Paul Rees

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Professor

Swansea University College of Engineering

United Kingdom

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About

Professor Paul Rees is a faculty member at Swansea University College of Engineering in the United Kingdom. His research interests encompass a variety of areas including imaging flow cytometry, technical ability quantification in rowing, and the study of cell cycles in fission yeast. He has contributed to recent articles focusing on immunocompetent cell targeting, biomarker assays, and the cellular responses to COVID-19. Professor Rees is actively engaged in advancing methodologies in both environmental sampling and biomedical research.

Recent Grants

Grant: Close

Open access deep learning solutions for imaging flow cytometry

Open Date: 2017-09-30

Close Date: 2019-02-28

Grant: Close

Engineering Blood Diagnostics: Integrated Platforms for Advanced Detection and Analysis

Open Date: 2016-02-01

Close Date: 2021-01-31

Grant: Close

14 NSFBIO: Mining of imaging flow cytometry data for label free, single cell analysis

Open Date: 2015-09-24

Close Date: 2018-09-23

Grant: Close

Bilateral BBSRC-NSF/BIO: Mining of imaging flow cytometry data for label-free, single cell analysis

Open Date: 2015-07-15

Close Date: 2018-06-30

Grant: Close

EPSRC Healthcare Impact Partnership for new blood clotting diagnostics and management

Open Date: 2014-04-30

Close Date: 2017-04-29

Articles (17)

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2025

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2021

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2021

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2021

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2021

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2021

Collaborators (11)

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George Johnson

Associate Professor

Swansea University

UNITED KINGDOM

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University of Exeter

UNITED KINGDOM

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Paul Rees

Research Interests

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About

Recent Grants

Articles (17)

A novel measure to quantify technical ability in on-water rowing

Immunocompetent cell targeting by food-additive titanium dioxide

In the Murine and Bovine Maternal Mammary Gland Signal Transducer and Activator of Transcription 3 is Activated in Clusters of Epithelial Cells around the Day of Birth

Distinct lung cell signatures define the temporal evolution of diffuse alveolar damage in fatal COVID-19

A multi-biomarker micronucleus assay using imaging flow cytometry.

Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry

OPTIMAL: An OPTimized Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration.

Imaging flow cytometry

A quantitative and spatial analysis of cell cycle regulators during the fission yeast cycle

The effects of curve registration on linear models of jump performance and classification based on vertical ground reaction forces.

Determining jumping performance from a single body-worn accelerometer using machine learning.

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning.

Empirical comparison of genotoxic potency estimations: The in vitro DNA-damage ToxTracker endpoints versus the in vivo micronucleus assay

Biomimetic Scaffolds Modulate the Posttraumatic Inflammatory Response in Articular Cartilage Contributing to Enhanced Neoformation of Cartilaginous Tissue In Vivo.

Data-Driven Modeling of the Cellular Pharmacokinetics of Degradable Chitosan-Based Nanoparticles.

Developing ovine mammary terminal duct lobular units have a dynamic mucosal and stromal immune microenvironment.

Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry

Collaborators (11)

Eleonora Po

Federica Banche Niclot

Andrew Filby

Catharien Hilkens

Peter Bankhead

John Love

George Johnson

Mark White

James Cronin

Katherine Hughes

Ann Power

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