Publikasjoner

Comparative hepatic gene expression profiling of rats treated with 1H, 1H, 2H, 2H-heptadecafluorodecan-1-o1 or with pentadecafluorooctanoic acid.

Nilsen, A.J.; Landin, M.A.; Haug, K.H.; Fonnum, F.; Berger, U.; Osmundsen, H.

Comparative Analysis of Deep Learning and Statistical Models for Air Pollutants Prediction in Urban Areas

Naz, Fareena; McCann, Conor; Fadim, Muhammad; Cao, Tuan-Vu; Hunter, Ruth; Nguyen, Trung Viet; Nguyen, Long D.; Duong, Trung Q.

Rapid growth in urbanization and industrialization leads to an increase in air pollution and poor air quality. Because of its adverse effects on the natural environment and human health, it’s been declared a “silent public health emergency”. To deal with this global challenge, accurate prediction of air pollution is important for stakeholders to take required actions. In recent years, deep learning-based forecasting models show promise for more effective and efficient forecasting of air quality than other approaches. In this study, we made a comparative analysis of various deep learning-based single-step forecasting models such as long short term memory (LSTM), gated recurrent unit (GRU), and a statistical model to predict five air pollutants namely Nitrogen Dioxide (NO 2 ), Ozone (O 3 ), Sulphur Dioxide (SO 2 ), and Particulate Matter (PM2.5, and PM10). For empirical evaluation, we used a publicly available dataset collected in Northern Ireland, using an air quality monitoring station situated in Belfast city centre. It measures the concentration of air pollutants. The performance of forecasting models is evaluated based on three performance metrics: (a) root mean square error (RMSE), (b) mean absolute error (MAE) and (c) R-squared ( R2 ). The result shows that deep learning models consistently achieved the least RMSE compared to the statistical models with a value of 0.59. In addition, the deep learning model is also found to have the highest R2 score of 0.856.

IEEE (Institute of Electrical and Electronics Engineers)

Communication, collaboration, interaction. ACCENT Web Portal. NILU PP

Kobernus, M.J.; Logna, R.A.; Tørseth, K.

Communicating uncertainty to users: from number to perception. NILU F

Lahoz, W.

Common solutions for power, communication and robustness in operations of large measurement networks within Research Infrastructures.

Huber, R.; Beranzoli, L.; Fiebig, M.; Gilbert, O.; Laj, P.; Mazzola, M.; Paris, J.-D.; Pedersen, H.; Stocker, M.; Vitale, V.; Waldmann, C.; the ENVRI WP3 Team.

Common eider and herring gull as indicators of contaminants in an urban fjord system

Ruus, Anders; Helberg, Morten; Bæk, Kine; Enge, Ellen Katrin; Borgå, Katrine

Common Considerations for Genotoxicity Assessment of Nanomaterials

Elespuru, Rosalie K.; Doak, Shareen H.; Collins, Andrew Richard; Dusinska, Maria; Pfuhler, Stefan; Manjanatha, Mugimane; Cardoso, Renato M.S.; Chen, Connie L.

Genotoxicity testing is performed to determine potential hazard of a chemical or agent for direct or indirect DNA interaction. Testing may be a surrogate for assessment of heritable genetic risk or carcinogenic risk. Testing of nanomaterials (NM) for hazard identification is generally understood to require a departure from normal testing procedures found in international standards and guidelines. A critique of the genotoxicity literature in Elespuru et al., 2018, reinforced evidence of problems with genotoxicity assessment of nanomaterials (NM) noted by many previously. A follow-up to the critique of problems (what is wrong) is a series of methods papers in this journal designed to provide practical information on what is appropriate (right) in the performance of genotoxicity assays altered for NM assessment. In this “Common Considerations” paper, general considerations are addressed, including NM characterization, sample preparation, dosing choice, exposure assessment (uptake) and data analysis that are applicable to any NM genotoxicity assessment. Recommended methods for specific assays are presented in a series of additional papers in this special issue of the journal devoted to toxicology methods for assessment of nanomaterials: the In vitro Micronucleus Assay, TK Mutagenicity assays, and the In vivo Comet Assay. In this context, NM are considered generally as insoluble particles or test articles in the nanometer size range that present difficulties in assessment using techniques described in standards such as OECD guidelines.

Frontiers Media S.A.

Comet in Germ Cells = CIG

Olsen, Ann-Karin Hardie

Combining plasma measurements and mechanistic modeling to explore the effect of POPs on type 2 diabetes mellitus in Norwegian women.

Rylander, C.; Sandanger, T.M.; Nøst, T.H.; Breivik, K.; Lund, E.

Combining in vitro toxicity data with QSARs and mass balance exposure models for high throughput risk assessment.

Arnot, J.; Brown, T.; Breivik, K.; Thomas, R.; Wetmore, B.

Combining Advanced Analytical Methodologies to Uncover Suspect PFAS and Fluorinated Pharmaceutical Contributions to Extractable Organic Fluorine in Human Serum (Tromsø Study)

Cioni, Lara; Nikiforov, Vladimir; Benskin, Jonathan P.; Coelho, Ana Carolina; Dudášová, Silvia; Lauria, Melanie; Lechtenfeld, Oliver J.; Plassmann, Merle M.; Reemtsma, Thorsten; Sandanger, Torkjel Manning; Herzke, Dorte

A growing number of studies have reported that routinely monitored per- and polyfluoroalkyl substances (PFAS) are not sufficient to explain the extractable organic fluorine (EOF) measured in human blood. In this study, we address this gap by screening pooled human serum collected over 3 decades (1986–2015) in Tromsø (Norway) for >5000 PFAS and >300 fluorinated pharmaceuticals. We combined multiple analytical techniques (direct infusion Fourier transform ion cyclotron resonance mass spectrometry, liquid chromatography-Orbitrap-high-resolution mass spectrometry, and total oxidizable precursors assay) in a three-step suspect screening process which aimed at unequivocal suspect identification. This approach uncovered the presence of one PFAS and eight fluorinated pharmaceuticals (including some metabolites) in human serum. While the PFAS suspect only accounted for 2–4% of the EOF, fluorinated pharmaceuticals accounted for 0–63% of the EOF, and their contribution increased in recent years. Although fluorinated pharmaceuticals often contain only 1–3 fluorine atoms, our results indicate that they can contribute significantly to the EOF. Indeed, the contribution from fluorinated pharmaceuticals allowed us to close the organofluorine mass balance in pooled serum from 2015, indicating a good understanding of organofluorine compounds in humans. However, a portion of the EOF in human serum from 1986 and 2007 still remained unexplained.

Combined observational and modeling based study of the aerosol indirect effect.

Storelvmo, T.; Kristjánsson, J.E.; Myhre, G.; Johnsrud, M.; Stordal, F.