Estimating the extent of Antarctic summer sea ice during the Heroic Age of Antarctic Exploration

In stark contrast to the sharp decline in Arctic sea ice, there has been a steady increase in ice extent around Antarctica during the last three decades, especially in the Weddell and Ross seas. In general, climate models do not to capture this trend and a lack of information about sea ice coverage in the pre-satellite period limits our ability to quantify the sensitivity of sea ice to climate change and robustly validate climate models. However, evidence of the presence and nature of sea ice was often recorded during early Antarctic exploration, though these sources have not previously been explored or exploited until now. We have analysed observations of the summer sea ice edge from the ship logbooks of explorers such as Robert Falcon Scott, Ernest Shackleton and their contemporaries during the Heroic Age of Antarctic Exploration (1897–1917), and in this study we compare these to satellite observations from the period 1989–2014, offering insight into the ice conditions of this period, from direct observations, for the first time. This comparison shows that the summer sea ice edge was between 1.0 and 1.7° further north in the Weddell Sea during this period but that ice conditions were surprisingly comparable to the present day in other sectors

Causality indices for bivariate time series data: a comparative review of performance

Inferring nonlinear and asymmetric causal relationships between multivariate longitudinal data is a challenging task with wide-ranging application areas including clinical medicine, mathematical biology, economics and environmental research. A number of methods for inferring causal relationships within complex dynamic and stochastic systems have been proposed but there is not a unified consistent definition of causality in the context of time series data. We evaluate the performance of ten prominent causality indices for bivariate time series, across four simulated model systems that have different coupling schemes and characteristics. Pairwise correlations between different methods, averaged across all simulations, show there is generally strong agreement between methods, with minimum, median and maximumPearson correlations between any pair (excluding two similarity indices) of 0.298, 0.719 and 0.955 respectively. In further experiments, we show that these methods are not always be invariant to real-world relevant transformations(data availability, standardisation and scaling, rounding error, missing data and noisy data). We recommend transfer entropy and nonlinear Granger causality as particularly strong approaches for estimating bivariate causal relationships inreal-world applications. Both successfully identify causal relationships and a lack thereof across multiple simulations, whilst remaining robust to rounding error, at least 20% missing data and small variance Gaussian noise. Finally, we provide flexible open-access Python code for computation of these methods and for the model simulations.TE: Engineering and Physical Sciences Research Council (EPSRC) National Productivity Investment Fund (NPIF) EP/S515334/1, reference 2089662 and Cantab Capital Institute for Mathematics of Information (CCIMI

Our friend in the north: the origins, evolution and appeal of the cult of St Duthac of Tain in later Middle Ages

St Duthac of Tain was one of the most popular Scottish saints of the later middle ages. From the late fourteenth century until the reformation devotion to Duthac outstripped that of Andrew, Columba, Margaret and Mungo, and Duthac's shrine in Easter Ross became a regular haunt of James IV (1488-1513) and James V (1513-42). Hitherto historians have tacitly accepted the view of David McRoberts that Duthac was one of several local saints whose emergence and popularity in the fifteenth century was part of a wider self-consciously nationalist trend in Scottish religious practice. This study looks beyond the paradigm of nationalism to trace and explain the popularity of St Duthac from the shadowy origins of the cult to its heyday in the early sixteenth century

Broadly engaging with tranquillity in protected landscapes:A matter of perspective identified in GIS

References to the subjective notion of tranquillity have long been extensively deployed in marketing\ud literature and in planning policy in relation to both its promotion and its protection, particularly in protected\ud areas. Whilst a liberal use of the term has ensued, a plethora of research interprets tranquillity\ud primarily with noise, and where broader interpretations are progressed, traditional, directional questioning\ud techniques are evident in attempts to understand tranquillity and quantify its features. Surprisingly,\ud few enquiries have taken a broader, inductive approach to determining the range of stakeholders’ views\ud and of these even fewer have engaged specifically with local residents and particularly those classed as\ud hard-to-reach. Using these latter approaches, of the few and most recent studies conducted, the Broadly\ud Engaging with Tranquillity project provides a replicable framework for determining and mapping tranquillity.\ud An extensive community engagement process launched the study, using participatory principles\ud from which stakeholders’ views were modelled using Geographical Information Systems. Results of this\ud research are reported together with an interpretation of the models created according to four distinct\ud groups representing views of institutions and members of the public. Similar views are identified amongst\ud the groups with tranquillity commonly related to natural environments, whereas nontranquillity was\ud primarily equated to seeing and hearing people and the products of human activity. Yet distinctions are\ud identified between the four groups that have important implications for who should be involved in determining\ud local characteristics of tranquillity and for how protected area managers might include nonexpert\ud views in their understanding and conservation of tranquillity

Information and generative deep learning with applications to medical time-series

Physiological time-series data are a valuable but under-utilised resource in intensive care medicine. These data are highly-structured and contain a wealth of information about the patient state, but can be very high-dimensional and difficult to interpret. Understanding temporal relationships between time-series variables is crucial for many important tasks, in particular identifying patient phenotypes within large heterogeneous cohorts, and predicting and explaining physiological changes to a patient over time. There are wide- ranging complexities involved in learning such insights from longitudinal data, including a lack of a universal accepted framework for understanding causal influence in time-series, issues with poor quality data segments that bias downstream tasks, and important privacy concerns around releasing sensitive personal data. These challenges are by no means unique to this clinical application, and there are significant domain-agnostic elements within this thesis that have a broad scope to any research area that is centred around time-series monitoring (e.g. climate science, mathematical finance, signal processing). In the first half of this thesis, I focused firstly on information and causal influence in time- series data and then on flexible time-series modelling and hierarchical model comparison using Bayesian methods. To aid these tasks, I reviewed and developed new statistical methodology, particularly using integrated likelihoods for model evidence estimation. Together, this provided a framework for evaluating trajectories of the information contained within and between physiological variables, and allowed a comparison between patient cohorts that showed evidence of impaired physiological regulation in Covid-19 patients. The second half of this thesis introduced generative deep learning models as a tool to address some of the key difficulties in clinical time-series data, including artefact detection, imputation and synthetic dataset generation. The latter is especially important in the future of critical care research, because of the inherent challenges in publishing clinical datasets. However, I showed that that there are many obstacles that must be addressed before large-scale synthetic datasets can be utilised fully, including preserving complex relationships between physiological time-series variables within the synthetic data.Engineering and Physical Sciences Research Council (EPSRC) National Productivity Investment Fund (NPIF) EP/S515334/1, reference 208966

Estimating the extent of Antarctic summer sea ice during the Heroic Age of Exploration

In stark contrast to the sharp decline in Arctic sea ice, there has been a steady increase in ice extent around Antarctica during the last three decades, especially in the Weddell and Ross seas. In general, climate models do not to capture this trend and a lack of information about sea ice coverage in the pre-satellite period limits our ability to quantify the sensitivity of sea ice to climate change and robustly validate climate models. However, evidence of the presence and nature of sea ice was often recorded during early Antarctic exploration, though these sources have not previously been explored or exploited until now. We have analysed observations of the summer sea ice edge from the ship logbooks of explorers such as Robert Falcon Scott, Ernest Shackleton and their contemporaries during the Heroic Age of Antarctic Exploration (1897–1917), and in this study we compare these to satellite observations from the period 1989–2014, offering insight into the ice conditions of this period, from direct observations, for the first time. This comparison shows that the summer sea ice edge was between 1.0 and 1.7° further north in the Weddell Sea during this period but that ice conditions were surprisingly comparable to the present day in other sectors

Bayesian model selection for multilevel models using integrated likelihoods.

Multilevel linear models allow flexible statistical modelling of complex data with different levels of stratification. Identifying the most appropriate model from the large set of possible candidates is a challenging problem. In the Bayesian setting, the standard approach is a comparison of models using the model evidence or the Bayes factor. Explicit expressions for these quantities are available for the simplest linear models with unrealistic priors, but in most cases, direct computation is impossible. In practice, Markov Chain Monte Carlo approaches are widely used, such as sequential Monte Carlo, but it is not always clear how well such techniques perform. We present a method for estimation of the log model evidence, by an intermediate marginalisation over non-variance parameters. This reduces the dimensionality of any Monte Carlo sampling algorithm, which in turn yields more consistent estimates. The aim of this paper is to show how this framework fits together and works in practice, particularly on data with hierarchical structure. We illustrate this method on simulated multilevel data and on a popular dataset containing levels of radon in homes in the US state of Minnesota

Application of the Sepsis-3 criteria to describe sepsis epidemiology in the Amsterdam UMCdb intensive care dataset.

INTRODUCTION: Sepsis is a major cause of morbidity and mortality worldwide. In the updated, 2016 Sepsis-3 criteria, sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, where organ dysfunction can be represented by an increase in the Sequential Organ Failure Assessment (SOFA) score of 2 points or more. We sought to apply the Sepsis-3 criteria to characterise the septic cohort in the Amsterdam University Medical Centres database (Amsterdam UMCdb). METHODS: We examined adult intensive care unit (ICU) admissions in the Amsterdam UMCdb, which contains de-identified data for patients admitted to a mixed surgical-medical ICU at a tertiary academic medical centre in the Netherlands. We operationalised the Sepsis-3 criteria, defining organ dysfunction as an increase in the SOFA score of 2 points or more, while infection was defined as a new course of antibiotics or an escalation in antibiotic therapy, with at least one antibiotic given intravenously. Patients with sepsis were determined to be in septic shock if they additionally required the use of vasopressors and had a lactate level >2 mmol/L. RESULTS: We identified 18,221 ICU admissions from 16,408 patients in our cohort. There were 6,312 unique sepsis episodes, of which 30.2% met the criteria for septic shock. A total of 4,911/6,312 sepsis (77.8%) episodes occurred on ICU admission. Forty-seven percent of emergency medical admissions and 36.7% of emergency surgical admissions were for sepsis. Overall, there was a 12.5% ICU mortality rate; patients with septic shock had a higher ICU mortality rate (38.4%) than those without shock (11.4%). CONCLUSIONS: We successfully operationalised the Sepsis-3 criteria to the Amsterdam UMCdb, allowing the characterization and comparison of sepsis epidemiology across different centres