Hilgendorf, J., Lautrette-Quinveros, H., Chan, W. S., Del Puerto, O.
, Ge, J., Noort, K. J., Ronanki, S., Loureiro, S., Spurgeon, D. & Selck, H. (2026).
Rethinking chemical hazard: an AOP-guided approach to non-conventional endpoints in the environmental assessment of neurotoxic, immunotoxic and metabolic toxic compounds.
Environmental Sciences Europe,
38(1), Article 12.
https://doi.org/10.1186/s12302-025-01292-6Olouladé, B. M., Gao, J., Al-Sabri, R., Chen, J. & Wu, Z. (2026).
Towards Better Graph Anomaly Detection: A Performance-Aware Neural Architecture Search Approach. In W. Senn, M. Sanguineti, A. Saudargiene, I. V. Tetko, A. E. P. Villa, V. Jirsa & Y. Bengio (Eds.),
Artificial Neural Networks and Machine Learning – ICANN 2025: 34th International Conference on Artificial Neural Networks, 2025, Proceedings (Vol. Part 1, pp. 285-297). Springer.
https://doi.org/10.1007/978-3-032-04558-4_23
Li, L., Li, B., Yin, X., Xia, Y., Yang, Y., Xu, X., Lam, T. T. Y., Leung, K. M. Y., Xu, F., Zhang, X., Guo, J., Schmitt, H., Wright, G. D., Midega, J., Bezuidenhout, C., Picão, R. C., Ahammad, S. Z., Smalla, K., Djordjevic, S. P. ... Zhang, T. (2025).
Assessing antimicrobial resistance connectivity across One Health sectors.
Nature Water,
3(10), 1100-1113.
https://doi.org/10.1038/s44221-025-00514-8