Machine learning models in trusted research environments - Understanding operational risks (2023)
Journal Article
Ritchie, F., Tilbrook, A., Cole, C., Jefferson, E., Krueger, S., Mansouri-Benssassi, E., …Smith, J. (2023). Machine learning models in trusted research environments - Understanding operational risks. International Journal of Population Data Science, 8(1), Article 2165. https://doi.org/10.23889/ijpds.v8i1.2165

IntroductionTrusted research environments (TREs) provide secure access to very sensitive data for research. All TREs operate manual checks on outputs to ensure there is no residual disclosure risk. Machine learning (ML) models require very large amou... Read More about Machine learning models in trusted research environments - Understanding operational risks.

NanoBiT‐ and NanoBiT/BRET‐based assays allow the analysis of binding kinetics of Wnt‐3a to endogenous Frizzled 7 in a colorectal cancer model (2023)
Journal Article
Grätz, L., Sajkowska-Kozielewicz, J. J., Wesslowski, J., Kinsolving, J., Bridge, L. J., Petzold, K., …Kozielewicz, P. (in press). NanoBiT‐ and NanoBiT/BRET‐based assays allow the analysis of binding kinetics of Wnt‐3a to endogenous Frizzled 7 in a colorectal cancer model. British Journal of Pharmacology, https://doi.org/10.1111/bph.16090

Background and Purpose: Wnt binding to Frizzleds (FZD) is a crucial step that leads to the initiation of signalling cascades governing multiple processes during embryonic development, stem cell regulation and adult tissue homeostasis. Recent efforts... Read More about NanoBiT‐ and NanoBiT/BRET‐based assays allow the analysis of binding kinetics of Wnt‐3a to endogenous Frizzled 7 in a colorectal cancer model.

Correct for the wrong reason: Why we should know more about mathematical common student errors in e-assessment questions (2023)
Journal Article
Sikurajapathi, B., Henderson, K., & Gwynllyw, R. (2023). Correct for the wrong reason: Why we should know more about mathematical common student errors in e-assessment questions. MSOR Connections, 21(Special Issue: CETL-MSOR Conference 2022 Part 1), 43-51. https://doi.org/10.21100/msor.v21i1

Students may arrive at an incorrect answer when answering a mathematical question due to several reasons, such as random errors, calculation errors or misreading the question. Such errors are sometimes referred to as Common Student Errors (CSEs). Thi... Read More about Correct for the wrong reason: Why we should know more about mathematical common student errors in e-assessment questions.