J
James G. Lefevre
Researcher at University of Queensland
Publications - 51
Citations - 999
James G. Lefevre is an academic researcher from University of Queensland. The author has contributed to research in topics: Computer science & Complete graph. The author has an hindex of 12, co-authored 49 publications receiving 793 citations.
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Journal ArticleDOI
Global Quantification of Tissue Dynamics in the Developing Mouse Kidney
Kieran M. Short,Alexander N. Combes,James G. Lefevre,Adler Ju,Kylie Georgas,Timothy O. Lamberton,Oliver Cairncross,Bree Rumballe,Andrew P. McMahon,Nicholas A. Hamilton,Ian M. Smyth,Melissa H. Little +11 more
TL;DR: A comprehensive, quantitative, multiscale analysis of mammalian kidney development in which changes in cell number, compartment volumes, and cellular dynamics across the entirety of organogenesis are measured, focusing on two key nephrogenic progenitor populations: the ureteric epithelium and the cap mesenchyme.
Journal ArticleDOI
Cortical F-actin stabilization generates apical–lateral patterns of junctional contractility that integrate cells into epithelia
Selwin K. Wu,Guillermo A. Gomez,Magdalene Michael,Suzie Verma,Hayley L. Cox,James G. Lefevre,Robert G. Parton,Nicholas A. Hamilton,Zoltan Neufeld,Alpha S. Yap +9 more
TL;DR: It is demonstrated that N-WASP enhances apical junctional tension by stabilizing local F-actin networks, which otherwise undergo stress-induced turnover and is proposed to regulate the landscape of intra-junctional contractility to determine whether or not cells integrate into epithelial populations.
Journal ArticleDOI
Independent contrasts and PGLS regression estimators are equivalent
TL;DR: It is proved that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameters of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution.
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Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer.
TL;DR: Characterising the tissue by classifying it into 12 meaningful dermatological classes, including hair follicles, sweat glands as well as identifying the well-defined stratified layers of the skin helps inform ways in which future computer aided diagnosis systems could be applied usefully in a clinical setting with human interpretable outcomes.
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Cap mesenchyme cell swarming during kidney development is influenced by attraction, repulsion, and adhesion to the ureteric tip.
TL;DR: Using ex vivo timelapse imaging, it is shown that cells of the cap mesenchyme are highly motile, and that the resulting swarming behaviour maintains a distinct cap meschyme domain while facilitating dynamic remodelling in response to underlying changes in the tip.