Fant 9998 publikasjoner. Viser side 393 av 400:
2025
Fungus-farming termites cultivate a Termitomyces fungus monoculture in enclosed gardens (combs) free of other fungi, except during colony declines, where Pseudoxylaria spp. stowaway fungi appear and take over combs. Here, we determined Volatile Organic Compounds (VOCs) of healthy Macrotermes bellicosus nests in nature and VOC changes associated with comb decay during Pseudoxylaria takeover. We identified 443 VOCs and unique volatilomes across samples and nest volatilomes that were mainly composed of fungus comb VOCs with termite contributions. Few comb VOCs were linked to chemical changes during decay, but longipinocarvone and longiverbenone were only emitted during comb decay. These terpenes may be involved in Termitomyces defence against antagonistic fungi or in fungus-termite signalling of comb state. Both comb and Pseudoxylaria biomass volatilomes contained many VOCs with antimicrobial activity that may serve in maintaining healthy Termitomyces monocultures or aid in the antagonistic takeover by Pseudoxylaria during colony decline. We further observed a series of oxylipins with known functions in the regulation of fungus germination, growth, and secondary metabolite production. Our volatilome map of the fungus-farming termite symbiosis provides new insights into the chemistry regulating complex interactions and serves as a valuable guide for future work on the roles of VOCs in symbioses.
2025
prismAId is an open-source tool designed to streamline systematic literature reviews by leveraging generative AI models for information extraction. It offers an accessible, efficient, and replicable method for extracting and analyzing data from scientific literature, eliminating the need for coding expertise. Supporting various review protocols, including PRISMA 2020, prismAId is distributed across multiple platforms – Go, Python, Julia, R – and provides user-friendly binaries compatible with Windows, macOS, and Linux. The tool integrates with leading large language models (LLMs) such as OpenAI’s GPT series, Google’s Gemini, Cohere’s Command, and Anthropic’s Claude, ensuring comprehensive and up-to-date literature analysis. prismAId facilitates systematic reviews, enabling researchers to conduct thorough, fast, and reproducible analyses, thereby advancing open science initiatives.
2025
2025
Citizen-operated low-cost sensors for estimating outdoor particulate matter infiltration
Fine particulates observed indoors exhibit high variability, influenced by both indoor emission sources and the infiltration of outdoor particles through open spaces and the incomplete building insulation. This study examines the relationship between indoor and outdoor PM2.5 levels in Legionowo, Poland, using data from low-cost air quality sensors operated by citizens. The indoor PM2.5 was lower than outdoor levels (median PM2.5: 1.9–17.3 μg m–3 indoors and 6.7–27.9 μg m–3 outdoors), with occasional peaks attributed to potential indoor emission sources. Statistical analysis identified emission events—particularly during cooking and household-heating periods—occurring more frequently from October to April. During this period, nearly 17% of indoor PM2.5 measurements were attributed to indoor emission sources after 18:00 LT, representing a 7% increase compared to the May–September period. In the absence of indoor sources, outdoor particles accounted for 29% to 75% of indoor concentrations, highlighting the significance of infiltration. This research emphasizes how citizen-generated data using low-cost sensors, after post-processing, can provide decision-ready information as for example outdoor particles’ infiltration factors for each building. The knowledge of the infiltration factor enables the determination of the contribution of indoor and outdoor sources to each resident’s exposure to airborne PM. This information can help decision-makers in devising interventions such as prioritizing indoor ventilation, reducing indoor activities resulting in increased exposure, and addressing outdoor pollution sources.
2025
Towards a remote-sensing-driven model of isoprene emissions from Alpine tundra
Abstract This study investigates isoprene emissions in a high-latitude Alpine tundra ecosystem, focusing on using near-field remote sensing of surface temperatures, the photochemical reflectance index (PRI) and normalized difference vegetation index (NDVI), and meteorological measurements to model these emissions. Isoprene is a key biogenic volatile organic compound (BVOC) emitted by select plants, which can impact atmospheric chemistry and climate. Increased temperatures, particularly in high latitudes, may enhance isoprene emissions due to extended growing seasons and heightened plant stress. The research was conducted in Finse, Norway, where isoprene and CO 2 fluxes were measured with eddy covariance alongside spectral and meteorological data, and surface temperature. A random forest (RF) model was developed to predict isoprene fluxes, considering the variable importance of different environmental factors. The results showed that surface temperature and CO 2 flux were consistently important predictors, across three differential temporal data aggregations (hourly, daily, weekly), while the PRI demonstrated low predictive power, possibly due to the heterogeneous vegetation and variable light conditions. The NDVI was more effective than anticipated, likely linked to phenological changes in vegetation. Model performance varied with temporal resolution, with weekly data achieving the highest predictive accuracy ( R 2 up to 0.76). The RF model accurately reflected seasonal emission patterns but underestimated short-term peaks, suggesting the potential to combine machine learning with process-based modelling. This research highlights the promise of proxy data from remote sensing for scaling BVOC emission models to regional levels, essential for understanding climate impacts in Arctic ecosystems.
2025
Personalized approaches are required for stroke management due to the variability in symptoms, triggers, and patient characteristics. An innovative stroke recommendation system that integrates automatic predictive analysis with semantic knowledge to provide personalized recommendations for stroke management is proposed by this paper. Stroke exacerbation are predicted and the recommendations are enhanced by the system, which leverages automatic Tree-based Pipeline Optimization Tool (TPOT) and semantic knowledge represented in an OWL Ontology (StrokeOnto). Digital sovereignty is addressed by ensuring the secure and autonomous control over patient data, supporting data sovereignty and compliance with jurisdictional data privacy laws. Furthermore, classifications are explained with Local Interpretable Model-Agnostic Explanations (LIME) to identify feature importance. Tailored interventions based on individual patient profiles are provided by this conceptual model, aiming to improve stroke management. The proposed model has been verified using public stroke dataset, and the same dataset has been utilized to support ontology development and verification. In TPOT, the best Variance Threshold + DecisionTree Classifier pipeline has outperformed other supervised machine learning models with an accuracy of 95.2%, for the used datasets. The Variance Threshold method reduces feature dimensionality with variance below a specified threshold of 0.1 to enhance predictive accuracy. To implement and evaluate the proposed model in clinical settings, further development and validation with more diverse and robust datasets are required.
2025
Sb-PiPLU: A Novel Parametric Activation Function for Deep Learning
The choice of activation function—particularly non-linear ones—plays a vital role in enhancing the classification performance of deep neural networks. In recent years, a variety of non-linear activation functions have been proposed. However, many of these suffer from drawbacks that limit the effectiveness of deep learning models. Common issues include the dying neuron problem, bias shift, gradient explosion, and vanishing gradients. To address these challenges, we introduce a new activation function: Softsign-based Piecewise Parametric Linear Unit (Sb-PiPLU). This function offers improved non-linear approximation capabilities for neural networks. Its piecewise, parametric design allows for greater adaptability and flexibility, which in turn enhances overall model performance. We evaluated Sb-PiPLU through a series of image classification experiments across various Convolutional Neural Network (CNN) architectures. Additionally, we assessed its memory usage and computational cost, demonstrating that Sb-PiPLU is both stable and efficient in practical applications. Our experimental results show that Sb-PiPLU consistently outperforms conventional activation functions in both classification accuracy and computational efficiency. It achieved higher accuracy on multiple benchmark datasets, including CIFAR-10, CINIC-10, MWD, Brain Tumor, and SVHN, surpassing widely-used functions such as ReLU and Tanh. Due to its flexibility and robustness, Sb-PiPLU is particularly well-suited for complex image classification tasks.
2025
2025
Mapping human-nature archetypes to guide global biodiversity, food security and land use policy
Reconciling biodiversity conservation, food security, and sustainable agriculture at global scale requires a clear understanding of regional social-ecological opportunities and challenges. This understanding helps untap regional contributions to better achieve global policy targets, such as those framed in the Kunming-Montreal Global Biodiversity Framework (GBF). Yet previous global syntheses of social-ecological interlinkages remain limited in thematic and spatial detail, restricting the discussion of regional contributions and targeted policy implementation. Here, we present 25 human-nature archetypes derived from clustering of global social-ecological data revealing regional opportunities and challenges for meeting global policy targets. Our results differentiate regions with large conservation opportunities from those well suited for ecological restoration or ecological intensification. They highlight the widespread need for improving governance to enhance food security and re-design agricultural systems. Overall, our analysis supports international and national decision makers in tailoring GBF targets to regional specificities in order to more effectively achieve global sustainability goals.
2025
Carbonaceous aerosols (CA), composed of black carbon (BC) and organic matter (OM), significantly impact the climate. Light absorption properties of CA, particularly of BC and brown carbon (BrC), are crucial due to their contribution to global and regional warming. We present the absorption properties of BC (bAbs,BC) and BrC (bAbs,BrC) inferred using Aethalometer data from 44 European sites covering different environments (traffic (TR), urban (UB), suburban (SUB), regional background (RB) and mountain (M)). Absorption coefficients showed a clear relationship with station setting decreasing as follows: TR > UB > SUB > RB > M, with exceptions. The contribution of bAbs,BrC to total absorption (bAbs), i.e. %AbsBrC, was lower at traffic sites (11–20 %), exceeding 30 % at some SUB and RB sites. Low AAE values were observed at TR sites, due to the dominance of internal combustion emissions, and at some remote RB/M sites, likely due to the lack of proximity to BrC sources, insufficient secondary processes generating BrC or the effect of photobleaching during transport. Higher bAbs and AAE were observed in Central/Eastern Europe compared to Western/Northern Europe, due to higher coal and biomass burning emissions in the east. Seasonal analysis showed increased bAbs, bAbs,BC, bAbs,BrC in winter, with stronger %AbsBrC, leading to higher AAE. Diel cycles of bAbs,BC peaked during morning and evening rush hours, whereas bAbs,BrC, %AbsBrC, AAE, and AAEBrC peaked at night when emissions from household activities accumulated. Decade-long trends analyses demonstrated a decrease in bAbs, due to reduction of BC emissions, while bAbs,BrC and AAE increased, suggesting a shift in CA composition, with a relative increase in BrC over BC. This study provides a unique dataset to assess the BrC effects on climate and confirms that BrC can contribute significantly to UV–VIS radiation presenting highly variable absorption properties in Europe.
2025
This study evaluated galvanostatic three-dimensional electrolysis using ceramic carbon foam anodes for the removal of emerging pollutants from wastewater and assessed transformation product formation. Five pollutants (paracetamol, triclosan, bisphenol A, caffeine, and diclofenac) were selected based on their detection in wastewater treatment plant effluents. Electrochemical oxidation was carried out on artificial wastewater spiked with these compounds under galvanostatic conditions (50, 125, and 250 mA) using a stainless steel tube electrolyzer with three ceramic carbon foam anodes and a stainless steel cathode. Decreasing pollutant concentrations were observed in all of the experiments. Nontarget chemical analysis using liquid chromatography coupled to a high-resolution mass spectrometer detected 338 features with increasing intensity including 12 confirmed transformation products (TPs). Real wastewater effluent spiked with the pollutants was then electrolyzed, again showing pollutant removal, with 9 of the 12 previously identified TPs present and increasing. Two TPs (benzamide and 2,4-dichlorophenol) are known toxicants, indicating the formation of a potential toxic by-product during electrolysis. Furthermore, electrolysis of unspiked real wastewater revealed the removal of five pharmaceuticals and a drug metabolite. While demonstrating electrolysis’ ability to degrade pollutants in wastewater, the study underscores the need to investigate transformation product formation and toxicity implications of the electrolysis process.
2025
På oppdrag fra Alcoa Norway AS dept. Mosjøen har NILU utført målinger i omgivelses-luft rundt smelteverket i Mosjøen. Målingene ble utført med aktiv prøvetaking (fluor, SO2, metaller, PAH, PM10) og passiv prøvetaking (SO2, støvnedfall). Måleprosjektet ble utført i perioden 22. mai – 19. august 2024. Alle målte komponenter var godt under de individuelle grenseverdier, målsettingsverdier og luftkvalitetskriterier i måleperioden. Siden Mosjøen er mest utsatt for utslipp fra aluminiumsverket i sommermånedene, pga. hovedvindretning fra fjorden, over smelteverket mot byen, blir måleresultatene et øvre anslag for bidraget fra smelteverket til konsentrasjonene i Mosjøen over hele året.
NILU
2025
2025
Monitoring of long-range transported air pollutants in Norway. Annual Report 2024
This report presents results from the monitoring of atmospheric composition and deposition of air pollution in 2024, and focuses on main components in air and precipitation, particulate and gaseous phase of inorganic constituents, particulate carbonaceous matter, ground level ozone and particulate matter.
NILU
2025
2025