Post-translational targeting of Rab35 by the effector IcsB of Shigella determines intracellular bacterial niche formation.

Escape from the bacterial-containing vacuole (BCV) is a key step of Shigella host cell invasion. Rab GTPases subverted to in situ-formed macropinosomes in the vicinity of the BCV have been shown to promote its rupture. The involvement of the BCV itself has remained unclear.

Microtubules provide force to promote membrane uncoating in vacuolar escape for a cyto-invasive bacterial pathogen.

Intracellular bacterial pathogens gain entry to mammalian cells inside a vacuole derived from the host membrane. Some of them escape the bacteria-containing vacuole (BCV) and colonize the cytosol. Bacteria replicating within BCVs coopt the microtubule network to position it within infected cells, whereas the role of microtubules for cyto-invasive pathogens remains obscure.

Shigella generates distinct IAM subpopulations during epithelial cell invasion to promote efficient intracellular niche formation.

The facultative intracellular pathogen Shigella flexneri invades non-phagocytic epithelial gut cells. Through a syringe-like apparatus called type 3 secretion system, it injects effector proteins into the host cell triggering actin rearrangements leading to its uptake within a tight vacuole, termed the bacterial-containing vacuole (BCV). Simultaneously, Shigella induces the formation of large vesicles around the entry site, which we refer to as infection-associated macropinosomes (IAMs).

An automated microscopy workflow to study Shigella-neutrophil interactions and antibiotic efficacy in vivo.

Shigella are Gram-negative bacterial pathogens responsible for bacillary dysentery (also called shigellosis). The absence of a licensed vaccine and widespread emergence of antibiotic resistance has led the World Health Organisation (WHO) to highlight Shigella as a priority pathogen requiring urgent attention. Several infection models have been useful to explore the Shigella infection process; yet, we still lack information regarding events taking place in vivo.

Explainable artificial intelligence for assault sentence prediction in New Zealand.

The judiciary has historically been conservative in its use of Artificial Intelligence, but recent advances in machine learning have prompted scholars to reconsider such use in tasks like sentence prediction. This paper investigates by experimentation the potential use of explainable artificial intelligence for predicting imprisonment sentences in assault cases in New Zealand's courts. We propose a proof-of-concept explainable model and verify in practice that it is fit for purpose, with predicted sentences accurate to within one year.