Found 9854 publications. Showing page 79 of 395:
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2015
Common Considerations for Genotoxicity Assessment of Nanomaterials
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.
2022
Seabirds like gulls are common indicators in contaminant monitoring. The herring gull (Larus argentatus) is a generalist with a broad range of dietary sources, possibly introducing a weakness in its representativeness of aquatic contamination. To investigate the herring gull as an indicator of contamination in an urban‐influenced fjord, the Norwegian Oslofjord, we compared concentrations of a range of lipophilic and protein‐associated organohalogen contaminants (OHCs), Hg, and dietary markers in blood (n = 15), and eggs (n = 15) between the herring gull and the strict marine‐feeding common eider (Somateria mollissima) in the breeding period of May 2017. Dietary markers showed that the herring gull was less representative of the marine food web than the common eider. We found higher concentrations of lipophilic OHCs (wet weight and lipid weight) and Hg (dry weight) in the blood of common eider (mean ± SE ∑PCB = 210 ± 126 ng/g ww, 60 600 ± 28 300 ng/g lw; mean Hg = 4.94 ± 0.438 ng/g dw) than of the herring gull (mean ± SE ∑PCB = 19.0 ± 15.6 ng/g ww, 1210 ± 1510 ng/g lw; mean Hg = 4.26 ± 0.438 ng/g dw). Eggs gave opposite results; higher wet weight and lipid weight OHC concentrations in the herring gull (mean ± SE ∑PCB = 257 ± 203 ng/g ww, 3240 ± 2610 ng/g lw) than the common eider (mean ± SE ∑PCB = 18.2 ± 20.8 ng/g ww, 101 ± 121 ng/g lw), resulting in higher OHC maternal transfer ratios in gulls than eiders. We suggest that the matrix differences are due to fasting during incubation in the common eider. We suggest that in urban areas, herring gull might not be representative as an indicator of marine contamination but rather urban contaminant exposure. The common eider is a better indicator of marine pollution in the Oslofjord. The results are influenced by the matrix choice, as breeding strategy affects lipid dynamics regarding the transfer of lipids and contaminants to eggs and remobilization of contaminants from lipids to blood during incubation, when blood is drawn from the mother. Our results illustrate the benefit of a multispecies approach for a thorough picture of contaminant status in urban marine ecosystems. Integr Environ Assess Manag 2020;00:1–12. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC)
John Wiley & Sons
2020
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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)
2023
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