Insights from explainable AI in oesophageal cancer team decisions.

Background: Clinician-led quality control into oncological decision-making is crucial for optimising patient care. Explainable artificial intelligence (XAI) techniques provide data-driven approaches to unravel how clinical variables influence this decision-making. We applied global XAI techniques to examine the impact of key clinical decision-drivers when mapped by a machine learning (ML) model, on the likelihood of receiving different oesophageal cancer (OC) treatment modalities by the multidisciplinary team (MDT).

Detection of anti-correlation of hot and cold baryons in galaxy clusters.

The largest clusters of galaxies in the Universe contain vast amounts of dark matter, plus baryonic matter in two principal phases, a majority hot gas component and a minority cold stellar phase comprising stars, compact objects, and low-temperature gas. Hydrodynamic simulations indicate that the highest-mass systems retain the cosmic fraction of baryons, a natural consequence of which is anti-correlation between the masses of hot gas and stars within dark matter halos of fixed total mass. We report observational detection of this anti-correlation based on 4 elements of a 9 × 9-element covariance matrix for nine cluster properties, measured from multi-wavelength observations of 41 clusters from the Local Cluster Substructure Survey.

Percent-Level Test of Isotropic Expansion Using Type Ia Supernovae.

We propose and implement a novel, robust, and nonparametric test of statistical isotropy of the expansion of the Universe and apply it to around 1000 type Ia supernovae from the Pantheon sample. We calculate the angular clustering of supernova magnitude residuals and compare it to the noise expected under the isotropic assumption. We also test for systematic effects and demonstrate that their effects are negligible or are already accounted for in our procedure.