IDyOMpy: A new Python-based model for the statistical analysis of musical expectations.

Background: IDyOM (Information Dynamics of Music) is the statistical model of music the most used in the community of neuroscience of music. It has been shown to allow for significant correlations with EEG (Marion, 2021), ECoG (Di Liberto, 2020) and fMRI (Cheung, 2019) recordings of human music listening. The language used for IDyOM -Lisp- is not very familiar to the neuroscience community and makes this model hard to use and more importantly to modify.

The utility of whole-genome sequencing to identify likely transmission pairs for pathogens with slow and variable evolution.

Pathogen whole-genome sequencing (WGS) has been used to track the transmission of infectious diseases in extraordinary detail, especially for pathogens that undergo fast and steady evolution, as is the case with many RNA viruses. However, for other pathogens evolution is less predictable, making interpretation of these data to inform our understanding of their epidemiology more challenging and the value of densely collected pathogen genome data uncertain. Here, we assess the utility of WGS for one such pathogen, in the "who-infected-whom" identification problem.

Animal Behaviour Packs a Punch: From Parasitism to Production, Pollution and Prevention in Grazing Livestock.

Behaviour is often the fundamental driver of disease transmission, where behaviours of individuals can be seen to scale up to epidemiological patterns seen at the population level. Here we focus on animal behaviour, and its role in parasite transmission to track its knock-on consequences for parasitism, production and pollution. Livestock face a nutrition versus parasitism trade-off in grazing environments where faeces creates both a nutritional benefit, fertilizing the surrounding sward, but also a parasite risk from infective nematode larvae contaminating the sward.

Optimal experimental designs for estimating genetic and non-genetic effects underlying infectious disease transmission.

Background: The spread of infectious diseases in populations is controlled by the susceptibility (propensity to acquire infection), infectivity (propensity to transmit infection), and recoverability (propensity to recover/die) of individuals. Estimating genetic risk factors for these three underlying host epidemiological traits can help reduce disease spread through genetic control strategies. Previous studies have identified important 'disease resistance single nucleotide polymorphisms (SNPs)', but how these affect the underlying traits is an unresolved question.

FAIR data pipeline: provenance-driven data management for traceable scientific workflows.

Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging. Data management is further complicated by data being imprecisely identified when used.