Bayesian experimental design is a fast growing area of research with many real-world applications. As computational power has increased over the years, so has the development of simulation-based design methods, which involve a number of algorithms, such as Markov chain Monte Carlo, sequential Monte Carlo and approximate Bayes methods, and which have enabled more complex design problems to be solved. TheBayesian framework provides a unified approach for incorporating prior information and/or uncertainties regarding the statistical model with a utility function which describes the experimental aims. In this paper, we provide a general overview on the concepts involved in Bayesian experimental design, and focus on describing some of the more commonly-used Bayesian utility functions and methods for their estimation, as well as a number of algorithms that are used to search over the design space to find the optimal Bayesian design. We also discuss other computational strategies for further research in Bayesian optimal design.

Compared with some other species, insulin dysregulation in equids is poorly understood. However, hyperinsulinemia causes laminitis, a significant and often lethal disease affecting the pedal bone/hoof wall attachment site. Until recently, hyperinsulinemia has been considered a counterregulatory response to insulin resistance (IR), but there is growing evidence to support a gastrointestinal etiology. Incretin hormones released from the proximal intestine, glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide, augment insulin secretion in several species but require investigation in horses. This study investigated peripheral and gut-derived factors impacting insulin secretion by comparing the response to intravenous (iv) and oral d-glucose. Oral and iv tests were performed in 22 ponies previously shown to be insulin dysregulated, of which only 15 were classified as IR (iv test). In a more detailed study, nine different ponies received four treatments: d-glucose orally, d-glucose iv, oats, and commercial grain mix. Insulin, glucose, and incretin concentrations were measured before and after each treatment. All nine ponies showed similar iv responses, but five were markedly hyperresponsive to oral d-glucose and four were not. Insulin responsiveness to oral d-glucose was strongly associated with blood glucose concentrations and oral glucose bioavailability, presumably driven by glucose absorption/distribution, as there was no difference in glucose clearance rates. Insulin was also positively associated with the active amide of GLP-1 following d-glucose and grain. This study has confirmed a functional enteroinsular axis in ponies that likely contributes to insulin dysregulation that may predispose them to laminitis. Moreover, iv tests for IR are not reliable predictors of the oral response to dietary nonstructural carbohydrate.

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Highlights High frequency water-quality data requires automated anomaly detection (AD)  Rule-based methods detected all missing, out-of-range and impossible values  Regression and feature-based methods detected sudden spikes and level shifts well  High false negative rates were associated with other types of anomalies, e.g. drift  Our transferable framework selects and compares AD methods for end-user needs AbstractMonitoring the water quality of rivers is increasingly conducted using automated in situ sensors, enabling timelier identification of unexpected values or trends. However, the data are confounded by anomalies caused by technical issues, for which the volume and velocity of data preclude manual detection. We present a framework for automated anomaly detection in high-frequency water-quality data from in situ sensors, using turbidity, conductivity and river level data collected from rivers flowing into the Great Barrier Reef. After identifying end-user needs and defining anomalies, we ranked anomaly importance and selected suitable detection methods. High priority anomalies included sudden isolated spikes and level shifts, most of which were classified correctly by regression-based methods such as autoregressive integrated moving average models. However, incorporation of multiple water-quality variables as covariates reduced performance due to complex relationships among variables. Classifications of drift and periods of anomalously low or high variability were more often correct when we applied mitigation, which replaces anomalous measurements with forecasts for further forecasting, but this inflated false positive rates. Feature-based methods also performed well on high priority anomalies and were similarly less proficient at detecting lower priority anomalies, resulting in high false negative rates. Unlike regression-based methods, however, all feature-based methods produced low false positive rates and have the benefit of not requiring training or optimization. Rule-based methods successfully detected a subset of lower priority anomalies, specifically impossible values and missing observations. We therefore suggest that a combination of methods will provide optimal performance in terms of correct anomaly detection, whilst minimizing false detection rates. Furthermore, our framework emphasizes the importance of communication between end-users and anomaly detection developers for optimal outcomes with respect to both detection performance and end-user application. To this end, our framework has high transferability to other types of high frequency time-series data and anomaly detection applications.