Fant 9850 publikasjoner. Viser side 390 av 394:
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.
John Wiley & Sons
2025
Status report of air quality in Europe for year 2024, using validated and up-to-date data
This report presents summarised information on the status of air quality in Europe in 2024, based on Up-To-Date data (i.e. prior to final quality control) and validated air quality monitoring data officially reported by the member and cooperating countries of the EEA. It aims at giving more timely and preliminary information on the status of ambient air quality in Europe in 2024 for five key air pollutants (PM10, PM2.5, O3, NO2 and SO2). The report also gives a preliminary assessment of the progress towards meeting the European air quality standards for the protection of health and the World Health Organization air quality guideline levels, and compares the air quality status in 2024 with the previous years. The preliminary data reported for 2024 shows that 7% and 13% of the monitoring stations exceeded the EU standards for PM10 and O3, respectively. The WHO AQG for PM2.5, PM10, O3 and SO2 were exceeded by 93%, 59%, 98% and 3%, respectively. Exceedances of the NO2 limit value still occur in 7 reporting countries and NO2 WHO AQG occur in all reporting countries.
ETC/HE
2025
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.
Elsevier
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.
Elsevier
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.
American Chemical Society (ACS)
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
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Open biomass burning has major impacts globally and regionally on atmospheric composition. Fire emissions include particulate matter, tropospheric ozone precursors, and greenhouse gases, as well as persistent organic pollutants, mercury, and other metals. Fire frequency, intensity, duration, and location are changing as the climate warms, and modelling these fires and their impacts is becoming more and more critical to inform climate adaptation and mitigation, as well as land management. Indeed, the air pollution from fires can reverse the progress made by emission controls on industry and transportation. At the same time, nearly all aspects of fire modelling – such as emissions, plume injection height, long-range transport, and plume chemistry – are highly uncertain. This paper outlines a multi-model, multi-pollutant, multi-regional study to improve the understanding of the uncertainties and variability in fire atmospheric science, models, and fires' impacts, in addition to providing quantitative estimates of the air pollution and radiative impacts of biomass burning. Coordinated under the auspices of the Task Force on Hemispheric Transport of Air Pollution, the international atmospheric modelling and fire science communities are working towards the common goal of improving global fire modelling and using this multi-model experiment to provide estimates of fire pollution for impact studies. This paper outlines the research needs, opportunities, and options for the fire-focused multi-model experiments and provides guidance for these modelling experiments, outputs, and analyses that are to be pursued over the next 3 to 5 years. The paper proposes a plan for delivering specific products at key points over this period to meet important milestones relevant to science and policy audiences.
2025
2025
Sunnere byer: – Jeg ville prioritert enkel, bilfri tilgang til alt du trenger i hverdagen
Norges forskningsråd
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
2025
CompSafeNano project: NanoInformatics approaches for safe-by-design nanomaterials
The CompSafeNano project, a Research and Innovation Staff Exchange (RISE) project funded under the European Union's Horizon 2020 program, aims to advance the safety and innovation potential of nanomaterials (NMs) by integrating cutting-edge nanoinformatics, computational modelling, and predictive toxicology to enable design of safer NMs at the earliest stage of materials development. The project leverages Safe-by-Design (SbD) principles to ensure the development of inherently safer NMs, enhancing both regulatory compliance and international collaboration. By building on established nanoinformatics frameworks, such as those developed in the H2020-funded projects NanoSolveIT and NanoCommons, CompSafeNano addresses critical challenges in nanosafety through development and integration of innovative methodologies, including advanced in vitro models, in silico approaches including machine learning (ML) and artificial intelligence (AI)-driven predictive models and 1st-principles computational modelling of NMs properties, interactions and effects on living systems. Significant progress has been made in generating atomistic and quantum-mechanical descriptors for various NMs, evaluating their interactions with biological systems (from small molecules or metabolites, to proteins, cells, organisms, animals, humans and ecosystems), and in developing predictive models for NMs risk assessment. The CompSafeNano project has also focused on implementing and further standardising data reporting templates and enhancing data management practices, ensuring adherence to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Despite challenges, such as limited regulatory acceptance of New Approach Methodologies (NAMs) currently, which has implications for predictive nanosafety assessment, CompSafeNano has successfully developed tools and models that are integral to the safety evaluation of NMs, and that enable the extensive datasets on NMs safety to be utilised for the re-design of NMs that are inherently safer, including through prediction of the acquired biomolecule coronas which provide the biological or environmental identities to NMs, promoting their sustainable use in diverse applications. Future efforts will concentrate on further refining these models, expanding the NanoPharos Database, and working with regulatory stakeholders thereby fostering the widespread adoption of SbD practices across the nanotechnology sector. CompSafeNano's integrative approach, multidisciplinary collaboration and extensive stakeholder engagement, position the project as a critical driver of innovation in NMs SbD methodologies and in the development and implementation of computational nanosafety.
Elsevier
2025
The complex and dynamic nature of airborne fine particulate matter (PM2.5) has hindered understanding of its chemical composition, sources, and toxic effects. In the first steps of a larger study, here, we aimed to elucidate relationships between source regions, ambient conditions, and the chemical composition in water extracts of PM2.5 samples (n = 85) collected over 16 months at an observatory in the Yellow Sea. In each extract, we quantified elements and major ions and profiled the complex mixtures of organic compounds by nontarget mass spectrometry. More than 50,000 nontarget features were detected, and by consensus of in silico tools, we assigned a molecular formula to 13,907 features. Oxygenated compounds were most prominent, followed by mixed nitrogenated/oxygenated compounds, organic sulfates, and sulfonates. Spectral matching enabled identification or structural annotation of 43 substances, and a workflow involving SIRIUS and MS-DIAL software enabled annotation of 74 unknown per- and polyfluoroalkyl substances with primary source regions in China and the Korean Peninsula. Multivariate modeling revealed seasonal variations in chemistry, attributable to the combination of warmer temperatures and maritime source regions in summer and to cooler temperatures and source regions of China in winter.
2025
2025
Intrusion Detection Systems (IDS) are critical in safeguarding network infrastructures against malicious attacks. Traditional IDSs often struggle with knowledge representation, real-time detection, and accuracy, especially when dealing with high-throughput data. This paper proposes a novel IDS framework that leverages machine learning models, streaming data, and semantic knowledge representation to enhance intrusion detection accuracy and scalability. Additionally, the study incorporates the concept of Digital Sovereignty, ensuring that data control, security, and privacy are maintained according to national and regional regulations. The proposed system integrates Apache Kafka for real-time data processing, an automatic machine learning pipeline (e.g., Tree-based Pipeline Optimization Tool (TPOT)) for classifying network traffic, and OWL-based semantic reasoning for advanced threat detection. The proposed system, evaluated on NSL-KDD and CIC-IDS-2017 datasets, demonstrated qualitative outcomes such as local compliance, reduced data storage needs due to real-time processing, and improved adaptability to local data laws. Experimental results reveal significant improvements in detection accuracy, processing efficiency, and Sovereignty alignment.
Elsevier
2025