Can the legacy of industrial pollution influence antimicrobial resistance in estuarine sediments?

Antimicrobial resistance (AMR) represents a major global health threat, as well as a major hazard to sustainable economic development and national security. It remains, therefore, vital that current research aligns to policy development and implementation to alleviate a potential crisis. One must consider, for example, whether drivers of antibiotic resistance can be controlled in the future, or have they already accumulated in the past? Whether from antibiotics and/or other pollutants. Unfortunately, industrial heritage and its pollution impact on the prevalence of environmental AMR have largely been ignored. Focussing on industrialised estuaries we demonstrate that anthropogenic pollution inputs in addition to the natural diurnal environmental conditions can sufficiently create stressful conditions to the microbiome, and thus promote selective pressures to shift the resistome (i.e., collection of resistance traits in the microbiological community). Unfortunately, the bacteria’s survival mechanisms, via co-selective pressures, can affect their susceptibility to antibiotics. This review highlights the complexity of estuarine environments, using two key contaminant groups (metals/toxic elements and polyaromatic hydrocarbons), through which a variety of possible chemical and biological pollutant stressors can promote the emergence and dissemination of antimicrobial resistance. We find compelling divers to call on more focused research on historically disrupted ecosystems, in propagating AMR in the real world

A framework for understanding mental imagery in design cognition research

Mental imagery is the experience of perceiving an object within one's own mind and is a subjective experience, leading to difficulties in the research and understanding of the phenomenon. This paper documents the development and verification of a framework for researching the elements of mental imagery. The framework was developed following a review of both psychology and design literature which signified three fundamental conceptual viewpoints of mental imagery: imagery modalities, dimensions of imagery ability, and imagery processes. The aim of this framework is to allow for structured research on mental imagery in any given research field. This is verified through discussion for the product design engineering discipline and provides a base for future work on this topic. The conclusions made in this paper reveal that mental imagery, and particularly visual mental imagery, is largely considered to be integral in design overlooking the different realities of designers and confirming a greater need to understand mental imagery experiences in product design engineering

A Recipe for Kai-dness

This paper provides a recipe for Kai-dness, as designed by staff and students from Te Herenga Waka, Victoria University of Wellington’s Faculty of Architecture and Design Innovation. It sets out both the ingredients and the method for Kai-dness. It celebrates the associated lessons learned and the agents of change who combined as ‘the Kai-dness Crew’ to facilitate conversation and collaboration through commensality, the sharing of kai. The plate this was served upon was the fifth Social Movements, Resistance, and Social Change Conference in November 2020. This paper suggests that there is power in the sharing of kai to aid the social transitions required in moving towards more positive futures

Antibiotic Therapy and the Gut Microbiome:Investigating the Effect of Delivery Route on Gut Pathogens

The contribution of the gut microbiome to human health has long been established, with normal gut microbiota conferring protection against invasive pathogens. Antibiotics can disrupt the microbial balance of the gut, resulting in disease and the development of antimicrobial resistance. The effect of antibiotic administration route on gut dysbiosis remains under-studied to date, with conflicting evidence on the differential effects of oral and parenteral delivery. We have profiled the rat gut microbiome following treatment with commonly prescribed antibiotics (amoxicillin and levofloxacin), via either oral or intravenous administration. Fecal pellets were collected over a 13-day period and bacterial populations were analyzed by 16S rRNA gene sequencing. Significant dysbiosis was observed in all treatment groups, regardless of administration route. More profound dysbiotic effects were observed following amoxicillin treatment than those with levofloxacin, with population richness and diversity significantly reduced, regardless of delivery route. The effect on specific taxonomic groups was assessed, revealing significant disruption following treatment with both antibiotics. Enrichment of a number of groups containing known gut pathogens was observed, in particular, with amoxicillin, such as the family Enterobacteriaceae. Depletion of other commensal groups was also observed. The degree of dysbiosis was significantly reduced toward the end of the sampling period, as bacterial populations began to return to pretreatment composition. Richness and diversity levels appeared to return to pretreatment levels more quickly in intravenous groups, suggesting convenient parenteral delivery systems may have a role to play in reducing longer term gut dysbiosis in the treatment of infection

Atmospheric Drivers of Wind Turbine Blade Leading Edge Erosion: Review and Recommendations for Future Research

Leading edge erosion (LEE) of wind turbine blades causes decreased aerodynamic performance leading to lower power production and revenue and increased operations and maintenance costs. LEE is caused primarily by materials stresses when hydrometeors (rain and hail) impact on rotating blades. The kinetic energy transferred by these impacts is a function of the precipitation intensity, droplet size distributions (DSD), hydrometeor phase and the wind turbine rotational speed which in turn depends on the wind speed at hub-height. Hence, there is a need to better understand the hydrometeor properties and the joint probability distributions of precipitation and wind speeds at prospective and operating wind farms in order to quantify the potential for LEE and the financial efficacy of LEE mitigation measures. However, there are relatively few observational datasets of hydrometeor DSD available for such locations. Here, we analyze six observational datasets from spatially dispersed locations and compare them with existing literature and assumed DSD used in laboratory experiments of material fatigue. We show that the so-called Best DSD being recommended for use in whirling arm experiments does not represent the observational data. Neither does the Marshall Palmer approximation. We also use these data to derive and compare joint probability distributions of drivers of LEE; precipitation intensity (and phase) and wind speed. We further review and summarize observational metrologies for hydrometeor DSD, provide information regarding measurement uncertainty in the parameters of critical importance to kinetic energy transfer and closure of data sets from different instruments. A series of recommendations are made about research needed to evolve towards the required fidelity for a priori estimates of LEE potential.publishedVersio

An assessment of practitioners approaches to forecasting in the presence of changepoints

A common challenge in time series is to forecast data that suffer from structural breaks or changepoints which complicate modeling. If we naively forecast using one model for the whole data, the model will be incorrect, and thus, our forecast error will be large. There are two common practices to account for these changepoints when the goal is forecasting: (1) preprocess the data to identify the changepoints, incorporating them as dummy variables in modeling the whole data, and (2) include the changepoint estimation into the model and forecast using the model fit to the last segment. This article examines these two practices, using the computationally exact Pruned Exact Linear Time (PELT) algorithm for changepoint detection, comparing and contrasting them in the context of an important Software Engineering application

Building confidence in projections of the responses of living marine resources to climate change

The Fifth Assessment Report of the Intergovernmental Panel on Climate Change highlights that climate change and ocean acidification are challenging the sustainable management of living marine resources (LMRs). Formal and systematic treatment of uncertainty in existing LMR projections, however, is lacking. We synthesize knowledge of how to address different sources of uncertainty by drawing from climate model intercomparison efforts. We suggest an ensemble of available models and projections, informed by observations, as a starting point to quantify uncertainties. Such an ensemble must be paired with analysis of the dominant uncertainties over different spatial scales, time horizons, and metrics. We use two examples: (i) global and regional projections of Sea Surface Temperature and (ii) projection of changes in potential catch of sablefish (Anoplopoma fimbria) in the 21st century, to illustrate this ensemble model approach to explore different types of uncertainties. Further effort should prioritize understanding dominant, undersampled dimensions of uncertainty, as well as the strategic collection of observations to quantify, and ultimately reduce, uncertainties. Our proposed framework will improve our understanding of future changes in LMR and the resulting risk of impacts to ecosystems and the societies under changing ocean conditions

Co-selection of antibiotic resistance in Gram-negative bacteria caused by pollution legacy in the Clyde estuary

Antimicrobial resistant bacteria can become harboured in sediments of postindustrial estuaries. Subsequently, their resistance traits could be enriched by pollutants deposited in the sediments. Recent evidence strongly suggests this may pose hazards that not only affects the health care sector, but could also impact tourism and the aquaculture industries. The River Clyde, UK was chosen for this study due to its extensive industrial history, and three sites were chosen to sample from representing different levels and types of industrial activities—two highly polluted and one relatively “pristine” site

The legacy of industrial pollution in estuarine sediments : spatial and temporal variability implications for ecosystem stress

The direct impacts of anthropogenic pollution are widely known public and environmental health concerns, and details on the indirect impact of these are starting to emerge, for example affecting the environmental microbiome. Anthropogenic activities throughout history with associated pollution burdens are notable contributors. Focusing on the historically heavily industrialised River Clyde, Scotland, we investigate spatial and temporal contributions to stressful/hostile environments using a geochemical framework, e.g. pH, EC, total organic carbon and potentially toxic elements: As, Co, Cr, Cu, Ni, Pb and Zn and enrichment indicators. With regular breaches of the sediment quality standards in the estuarine system we focused on PTE correlations instead. Multivariate statistical analysis (principle component analysis) identifies two dominant components, PC1: As, Cr, Cu, Pb and Zn, as well as PC2: Ni, Co and total organic carbon. Our assessment confirms hot spots in the Clyde Estuary indicative of localised inputs. In addition, there are sites with high variability indicative of excessive mixing. We demonstrate that industrialised areas are dynamic environmental sites dependant on historical anthropogenic activity with short-scale variation. This work supports the development of ‘contamination’ mapping to enable an assessment of the impact of historical anthropogenic pollution, identifying specific ‘stressors’ that can impact the microbiome, neglecting in estuarine recovery dynamics and potentially supporting the emergence of antimicrobial resistance in the environment