Biology subjects

Milner, R.

Publications and source records attributed to Milner, R..

4 recordsLinked to original sources

bioRxiv2026

Modelling habitat suitability for multiple priority weed species to predict invasion hotspots for strategic management in complex landscapes

Established invasive alien plant species require ongoing, costly management to reduce harm to agricultural and environmental values. Heterogeneous landscapes are often under threat from multiple long-established invasive plants, whose simultaneous management presents strategic and tactical challenges. Systematic monitoring of weed populations enables more strategic management by providing more detailed insights into invasion threats than the more readily available presence-only data. We leveraged six years of weed density monitoring and high-resolution environmental data to model habitat suitability for four high-priority weeds across the Australian Capital Territory. We developed habitat suitability maps for African lovegrass, Chilean needle grass, serrated tussock grass and St Johns wort. Maps were then standardised and combined to derive potential multi-species invasion hotspots at different management thresholds and explore patterns of secondary invasion threat. While some species shared environmental drivers such as elevation, each responded to distinct combinations and interactions among environmental drivers. Overall, we found approximately 71% of land had suitability for multiple weeds at various densities, but only 11% is suitable for two or more species to reach high densities. Areas suitable for most weeds often had St Johns wort as the next most suitable invader. These findings could improve management efficiency and reduce intervention challenges by focussing integrated control across species in a small number of priority areas. These findings support spatially explicit, multi-species decision-support tools for surveillance, prioritisation and strategic landscape-scale control.

ecology↗

bioRxiv2024

A Patient-Specific Morphoelastic Growth Model of Aortic Dissection Evolution

The human aorta undergoes complex morphologic changes that indicate the evolution of disease. Finite element analysis enables the prediction of aortic pathologic states, but the absence of a biomechanical understanding hinders the applicability of this computational tool. We incorporate geometric information from computed tomography angiography (CTA) imaging scans into finite element analysis (FEA) to predict a trajectory of future geometries for four aortic disease patients. Through defining a geometric correspondence between two patient scans separated in time, a patient-specific FEA model can recreate the deformation of the aorta between the two time points, showing pathologic growth drives morphologic heterogeneity. A shape-size geometric feature space plotting the variance of the shape index versus the inverse square root of aortic surface area ({delta}[S] vs. [Formula]) quantitatively demonstrates the simulated breakdown in aortic shape. An increase in {delta}[S] closely parallels the true geometric progression of aortic disease patients.

bioengineering↗

bioRxiv2023

mRNA challenge predicts brain cancer immunogenicity and response to checkpoint inhibitors

To prospectively determine whether brain tumors will respond to immune checkpoint inhibitors (ICIs), we developed a novel mRNA vaccine as a viral mimic to elucidate cytokine release from brain cancer cells in vitro. Our results indicate that cytokine signatures following mRNA challenge differ substantially from ICI responsive versus non-responsive murine tumors. These findings allow for creation of a diagnostic assay to quickly assess brain tumor immunogenicity, allowing for informed treatment with ICI or lack thereof in poorly immunogenic settings.

cancer biology↗

bioRxiv2022

Aortic Shape Space Topology

Clinical imaging modalities are a mainstay of modern disease management, but the full utilization of imaging-based data remains elusive. Aortic disease is defined by anatomic scalars quantifying aortic size, even though aortic disease progression initiates complex shape changes. We present an imaging-based geometric descriptor, inspired by fundamental ideas from topology and soft-matter physics that captures dynamic shape evolution. The aorta is reduced to a two-dimensional mathematical surface in space whose geometry is fully characterized by the local principal curvatures. Disease causes deviation from the smooth bent cylindrical shape of normal aortas, leading to a family of highly heterogeneous surfaces of varying shapes and sizes. To deconvolute changes in shape from size, the shape is characterized using integrated Gaussian curvature or total curvature. The fluctuation in total curvature ({delta}K) across aortic surfaces captures heterogeneous morphologic evolution by characterizing local shape changes. We discover that aortic morphology evolves with a power-law defined behavior with rapidly increasing{delta} K forming the hallmark of aortic disease. Divergent{delta} K is seen for highly diseased aortas indicative of impending topologic catastrophe or aortic rupture. We also show that aortic size (surface area or enclosed aortic volume) scales as a generalized cylinder for all shapes. Classification accuracy for predicting aortic disease state (normal, diseased with successful surgery, and diseased with failed surgical outcomes) is 92.8 {+/-}1.7%. The analysis of{delta} K can be applied on any three-dimensional geometric structure and thus may be extended to other clinical problems of characterizing disease through captured anatomic changes.

bioengineering↗