urbanization and climate change. This study proposes a dynamic water quality resilience assessment framework by coupling the Storm Water Management Model (SWMM) with a completely mixed river model. Grounded in system performance curve theory, the framework integrates a failure index with nonlinear penalty coefficients to quantify exceedance magnitude, duration, and the full response-recovery trajectory, from resistance and absorption to restoration, thereby overcoming the limitations of traditional static indicators. Applied to the Xixiang River, a typical rain-fed urban river in Shenzhen, the framework revealed pronounced seasonal and pollutant-specific vulnerabilities under baseline conditions. Multi-scenario simulations demonstrated that Low-impact Development (LID) significantly enhances water quality resilience through source-load reduction, peak-flow attenuation, and hydrograph smoothing. At 80% LID coverage under a 1-year return period storm, loads of Chemical Oxygen Demand (CODcr), Total Phosphorus (TP), Total Nitrogen (TN), and Ammonia Nitrogen (NH3-N) decreased by 60.33-78.11%, with markedly improved resilience and faster recovery times across wet and dry seasons. The results highlight the critical role of source control in bolstering urban river resilience under climate change, while revealing LID's limitations for extreme events and dissolved pollutants. The proposed framework provides a robust and transferable tool for resilience-oriented urban water management and supports integrated "gray-green-blue" strategies for sustainable and climate-resilient urban aquatic ecosystems.
Multidisciplinary (MDT) management of sarcoma patients, with adherence to clinical practice guidelines (CPG), has a positive impact on patients' outcomes. Sarcoma European & Latin-American Network (SELNET) is an international consortium aiming to improve sarcoma management in Ibero-America. This study presents data from the observational study Sar-Track, which assessed the adherence of SELNET institutions to CPGs. This study aimed to evaluate adherence to established quality indicators in the diagnostic and therapeutic management of patients with soft tissue sarcoma within the SELNET network. Data from sarcoma patients managed in SELNET institutions (2012-2022) were collected. The primary objective was to analyze the adherence to quality factors during diagnosis and treatment, comparing Ibero-American (IA) and Spanish (SP) institutions. Data from 1523 localized patients with sarcoma of limbs or trunk-wall were included in this analysis. The median age was 50 (28), with 780 (51%) being female in the whole series. Ethnicity data was not available. Patients managed in IA underwent fewer core biopsies [n = 138 (25%) vs n = 153 (57%), p < 0.0001], fewer magnetic resonance imaging (MRI) [n = 464 (59%) vs 500 (68%), p = 0.003], and fewer MDT discussions before surgery [n = 208 (27%) vs 737 (99.6%), p < 0.0001]. Surgical margin involvement occurred more often in IA cases [n = 215 (27%) vs n = 61 (9%), p < 0.0001], and less radiotherapy was administered in patients from IA [n = 172 (37%) vs n = 185 (62%), p < 0.0001]. This analysis establishes the baseline reality at the initial stage of SELNET, highlighting substantial opportunities for improving adherence to CPGs in Ibero-America. SELNET was supported by a European Horizon 2020 grant (H2020-SC1-BHC-2018-2020).
Fluopyram is a nematicide for use as an at-plant, in-furrow on cotton (among other crops) in the United States. Trials (33 total) were conducted in cotton fields with and without fluopyram applied at-plant, in-furrow in Meloidogyne incognita and Rotylenchulus reniformis infested fields. Root galling caused by M. incognita was reduced on average by 24% with fluopyram. The amount of rain for the first rain after planting did affect fluopyram activity, with 1, 2, and 3 cm rain predicted to reduce root galling by 15, 26, and 39%, respectively. On average, cotton lint yield in M. incognita tests was 7% higher with fluopyram (1,270 kg lint/ha) compared to no nematicide (1,185 kg lint/ha). Fluopyram did not significantly affect R. reniformis density or cotton lint yield in R. reniformis tests. Lint yield increased as the number of days until the first rain increased, and fluopyram-treated plots yielded more than no nematicide as days until the first rain increased. Lint yield decreased in R. reniformis fields as average air temperature increased (for the first 14 days), but the warmer the temperature, the bigger the difference between fluopyram (higher yields) than no nematicide (lower yields). There is strong evidence, even in a water-limited environment like west Texas, that significant early rains will improve fluopyram reduction of M. incognita galling and increase yield. The impact of fluopyram with R. reniformis is not as clear and trended opposite for beneficial environmental conditions compared to M. incognita.
Nutrient dynamics in bay-estuary systems are shaped by the complex interplay between hydrology, biogeochemistry and hydrodynamics. Riverine nutrient loads are widely recognized as primary drivers of nutrients, yet how these loads translate into internal nutrient dynamics across varying hydrologic and salinity regimes remains poorly resolved. We addressed this gap by investigating long-term (19 years) dynamics of dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) loads and their role in governing nutrient variability under varying freshwater inflow and salinity regimes in Apalachicola Bay, Florida. We applied a series of generalized additive models (GAMs) that incrementally accounted for freshwater inflow, nutrient load and estuarine salinity alongside Neural Additive Models (NAMs) with concurvity regularization to account for the correlation among these drivers. Our results showed that riverine nutrient load is the dominant driver of estuarine nutrient dynamics. Although freshwater inflow and riverine nutrient loads are often used interchangeably in past studies, we show that "inflow as a proxy for nutrient load" is an oversimplification, particularly under low-flow conditions. We further showed that estuarine DIN exhibits more predictable, seasonally coupled variability with freshwater inflow and salinity. In contrast, episodic and localized biogeochemical processes make DIP dynamics less predictable and obscure the influence of salinity on DIP at monthly timescales. However, we showed that the dependence of estuarine DIN concentrations on the riverine load is strongly structured by salinity regimes, with fresher conditions favoring this dependence. Increasing variability in sea level and freshwater supply under climate change can make these dynamics less predictable.
Gastrointestinal stromal tumor (GIST) is the most common mesenchymal tumor arising from the gastrointestinal tract. Accurate pathological diagnosis and appropriate treatment for this malignancy require a multidisciplinary approach. In consideration of the differences in clinical practice between Asian and Western countries, the Asian Consensus Guidelines for the Diagnosis and Management of GISTs were published in 2016 by multidisciplinary experts in Asian countries (Japan, Korea, China, and Taiwan). Given the accumulation of new evidence since the previous publication, a multidisciplinary expert panel consisting of pathologists, surgical oncologists, and medical oncologists revised the Asian consensus guidelines. This narrative review provides updated consensus recommendations reflecting available evidence, expert opinion, and current clinical practice for the diagnosis and management of GIST in Asian countries.
Rivers are dynamic geomorphological systems that frequently alter their courses due to erosion, sediment deposition, channel migration, and flooding. Although such changes are normal, a sudden and major change like the diversion of the Kosi River in Bihar in 2008 can cause disastrous flooding, displacement and heavy land loss. The conventional methods are a poor fit because manual interpretation of satellites and hydrological modelling is time-consuming and has limited spatial-temporal resolution and lacks predictability. This study utilizes multi-source databases to present an AI-based Geo-Informatics framework for river course change prediction and disaster risk mitigation. Other than satellite imagery the data also includes hydrology, rain and soil data. The suggested hybrid architecture aims to jointly model the spatial river morphology and the evolution of the spatial pattern over time through the use of machine learning models (e.g. Random Forest, Gradient Boosting) and deep learning components (CNN, U-Net, LSTM/ConvLSTM). The framework has the ability to create predictive geospatial risk maps, forecasts of river migration over time, and interactive visualization products that support disaster preparedness and sustainable usage of water resources. Overall, the results demonstrate the potential of AI-driven Geo-Informatics to transform river monitoring from reactive assessment to proactive prediction, contributing to resilience building in accordance with the UN Sendai Framework (2015-2030) and the Sustainable Development Goals on climate action and water management.
Direct air capture (DAC) is promoted as an essential climate solution, yet thermodynamic and energy constraints make deployment at climate-relevant scales deeply problematic. Current DAC systems require 1,500-3,000 kWh per tonne of CO2 captured and stored-one to two orders of magnitude higher than point-source capture and far beyond what global clean-energy availability can support. Meeting even the lower bound of the IPCC's mid-century carbon-removal targets via DAC alone would demand more than half of today's global electricity, diverting clean energy away from direct decarbonization. Overreliance on DAC thus risks institutionalizing energy inefficiency and delaying essential emissions cuts. Historical precedents, from acid rain mitigation to ozone recovery, demonstrate that pollution is best addressed at its source. We propose a strategic realignment that prioritizes emissions prevention, strengthens natural carbon uptake through photosynthesis, and deploys proven, energy-positive, negative-emission pathways, such as biochar, that leverage rather than oppose fundamental thermodynamic constraints.
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Physical activities can be divided into indoor and outdoor activities. While outdoor activities offer enjoyable fitness opportunities, they are often limited by weather conditions. Unfavorable weather conditions such as cold, rain, fog, or snow can significantly re-duce physical activity levels, posing risks such as heat stress, dehydration, or cold-related injuries. To address these challenges, we have developed the concept of an automated eCoaching system that provides personalized activity recommendations based on real-time weather data. Our system uses an algorithm to annotate, process, and classify the collected data, generating tailored suggestions for indoor or outdoor exercise. This information is semantically represented using an Ontology framework. We have conducted a comprehensive study by collecting weather data for 18 months from thirteen cities in southern Norway. Furthermore, we have developed rules to determine the appropriate activity types corresponding to different weather conditions. The classification performance of the system has been rigorously evaluated using metrics such as accuracy, precision, recall, F1 score, and Matthews correlation coefficient (MCC). Remarkably, the decision tree classifier achieved an accuracy of 99.1%. To increase interpretability, we used local model-independent interpretable explanations (LIME) to explain individual predictions. The consistency of the Ontology model has been verified using inference, providing a reliable semantic representation and efficient rule-based recommendation modeling. In addition, we have developed various test cases of the system to evaluate eCoaching recommendations under different weather scenarios. This approach provides users with accurate and contextually relevant guidance, promoting continuous physical activity regardless of external weather conditions.
Here we present a 15-year record (2009-2023) of quality-controlled, one-minute rainfall observations from the Da-Tun rain gauge network (DTRGN), deployed to capture fine-scale precipitation variability over Da-Tun Mountain (DT). DT, situated immediately inland of northern Taiwan's coast, is an isolated three-dimensional volcanic massif ~15 km wide and ~1 km high, and represents a key climatological center of heavy orographic rainfall because tropical cyclones and northeasterly monsoonal flow frequently bring abundant moisture and precipitation to this mountain barrier. The DTRGN comprises 25 tipping-bucket rain gauges measuring rainfall across major mountain peaks, slopes, and valleys that are potential hotspots of orographically enhanced precipitation. We additionally provide temporal aggregations of one-minute rainfall observations at 10-minute, hourly, daily, and monthly intervals to support the research community. As a high-density, station-based rainfall dataset, DTRGN serves as an essential benchmark and resource for a broad spectrum of research and operational applications, including studies of orographic precipitation processes, tropical cyclone rainfall characterization, radar-derived precipitation correction, hydrological modeling, and assessments of long-term precipitation trends in complex terrain.