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Welcome to Durham Research Online (DRO)

Durham Research Online (DRO) is the University’s Open Access repository for publications. The primary purpose of DRO is to provide open access to publications authored by staff and students affiliated with Durham University.

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Latest Additions

Optically induced charge-transfer in donor-acceptor-substituted p- and m- C2B10H12 carboranes (2024)
Journal Article
Wu, L., Holzapfel, M., Schmiedel, A., Peng, F., Moos, M., Mentzel, P., …Ji, L. (2024). Optically induced charge-transfer in donor-acceptor-substituted p- and m- C2B10H12 carboranes. Nature Communications, 15(1), Article 3005. https://doi.org/10.1038/s41467-024-47384-4

Icosahedral carboranes, C2B10H12, have long been considered to be aromatic but the extent of conjugation between these clusters and their substituents is still being debated. m- and p-Carboranes are compared with m- and p-phenylenes as conjugated bri... Read More about Optically induced charge-transfer in donor-acceptor-substituted p- and m- C2B10H12 carboranes.

The PAU Survey: a new constraint on galaxy formation models using the observed colour redshift relation (2024)
Journal Article
Manzoni, G., Baugh, C. M., Norberg, P., Cabayol, L., van den Busch, J. L., Wittje, A., …Tortorelli, L. (2024). The PAU Survey: a new constraint on galaxy formation models using the observed colour redshift relation. Monthly Notices of the Royal Astronomical Society, 530(2), 1394-1413. https://doi.org/10.1093/mnras/stae659

We use the GALFORM semi-analytical galaxy formation model implemented in the Planck Millennium N-body simulation to build a mock galaxy catalogue on an observer’s past lightcone. The mass resolution of this N-body simulation is almost an order of mag... Read More about The PAU Survey: a new constraint on galaxy formation models using the observed colour redshift relation.

MRL-Seg: Overcoming Imbalance in Medical Image Segmentation With Multi-Step Reinforcement Learning (2023)
Journal Article
Yang, F., Li, X., Duan, H., Xu, F., Huang, Y., Zhang, X., …Zheng, Y. (2024). MRL-Seg: Overcoming Imbalance in Medical Image Segmentation With Multi-Step Reinforcement Learning. IEEE Journal of Biomedical and Health Informatics, 28(2), 858-869. https://doi.org/10.1109/jbhi.2023.3336726

Medical image segmentation is a critical task for clinical diagnosis and research. However, dealing with highly imbalanced data remains a significant challenge in this domain, where the region of interest (ROI) may exhibit substantial variations acro... Read More about MRL-Seg: Overcoming Imbalance in Medical Image Segmentation With Multi-Step Reinforcement Learning.

Cell Senescence-Independent Changes of Human Skin Fibroblasts with Age (2024)
Journal Article
Fullard, N., Wordsworth, J., Welsh, C., Maltman, V., Bascom, C., Tasseff, R., …Shanley, D. (2024). Cell Senescence-Independent Changes of Human Skin Fibroblasts with Age. Cells, 13(8), Article 659. https://doi.org/10.3390/cells13080659

Skin ageing is defined, in part, by collagen depletion and fragmentation that leads to a loss of mechanical tension. This is currently believed to reflect, in part, the accumulation of senescent cells. We compared the expression of genes and proteins... Read More about Cell Senescence-Independent Changes of Human Skin Fibroblasts with Age.