Author

P. McLoughlin

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Animal Behavior & Cognition· 1 Ecology· 1 Physiology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Research Focus:cell signaling & trafficking· 1 Research Focus:development & morphogenesis· 1 Approach:experimental assay· 1 Research Focus:host-pathogen & immune response· 1 Research Focus:metabolism & bioenergetics· 1 Organism:mouse· 1 Organism:multiple species· 1

Frequent Co-Authors

A. C. Ortega· 1 shared A. K. Reinking· 1 shared A. Keuroghlian· 1 shared A. Loison· 1 shared A. Rodgers· 1 shared B. Strickland· 1 shared C. Prokopenko· 1 shared D. Bernasconi· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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1 shared paperAnimal Behavior & Cognition

A. C. Ortega

Mutual co-authors in cluster

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3 papers

Recent·Most cited

Shear stress-induced restoration of pulmonary endothelial barrier function following ischaemia reperfusion injury requires VEGFR2 signalling

D. Walsh, D. S. Kostyunina, J. F. Boylan, P. McLoughlin

This study shows that shear stress helps restore endothelial barrier function after ischaemia reperfusion injury, and this process requires VEGFR2 signaling but not VEGFA binding.

bioRxiv · Jan 10, 2024Physiology

Assessing bias and robustness of social network metrics using GPS based radio-telemetry data

P. Kaur, S. Ciuti, F. Ossi, F. Cagnacci, N. Morellet, A. Loison, K. Atmeh, P. McLoughlin, A. K. Reinking, J. L. Beck, A. C. Ortega, M. Kauffman, M. S. Boyce, M. Salter-Townshend

This study evaluates the bias and robustness of social network metrics using GPS telemetry data from five ungulate species, showing that global metrics are robust while local metrics are unreliable with low sampling.

bioRxiv · Mar 30, 2023 · PreprintAnimal Behavior & Cognition

Solving the Sample Size Problem for Resource Selection Analysis

G. M. Street, J. R. Potts, L. Borger, J. Beasley, S. Demarais, J. M. Fryxell, P. McLoughlin, K. Monteith, C. Prokopenko, M. Ribeiro, A. Rodgers, B. Strickland, F. Van Beest, D. Bernasconi, L. Beumer, , E. Vander Wal

This paper provides mathematical expressions to determine optimal sample sizes for resource selection analyses, showing that fewer data may be sufficient for strong selection effects.

bioRxiv · Feb 22, 2021 · PreprintEcology