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Predicting the probability that a chemical causes steatosis using adverse outcome pathway Bayesian networks (AOPBNs)
Burgoon, L. D., Angrish, M., Garcia‐Reyero, N., Pollesch, N., Zupanic, A., & Perkins, E. (2020). Predicting the probability that a chemical causes steatosis using adverse outcome pathway Bayesian networks (AOPBNs). Risk Analysis, 40(3), 512-523. https://doi.org/10.1111/risa.13423
Common gene expression patterns in environmental model organisms exposed to engineered nanomaterials: a meta-analysis
Burkard, M., Betz, A., Schirmer, K., & Zupanic, A. (2020). Common gene expression patterns in environmental model organisms exposed to engineered nanomaterials: a meta-analysis. Environmental Science and Technology, 54(1), 335-344. https://doi.org/10.1021/acs.est.9b05170
Systems toxicology approach for testing chemical cardiotoxicity in larval zebrafish
Li, R., Zupanic, A., Talikka, M., Belcastro, V., Madan, S., Dörpinghaus, J., … Hoeng, J. (2020). Systems toxicology approach for testing chemical cardiotoxicity in larval zebrafish. Chemical Research in Toxicology. https://doi.org/10.1021/acs.chemrestox.0c00095
Repeatability and reproducibility of the RTgill-W1 cell line assay for predicting fish acute toxicity
Fischer, M., Belanger, S. E., Berckmans, P., Bernhard, M. J., Bláha, L., Coman Schmid, D. E., … Schirmer, K. (2019). Repeatability and reproducibility of the RTgill-W1 cell line assay for predicting fish acute toxicity. Toxicological Sciences, 169(2), 353-364. https://doi.org/10.1093/toxsci/kfz057
Building and applying quantitative adverse outcome pathway models for chemical hazard and risk assessment
Perkins, E. J., Ashauer, R., Burgoon, L., Conolly, R., Landesmann, B., Mackay, C., … Scholz, S. (2019). Building and applying quantitative adverse outcome pathway models for chemical hazard and risk assessment. Environmental Toxicology and Chemistry, 38(9), 1850-1865. https://doi.org/10.1002/etc.4505
Cell-based data to predict the toxicity of chemicals to fish. Commentary on the manuscript by Rodrigues et al., 2019. Cell-based assays seem not to accurately predict fish short-term toxicity of pesticides. <em>Environmental Pollution</em> 252
Schirmer, K., Stadnicka-Michalak, J., Belanger, S. E., Blaha, L., Bols, N. C., Dyer, S. D., … Zupanic, A. (2019). Cell-based data to predict the toxicity of chemicals to fish. Commentary on the manuscript by Rodrigues et al., 2019. Cell-based assays seem not to accurately predict fish short-term toxicity of pesticides. Environmental Pollution 252:476–482. Environmental Pollution, 254(B), 113060 (3 pp.). https://doi.org/10.1016/j.envpol.2019.113060
Intestinal fish cell barrier model to assess transfer of organic chemicals in vitro: an experimental and computational study
Schug, H., Maner, J., Begnaud, F., Berthaud, F., Gimeno, S., Schirmer, K., & Županič, A. (2019). Intestinal fish cell barrier model to assess transfer of organic chemicals in vitro: an experimental and computational study. Environmental Science and Technology, 53(20), 12062-12070. https://doi.org/10.1021/acs.est.9b04281
A fish intestinal barrier model to assess the interaction with chemicals <em>in vitro</em>
Schug, H. (2018). A fish intestinal barrier model to assess the interaction with chemicals in vitro [Doctoral dissertation]. École polytechnique fédérale de Lausanne.
Characterization of aquatic biofilms with flow cytometry
Sgier, L., Merbt, S. N., Tlili, A., Kroll, A., & Zupanic, A. (2018). Characterization of aquatic biofilms with flow cytometry. Journal of Visualized Experiments (136), e57655 (9 pp.). https://doi.org/10.3791/57655
Evaluation of phototrophic stream biofilms under stress: comparing traditional and novel ecotoxicological endpoints after exposure to diuron
Sgier, L., Behra, R., Schönenberger, R., Kroll, A., & Zupanic, A. (2018). Evaluation of phototrophic stream biofilms under stress: comparing traditional and novel ecotoxicological endpoints after exposure to diuron. Frontiers in Microbiology, 9, 2974 (11 pp.). https://doi.org/10.3389/fmicb.2018.02974
A validated algorithm for selecting non-toxic chemical concentrations
Stadnicka-Michalak, J., Knöbel, M., Županič, A., & Schirmer, K. (2018). A validated algorithm for selecting non-toxic chemical concentrations. ALTEX: Alternatives to Animal Experimentation, 35(1), 37-50. https://doi.org/10.14573/altex.1701231
Predominant asymmetrical stem cell fate outcome limits the rate of niche succession in human colonic crypts
Stamp, C., Zupanic, A., Sachdeva, A., Stoll, E. A., Shanley, D. P., Mathers, J. C., … Greaves, L. C. (2018). Predominant asymmetrical stem cell fate outcome limits the rate of niche succession in human colonic crypts. EBioMedicine, 31, 166-173. https://doi.org/10.1016/j.ebiom.2018.04.017
Green algae and networks for adverse outcome pathways
Zupanic, A., Pillai, S., Coman Schmid, D., & Schirmer, K. (2018). Green algae and networks for adverse outcome pathways. In N. Garcia-Reyero & C. Murphy (Eds.), A systems biology approach to advancing adverse outcome pathways for risk assessment (pp. 133-148). https://doi.org/10.1007/978-3-319-66084-4_7
Flow cytometry combined with viSNE for the analysis of microbial biofilms and detection of microplastics
Sgier, L., Freimann, R., Zupanic, A., & Kroll, A. (2016). Flow cytometry combined with viSNE for the analysis of microbial biofilms and detection of microplastics. Nature Communications, 7, 11587 (10 pp.). https://doi.org/10.1038/ncomms11587
Selenium uptake and methylation by the microalga <I>Chlamydomonas reinhardtii</I>
Vriens, B., Behra, R., Voegelin, A., Zupanic, A., & Winkel, L. H. E. (2016). Selenium uptake and methylation by the microalga Chlamydomonas reinhardtii. Environmental Science and Technology, 50(2), 711-720. https://doi.org/10.1021/acs.est.5b04169
Modeling and gene knockdown to assess the contribution of nonsense-mediated decay, premature termination, and selenocysteine insertion to the selenoprotein hierarchy
Zupanic, A., Meplan, C., Huguenin, G. V. B., Hesketh, J. E., & Shanley, D. P. (2016). Modeling and gene knockdown to assess the contribution of nonsense-mediated decay, premature termination, and selenocysteine insertion to the selenoprotein hierarchy. RNA, 22(7), 1076-1084. https://doi.org/10.1261/rna.055749.115
Ribosome Profiling
Zupanic, A., & Grellscheid, S. N. (2016). Ribosome Profiling. In A. M. Aransay & J. L. Lavín Trueba (Eds.), Field Guidelines for Genetic Experimental Designs in High-Throughput Sequencing (pp. 175-195). https://doi.org/10.1007/978-3-319-31350-4_8
Integrated stochastic model of DNA damage repair by non-homologous end joining and p53/p21- mediated early senescence signalling
Dolan, D. W. P., Zupanic, A., Nelson, G., Hall, P., Miwa, S., Kirkwood, T. B. L., & Shanley, D. P. (2015). Integrated stochastic model of DNA damage repair by non-homologous end joining and p53/p21- mediated early senescence signalling. PLoS Computational Biology, 11(5), 1-19. https://doi.org/10.1371/journal.pcbi.1004246
Detecting translational regulation by change point analysis of ribosome profiling data sets
Zupanic, A., Meplan, C., Grellscheid, S. N., Mathers, J. C., Kirkwood, T. B. L., Hesketh, J. E., & Shanley, D. P. (2014). Detecting translational regulation by change point analysis of ribosome profiling data sets. RNA, 20(10), 1507-1518. https://doi.org/10.1261/rna.045286.114