An Inducible Luminescent System to Explore Parkinson's Disease-Associated Genes.

With emerging genetic association studies, new genes and pathways are revealed as causative factors in the development of Parkinson's disease (PD). However, many of these PD genes are poorly characterized in terms of their function, subcellular localization, and interaction with other components in cellular pathways. This represents a major obstacle towards a better understanding of the molecular causes of PD, with deeper molecular studies often hindered by a lack of high-quality, validated antibodies for detecting the corresponding proteins of interest.

Stratified epidemic model using a latent marked Hawkes process.

We extend the unstructured homogeneously mixing epidemic model introduced by Lamprinakou et al. (2023) to a finite population stratified by age bands. We model the actual unobserved infections using a latent marked Hawkes process and the reported aggregated infections as random quantities driven by the underlying Hawkes process.

Analysis of the Structure of Heavy Ion Irradiated LaFeO Using Grazing Angle X-ray Absorption Spectroscopy.

Crystalline ceramics are candidate materials for the immobilization of radionuclides, particularly transuranics (such as U, Pu, and Am), arising from the nuclear fuel cycle. Due to the α-decay of transuranics and the associated recoil of the parent nucleus, crystalline materials may eventually be rendered amorphous through changes to the crystal lattice caused by these recoil events. Previous work has shown irradiation of titanate-based ceramics to change the local cation environment significantly, particularly in the case of Ti which was shown to change from 6- to 5-fold coordination.

Using a latent Hawkes process for epidemiological modelling.

Understanding the spread of COVID-19 has been the subject of numerous studies, highlighting the significance of reliable epidemic models. Here, we introduce a novel epidemic model using a latent Hawkes process with temporal covariates for modelling the infections. Unlike other models, we model the reported cases via a probability distribution driven by the underlying Hawkes process.

A COVID-19 model for local authorities of the United Kingdom.

We propose a new framework to model the COVID-19 epidemic of the United Kingdom at the local authority level. The model fits within a general framework for semi-mechanistic Bayesian models of the epidemic based on renewal equations, with some important innovations, including a random walk modelling the reproduction number, incorporating information from different sources, including surveys to estimate the time-varying proportion of infections that lead to reported cases or deaths, and modelling the underlying infections as latent random variables. The model is designed to be updated daily using publicly available data.