Everything but the kitchen sink: The use of multiple hypothesis generation methods to investigate an outbreak of Enteritidis associated with frozen profiteroles and eclairs.

In December 2018, an outbreak of Enteritidis infections was identified in Canada by whole-genome sequencing (WGS). An investigation was initiated to identify the source of the illnesses, which proved challenging and complex. Microbiological hypothesis generation methods included comparisons of isolate sequence data to historical domestic outbreaks and international repositories.

A multi-provincial outbreak of Newport infections associated with red onions: A report of the largest outbreak in Canada in over 20 years.

An investigation into an outbreak of Newport infections in Canada was initiated in July 2020. Cases were identified across several provinces through whole-genome sequencing (WGS). Exposure data were gathered through case interviews.

DLSIA: Deep Learning for Scientific Image Analysis.

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales.