Cell-surface nucleolin is sequestered into EPEC microcolonies and may play a role during infection

Nucleolin is a prominent nucleolar protein that is mobilized into the cytoplasm during infection by enteropathogenic Escherichia coli (EPEC). Nucleolin also exists at low levels at the cell surface of eukaryotic cells and here we show that upon infection of an intestinal cell model, EPEC recruits and subsequently sequesters cell-surface EGFP-nucleolin into extracellularly located bacterial microcolonies. The recruitment of nucleolin was evident around bacteria within the centre of the microcolonies that were not directly associated with actin-based pedestals. Incubation of host intestinal cells with different ligands that specifically bind nucleolin impaired the ability of EPEC to disrupt epithelial barrier function but did not inhibit bacterial attachment or other effector-driven processes such as pedestal formation or microvilli effacement. Taken together, this work suggests that EPEC exploits two spatially distinct pools of nucleolin during the infection process

The Enteropathogenic E. coli Effector EspF Targets and Disrupts the Nucleolus by a Process Regulated by Mitochondrial Dysfunction

The nucleolus is a multifunctional structure within the nucleus of eukaryotic cells and is the primary site of ribosome biogenesis. Almost all viruses target and disrupt the nucleolus—a feature exclusive to this pathogen group. Here, using a combination of bio-imaging, genetic and biochemical analyses, we demonstrate that the enteropathogenic E. coli (EPEC) effector protein EspF specifically targets the nucleolus and disrupts a subset of nucleolar factors. Driven by a defined N-terminal nucleolar targeting domain, EspF causes the complete loss from the nucleolus of nucleolin, the most abundant nucleolar protein. We also show that other bacterial species disrupt the nucleolus, dependent on their ability to deliver effector proteins into the host cell. Moreover, we uncover a novel regulatory mechanism whereby nucleolar targeting by EspF is strictly controlled by EPEC's manipulation of host mitochondria. Collectively, this work reveals that the nucleolus may be a common feature of bacterial pathogenesis and demonstrates that a bacterial pathogen has evolved a highly sophisticated mechanism to enable spatio-temporal control over its virulence proteins

Cyclooxygenase-1 and -2 modulate sweating but not cutaneous vasodilation during exercise in the heat in young men

We recently reported that the nonselective cyclooxygenase (COX) inhibitor ketorolac attenuated sweating but not cutaneous vasodilation during moderate‐intensity exercise in the heat. However, the specific contributions of COX‐1 and COX‐2 to the sweating response remained to be determined. We tested the hypothesis that COX‐1 but not COX‐2 contributes to sweating with no role for either COX isoform in cutaneous vasodilation during moderate‐intensity exercise in the heat. In thirteen young males (22 ± 2 years), sweat rate and cutaneous vascular conductance were measured at three forearm skin sites that were continuously treated with (1) lactated Ringer\u27s solution (Control), (2) 150 μmmol·L−1 celecoxib, a selective COX‐2 inhibitor, or (3) 10 mmol L−1 ketorolac, a nonselective COX inhibitor. Participants first rested in a non heat stress condition (≥85 min, 25°C) followed by a further 70‐min rest period in the heat (35°C). They then performed 50 min of moderate‐intensity cycling (~55% peak oxygen uptake) followed by a 30‐min recovery period. At the end of exercise, sweat rate was lower at the 150 μmol·L−1 celecoxib (1.51 ± 0.25 mg·min−1·cm−2) and 10 mmol·L−1 ketorolac (1.30 ± 0.30 mg·min−1·cm−2) treated skin sites relative to the Control site (1.89 ± 0.27 mg·min−1·cm−2) (both P ≤ 0.05). Additionally, sweat rate at the ketorolac site was attenuated relative to the celecoxib site (P ≤ 0.05). Neither celecoxib nor ketorolac influenced cutaneous vascular conductance throughout the experiment (both P > 0.05). We showed that both COX‐1 and COX‐2 contribute to sweating but not cutaneous vasodilation during moderate‐intensity exercise in the heat in young men

Working Group on Unification Referendums on the Island of Ireland: Interim Report

The Belfast/Good Friday Agreement of 1998 provides for the possibility of future referendums on the question of whether Northern Ireland should remain in the United Kingdom or become part of a united Ireland. It sets out some of the principles that such votes would need to follow, but it leaves many aspects of the process unclear or unspecified. How would the Secretary of State for Northern Ireland decide whether to call a referendum? Would a vote also be needed in the Republic of Ireland? Would referendums north and south need to be simultaneous? Would they best take place before or after detailed proposals for the form of a united Ireland had been worked out? Who should be able to vote? What should the question on the ballot paper be? How would the referendum campaigns be conducted? This interim report explores possible answers to these and other questions, and sets out the provisional conclusions of the Working Group on Unification Referendums on the Island of Ireland. The Working Group has no collective view on whether such referendums should take place or what the outcome should be if a vote is called. The Group does not see referendums on this subject as imminent. But they could happen in the future. And thinking through in advance what that would involve is vitally important

DeepWeeds: a multiclass weed species image dataset for deep learning

Robotic weed control has seen increased research of late with its potential for boosting productivity in agriculture. Majority of works focus on developing robotics for croplands, ignoring the weed management problems facing rangeland stock farmers. perhaps the greatest obstacle to widespread uptake of robotic weed control is the robust classification of weed species in their natural environment. the unparalleled successes of deep learning make it an ideal candidate for recognising various weed species in the complex rangeland environment. This work contributes the first large, public, multiclass image dataset of weed species from the Australian rangelands; allowing for the development of robust classification methods to make robotic weed control viable. The DeepWeeds dataset consists of 17,509 labelled images of eight nationally significant weed species native to eight locations across northern Australia. This paper presents a baseline for classification performance on the dataset using the benchmark deep learning models, Inception-v3 and ResNet-50. These models achieved an average classification accuracy of 95.1% and 95.7%, respectively. We also demonstrate real time performance of the ResNet-50 architecture, with an average inference time of 53.4 ms per image. These strong results bode well for future field implementation of robotic weed control methods in the Australian rangelands