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Although hyperpolarization‐activated cation (HCN) ion channels are well established to underlie cardiac pacemaker activity, their role in smooth muscle organs remains controversial. HCN‐expressing cells are localized to renal pelvic smooth muscle (RPSM) pacemaker tissues of the murine upper urinary tract and HCN channel conductance is required for peristalsis. To date, however, the Ih pacemaker current conducted by HCN channels has never been detected in these cells, raising questions on the identity of RPSM pacemakers. Indeed, the RPSM pacemaker mechanisms of the unique multicalyceal upper urinary tract exhibited by humans remains unknown. Here, we developed immunopanning purification protocols and demonstrate that 96% of isolated HCN+ cells exhibit Ih. Single‐molecule STORM to whole‐tissue imaging showed HCN+ cells express single HCN channels on their plasma membrane and integrate into the muscular syncytium. By contrast, PDGFR‐α+ cells exhibiting the morphology of ICC gut pacemakers were shown to be vascular mural cells. Translational studies in the homologous human and porcine multicalyceal upper urinary tracts showed that contractions and pacemaker depolarizations originate in proximal calyceal RPSM. Critically, HCN+ cells were shown to integrate into calyceal RPSM pacemaker tissues, and HCN channel block abolished electrical pacemaker activity and peristalsis of the multicalyceal upper urinary tract. Cumulatively, these studies demonstrate that HCN ion channels play a broad, evolutionarily conserved pacemaker role in both cardiac and smooth muscle organs and have implications for channelopathies as putative aetiologies of smooth muscle disorders. Key points: Pacemakers trigger contractions of involuntary muscles. Hyperpolarization‐activated cation (HCN) ion channels underpin cardiac pacemaker activity, but their role in smooth muscle organs remains controversial.Renal pelvic smooth muscle (RPSM) pacemakers trigger contractions that propel waste away from the kidney. HCN+ cells localize to murine RPSM pacemaker tissue and HCN channel conductance is required for peristalsis.The HCN (Ih) current has never been detected in RPSM cells, raising doubt whether HCN+ cells are bona fide pacemakers. Moreover, the pacemaker mechanisms of the unique multicalyceal RPSM of higher order mammals remains unknown.In total, 97% of purified HCN+ RPSM cells exhibit Ih. HCN+ cells integrate into the RPSM musculature, and pacemaker tissue peristalsis is dependent on HCN channels. Translational studies in human and swine demonstrate HCN channels are conserved in the multicalyceal RPSM and that HCN channels underlie pacemaker activity that drives peristalsis.These studies provide insight into putative channelopathies that can underlie smooth muscle dysfunction. [ABSTRACT FROM AUTHOR]

Introduction: Leptospirosis is a zoonotic disease responsible for over 1 million severe cases and 60,000 deaths annually. The wide range of animal hosts and complex environmental drivers of transmission make targeted interventions challenging, particularly when restricted to regression-based analyses which have limited ability to deal with complexity. In Fiji, important environmental and socio-demographic factors include living in rural areas, poverty, and livestock exposure. This study aims to examine drivers of transmission under different scenarios of environmental and livestock exposures. Methods: Spatial Bayesian networks (SBN) were used to analyse the influence of livestock and poverty on the risk of leptospirosis infection in urban compared to rural areas. The SBN models used a combination of spatially-explicit field data from previous work and publically available census information. Predictive risk maps were produced for overall risk, and for scenarios related to poverty, livestock, and urban/rural setting. Results: While high, rather than low, commercial dairy farm density similarly increased the risk of infection in both urban (12% to 18%) and rural areas (70% to 79%), the presence of pigs in a village had different impact in rural (43% to 84%) compared with urban areas (4% to 24%). Areas with high poverty rates were predicted to have 26.6% and 18.0% higher probability of above average seroprevalence in rural and urban areas, respectively. In urban areas, this represents >300% difference between areas of low and high poverty, compared to 43% difference in rural areas. Conclusions: Our study demonstrates the use of SBN to provide valuable insights into the drivers of leptospirosis transmission under complex scenarios. By estimating the risk of leptospirosis infection under different scenarios, such as urban versus rural areas, these subgroups or areas can be targeted with more precise interventions that focus on the most relevant key drivers of infection. [ABSTRACT FROM AUTHOR]

Invasive wildlife often causes serious damage to the economy and agriculture as well as environmental, human and animal health. Habitat models can fill knowledge gaps about species distributions and assist planning to mitigate impacts. Yet, model accuracy and utility may be compromised by small study areas and limited integration of species ecology or temporal variability. Here we modelled seasonal habitat suitability for wild pigs, a widespread and harmful invader, in northern Australia. We developed a resource-based, spatially-explicit and regional-scale approach using Bayesian networks and spatial pattern suitability analysis. We integrated important ecological factors such as variability in environmental conditions, breeding requirements and home range movements. The habitat model was parameterized during a structured, iterative expert elicitation process and applied to a wet season and a dry season scenario. Model performance and uncertainty was evaluated against independent distributional data sets. Validation results showed that an expert-averaged model accurately predicted empirical wild pig presences in northern Australia for both seasonal scenarios. Model uncertainty was largely associated with different expert assumptions about wild pigs’ resource-seeking home range movements. Habitat suitability varied considerably between seasons, retracting to resource-abundant rainforest, wetland and agricultural refuge areas during the dry season and expanding widely into surrounding grassland floodplains, savanna woodlands and coastal shrubs during the wet season. Overall, our model suggested that suitable wild pig habitat is less widely available in northern Australia than previously thought. Mapped results may be used to quantify impacts, assess risks, justify management investments and target control activities. Our methods are applicable to other wide-ranging species, especially in data-poor situations. [ABSTRACT FROM AUTHOR]

Globally, infectious diseases are responsible for a significant burden on human health. Drivers of disease transmission depend on interactions between humans, the environment, vectors, carriers, and pathogens; transmission dynamics are therefore potentially highly complex. Research in infectious disease eco-epidemiology has been rapidly gaining momentum because of the rising global importance of disease emergence and outbreaks, and growing understanding of the intimate links between human health and the environment. The scientific community is increasingly recognising the need for multidisciplinary translational research, integrated approaches, and innovative methods and tools to optimise risk prediction and control measures. Environmental health experts have also identified the need for more advanced analytical and biostatistical approaches to better determine causality, and deal with unknowns and uncertainties inherent in complex systems. In this paper, we discuss the use of Bayesian networks in infectious disease eco-epidemiology, and the potential for developing dynamic tools for public health decision-making and improving intervention strategies. [ABSTRACT FROM AUTHOR]

In this study, key ecological modelling limitations of a process-based simulation model and a Bayesian network were reduced by combining the two approaches. We demonstrate the combined modelling approach with a case study investigating increases in woody vegetation density in northern Australia's tropical savannas. We found that by utilising the strengths of a simulation model and a Bayesian network we could both forecast future change in woody vegetation density and diagnose the reasons for current vegetation states. The local conditions of climate, soil characteristics and the starting population of trees were found to be more important in explaining the likelihood of change in woody vegetation density compared to management practices such as grazing pressure and fire regimes. We conclude that combining the strengths of a process and BN model allowed us to produce a simple model that utilised the ability of the process model to simulate ecosystem processes in detail and over long time periods, and the ability of the BN to capture uncertainty in ecosystem response and to conduct scenario, sensitivity and diagnostic analysis. The overall result was a model that has the potential to provide land managers with a better understanding of the behaviour of a complex ecosystem than simply utilising either modelling approach in isolation. [Copyright &y& Elsevier]

Wildlife managers are often required to make important conservation and recovery decisions despite incomplete and uncertain knowledge of the species and ecosystems they manage. Conducting further research to collect more empirical data may reduce that uncertainty. However, a sense of urgency often surrounds threatened or endangered species’ management and decisions cannot wait until a definitive understanding of a species’ ecology and distribution is obtained. Bayesian belief networks (BBNs) are proving to be valuable and flexible tools for integrating available expert knowledge and empirical data, thus strengthening conservation decisions when empirical data is lacking. We developed a BBN model and linked it to a geographical information system (GIS) to map habitat suitability for the Julia Creek dunnart (Sminthopsis douglasi), an endangered ground-dwelling mammal of the Mitchell grasslands of north-west Queensland, Australia. Expert knowledge, supported by field data, was used to determine the probabilistic influence of grazing pressure, density of the invasive shrub prickly acacia (Acacia nilotica), land tenure, soil variability and seasonal variability on dunnart habitat suitability. The model was then applied in a GIS to map the likelihood of suitable dunnart habitat. Sensitivity analysis was performed to identify the influence of environmental conditions and management options on habitat suitability. The study provides an example of how expert knowledge and limited empirical data can be combined within a BBN model, and linked to GIS data, to assist in recovery planning of endangered fauna populations. [Copyright &y& Elsevier]

Regression models are the standard approaches used in infectious disease epidemiology, but have limited ability to represent causality or complexity. We explore Bayesian networks (BNs) as an alternative approach for modelling infectious disease transmission, using leptospirosis as an example. Data were obtained from a leptospirosis study in Fiji in 2013. We compared the performance of naïve versus expert-structured BNs for modelling the relative importance of animal species in disease transmission in different ethnic groups and residential settings. For BNs of animal exposures at the individual/household level, R 2 for predicted versus observed infection rates were 0.59 for naïve and 0.75–0.93 for structured models of ethnic groups; and 0.54 for naïve and 0.93–1.00 for structured models of residential settings. BNs provide a promising approach for modelling infectious disease transmission under complex scenarios. The relative importance of animal species varied between subgroups, with important implications for more targeted public health control strategies. [ABSTRACT FROM AUTHOR]