West Antarctic Ice Sheet retreat driven by Holocene warm water incursions.

Glaciological and oceanographic observations coupled with numerical models show that warm Circumpolar Deep Water (CDW) incursions onto the West Antarctic continental shelf cause melting of the undersides of floating ice shelves. Because these ice shelves buttress glaciers feeding into them, their ocean-induced thinning is driving Antarctic ice-sheet retreat today. Here we present a multi-proxy data based reconstruction of variability in CDW inflow to the Amundsen Sea sector, the most vulnerable part of the West Antarctic Ice Sheet, during the Holocene epoch (from 11.7 thousand years ago to the present). The chemical compositions of foraminifer shells and benthic foraminifer assemblages in marine sediments indicate that enhanced CDW upwelling, controlled by the latitudinal position of the Southern Hemisphere westerly winds, forced deglaciation of this sector from at least 10,400 years ago until 7,500 years ago-when an ice-shelf collapse may have caused rapid ice-sheet thinning further upstream-and since the 1940s. These results increase confidence in the predictive capability of current ice-sheet models.

On Sequential Bayesian Inference for Continual Learning.

Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks. We revisit sequential Bayesian inference and assess whether using the previous task's posterior as a prior for a new task can prevent catastrophic forgetting in Bayesian neural networks. Our first contribution is to perform sequential Bayesian inference using Hamiltonian Monte Carlo. We propagate the posterior as a prior for new tasks by approximating the posterior via fitting a density estimator on Hamiltonian Monte Carlo samples. We find that this approach fails to prevent catastrophic forgetting, demonstrating the difficulty in performing sequential Bayesian inference in neural networks. From there, we study simple analytical examples of sequential Bayesian inference and CL and highlight the issue of model misspecification, which can lead to sub-optimal continual learning performance despite exact inference. Furthermore, we discuss how task data imbalances can cause forgetting. From these limitations, we argue that we need probabilistic models of the continual learning generative process rather than relying on sequential Bayesian inference over Bayesian neural network weights. Our final contribution is to propose a simple baseline called Prototypical Bayesian Continual Learning, which is competitive with the best performing Bayesian continual learning methods on class incremental continual learning computer vision benchmarks.

Functional and informatics analysis enables glycosyltransferase activity prediction.

The elucidation and prediction of how changes in a protein result in altered activities and selectivities remain a major challenge in chemistry. Two hurdles have prevented accurate family-wide models: obtaining (i) diverse datasets and (ii) suitable parameter frameworks that encapsulate activities in large sets. Here, we show that a relatively small but broad activity dataset is sufficient to train algorithms for functional prediction over the entire glycosyltransferase superfamily 1 (GT1) of the plant Arabidopsis thaliana. Whereas sequence analysis alone failed for GT1 substrate utilization patterns, our chemical-bioinformatic model, GT-Predict, succeeded by coupling physicochemical features with isozyme-recognition patterns over the family. GT-Predict identified GT1 biocatalysts for novel substrates and enabled functional annotation of uncharacterized GT1s. Finally, analyses of GT-Predict decision pathways revealed structural modulators of substrate recognition, thus providing information on mechanisms. This multifaceted approach to enzyme prediction may guide the streamlined utilization (and design) of biocatalysts and the discovery of other family-wide protein functions.

Salt-Enrichment Impact on Biomass Production in a Natural Population of Peatland Dwelling Arcellinida and Euglyphida (Testate Amoebae).

Unicellular free-living microbial eukaryotes of the order Arcellinida (Tubulinea; Amoebozoa) and Euglyphida (Cercozoa; SAR), commonly termed testate amoebae, colonise almost every freshwater ecosystem on Earth. Patterns in the distribution and productivity of these organisms are strongly linked to abiotic conditions-particularly moisture availability and temperature-however, the ecological impacts of changes in salinity remain poorly documented. Here, we examine how variable salt concentrations affect a natural community of Arcellinida and Euglyphida on a freshwater sub-Antarctic peatland. We principally report that deposition of wind-blown oceanic salt-spray aerosols onto the peatland surface corresponds to a strong reduction in biomass and to an alteration in the taxonomic composition of communities in favour of generalist taxa. Our results suggest novel applications of this response as a sensitive tool to monitor salinisation of coastal soils and to detect salinity changes within peatland palaeoclimate archives. Specifically, we suggest that these relationships could be used to reconstruct millennial scale variability in salt-spray deposition-a proxy for changes in wind-conditions-from sub-fossil communities of Arcellinida and Euglyphida preserved in exposed coastal peatlands.

Monitoring chicken flock behaviour provides early warning of infection by human pathogen Campylobacter.

Campylobacter is the commonest bacterial cause of gastrointestinal infection in humans, and chicken meat is the major source of infection throughout the world. Strict and expensive on-farm biosecurity measures have been largely unsuccessful in controlling infection and are hampered by the time needed to analyse faecal samples, with the result that Campylobacter status is often known only after a flock has been processed. Our data demonstrate an alternative approach that monitors the behaviour of live chickens with cameras and analyses the 'optical flow' patterns made by flock movements. Campylobacter-free chicken flocks have higher mean and lower kurtosis of optical flow than those testing positive for Campylobacter by microbiological methods. We show that by monitoring behaviour in this way, flocks likely to become positive can be identified within the first 7-10 days of life, much earlier than conventional on-farm microbiological methods. This early warning has the potential to lead to a more targeted approach to Campylobacter control and also provides new insights into possible sources of infection that could transform the control of this globally important food-borne pathogen.

Variational integrator graph networks for learning energy-conserving dynamical systems.

Recent advances show that neural networks embedded with physics-informed priors significantly outperform vanilla neural networks in learning and predicting the long-term dynamics of complex physical systems from noisy data. Despite this success, there has only been a limited study on how to optimally combine physics priors to improve predictive performance. To tackle this problem we unpack and generalize recent innovations into individual inductive bias segments. As such, we are able to systematically investigate all possible combinations of inductive biases of which existing methods are a natural subset. Using this framework we introduce variational integrator graph networks-a novel method that unifies the strengths of existing approaches by combining an energy constraint, high-order symplectic variational integrators, and graph neural networks. We demonstrate, across an extensive ablation, that the proposed unifying framework outperforms existing methods, for data-efficient learning and in predictive accuracy, across both single- and many-body problems studied in the recent literature. We empirically show that the improvements arise because high-order variational integrators combined with a potential energy constraint induce coupled learning of generalized position and momentum updates which can be formalized via the partitioned Runge-Kutta method.

Coolsense® versus EMLA® for peripheral venous cannulation in adult volunteers: A randomised crossover trial.

Peripheral venous cannulation (PVC) is a commonly performed invasive medical procedure. Topical treatments such as the eutectic mixture of local anaesthetics (EMLA®, Aspen Pharmacare Australia Pty Ltd, St Leonards, NSW) attenuate the associated pain, but are limited by requiring up to one hour of application before becoming effective. The Coolsense® (Coolsense Medical Ltd., Tel Aviv, Israel) pain numbing applicator is a new device using a cryoanalgesic means to anaesthetise skin within seconds. Coolsense is being increasingly used for cannulation, but comparative studies are lacking. We recruited 64 healthy adult volunteers to this open-label two sequence, two period randomised crossover trial. Participants had two 20 gauge venous cannulae inserted, one on the dorsum of each hand. Each cannulation attempt was preceded by treatment with Coolsense or an EMLA patch containing 2.5% lidocaine and 2.5% prilocaine. The primary outcome was participant pain using the 0-10 numerical pain rating scale. Secondary outcomes were participant satisfaction scores on a 0-10 scale, treatment preference, and failed cannulation attempts. Participants were randomly assigned to either the Coolsense EMLA (n = 32) or EMLA Coolsense (n = 32) sequence. All participants completed the trial. The pooled mean paired difference of the numerical pain rating scale was -1.84 (95% confidence intervals -1.28 to -2.41; P < 0.001) in favour of EMLA. The pooled mean paired difference for satisfaction score was 2.26 (95% confidence intervals 1.46 to 3.07; P < 0.001) higher with EMLA. Most participants preferred EMLA over Coolsense (P < 0.001). There was no significant difference regarding failed cannulation between the two treatments (P = 0.14). Among healthy individuals undergoing elective PVC, EMLA was associated with reduced pain, increased satisfaction, and was the preferred treatment compared to Coolsense.

Port-Hamiltonian neural networks for learning explicit time-dependent dynamical systems.

Accurately learning the temporal behavior of dynamical systems requires models with well-chosen learning biases. Recent innovations embed the Hamiltonian and Lagrangian formalisms into neural networks and demonstrate a significant improvement over other approaches in predicting trajectories of physical systems. These methods generally tackle autonomous systems that depend implicitly on time or systems for which a control signal is known a priori. Despite this success, many real world dynamical systems are nonautonomous, driven by time-dependent forces and experience energy dissipation. In this study, we address the challenge of learning from such nonautonomous systems by embedding the port-Hamiltonian formalism into neural networks, a versatile framework that can capture energy dissipation and time-dependent control forces. We show that the proposed port-Hamiltonian neural network can efficiently learn the dynamics of nonlinear physical systems of practical interest and accurately recover the underlying stationary Hamiltonian, time-dependent force, and dissipative coefficient. A promising outcome of our network is its ability to learn and predict chaotic systems such as the Duffing equation, for which the trajectories are typically hard to learn.

Excavating past population structures by surname-based sampling: the genetic legacy of the Vikings in northwest England.

The genetic structures of past human populations are obscured by recent migrations and expansions and have been observed only indirectly by inference from modern samples. However, the unique link between a heritable cultural marker, the patrilineal surname, and a genetic marker, the Y chromosome, provides a means to target sets of modern individuals that might resemble populations at the time of surname establishment. As a test case, we studied samples from the Wirral Peninsula and West Lancashire, in northwest England. Place-names and archaeology show clear evidence of a past Viking presence, but heavy immigration and population growth since the industrial revolution are likely to have weakened the genetic signal of a 1,000-year-old Scandinavian contribution. Samples ascertained on the basis of 2 generations of residence were compared with independent samples based on known ancestry in the region plus the possession of a surname known from historical records to have been present there in medieval times. The Y-chromosomal haplotypes of these 2 sets of samples are significantly different, and in admixture analyses, the surname-ascertained samples show markedly greater Scandinavian ancestry proportions, supporting the idea that northwest England was once heavily populated by Scandinavian settlers. The method of historical surname-based ascertainment promises to allow investigation of the influence of migration and drift over the last few centuries in changing the population structure of Britain and will have general utility in other regions where surnames are patrilineal and suitable historical records survive.

Gaussian Processes for Personalized Interpretable Volatility Metrics in the Step-Down Ward.

Patients in a hospital step-down unit require a level of care that is between that of the intensive care unit (ICU) and that of the general ward. While many patients remain physiologically stabilized, others will suffer clinical emergencies and be readmitted to the ICU, with a subsequent high risk of mortality. Had the associated physiological deterioration been detected early, the emergency may have been less severe or avoided entirely. Current clinical monitoring is largely heuristic, requiring manual calculation of risk scores and the use of heuristic decision criteria. Technical drawbacks include ignoring the time-series dynamics of physiological measurements, and lacking patient-specificity (i.e., personalization of models to the individual patient). In this paper, we demonstrate how Gaussian process regression models can supplement current monitoring practice by providing interpretable and intuitive illustrations of erratic vital-sign volatility. These personalized volatility metrics may provide significantly advanced warning of deterioration, while minimizing the false alarms that induce so-called alarm fatigue. While many AI-based approaches to healthcare are criticized for being uninterpretable "black-box" methods, the cause of alarms generated from the proposed methods are explicitly interpretable and intuitive. We conclude that intelligent computational inference using methods such as those proposed can enhance current clinical decision making and potentially save lives.

Synthesis, characterisation and water-gas shift activity of nano-particulate mixed-metal (Al, Ti) cobalt oxides.

The formation of mixed-metal cobalt oxides, representing potential metal-support compounds for cobalt-based catalysts, has been observed at high conversion levels in the Fischer-Tropsch synthesis over metal oxide-supported cobalt catalysts. An often observed increase in the carbon dioxide selectivity at Fischer-Tropsch conversion levels above 80% has been suggested to be associated to the formation of water-gas shift active oxidic cobalt species. Mixed-metal cobalt oxides, namely cobalt aluminate and cobalt titanate, were therefore synthesised and tested for potential catalytic activity in the water-gas shift reaction. We present a preparation route for amorphous mixed-metal oxides via thermal treatment of metal precursors in benzyl alcohol. Calcination of the as prepared nanoparticles results in highly crystalline phases. The nano-particulate mixed-metal cobalt oxides were thoroughly analysed by means of X-ray diffraction, Raman spectroscopy, temperature-programmed reduction, X-ray absorption near edge structure spectroscopy, extended X-ray absorption fine structure, and high-resolution scanning transmission electron microscopy. This complementary characterisation of the synthesised materials allows for a distinct identification of the phases and their properties. The cobalt aluminate prepared has a cobalt-rich composition (Co1+xAl2-xO4) with a homogeneous atomic distribution throughout the nano-particulate structures, while the perovskite-type cobalt titanate (CoTiO3) features cobalt-lean smaller particles associated with larger ones with an increased concentration of cobalt. The cobalt aluminate prepared showed no water-gas shift activity in the medium-shift temperature range, while the cobalt titanate sample catalysed the conversion of water and carbon monoxide to hydrogen and carbon dioxide after an extended activation period. However, this perovskite underwent vast restructuring forming metallic cobalt, a known catalyst for the water-gas shift reaction at temperatures exceeding typical conditions for the cobalt-based Fischer-Tropsch synthesis, and anatase-TiO2. The partial reduction of the mixed-metal oxide and segregation was identified by means of post-run characterisation using X-ray diffraction, Raman spectroscopy, and transmission electron microscopy energy-dispersive spectrometry.

Bayesian Optimization of Personalized Models for Patient Vital-Sign Monitoring.

Gaussian process regression (GPR) provides a means to generate flexible personalized models of time series of patient vital signs. These models can perform useful clinical inference in ways that population-based models cannot. A challenge for the use of personalized models is that they must be amenable to a wide range of parameterizations, to accommodate the plausible physiology of any patient in the population. Additionally, optimal performance is typically achieved when models are regularized in light of the knowledge of the physiology of the individual patient. In this paper, we describe a method to build GP models with varying complexity (via covariance kernels) and regularization (via fixed priors over hyperparameters) on a patient-specific level, for the purpose of robust vital-sign forecasting. To this end, our results present evidence in support of two main hypotheses: 1) the use of patient-specific models can outperform population-based models for useful clinical tasks, such as vital-sign forecasting; and 2) the optimal values of (hyper)parameters of these models are best determined by sophisticated methods of optimization, due to high correlation between dimensions of the search space. The resulting models are sufficiently robust to inform clinicians of a patient's vital-sign trajectory and warn of imminent deterioration.

Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection.

BACKGROUND: Voice disorders affect patients profoundly, and acoustic tools can potentially measure voice function objectively. Disordered sustained vowels exhibit wide-ranging phenomena, from nearly periodic to highly complex, aperiodic vibrations, and increased "breathiness". Modelling and surrogate data studies have shown significant nonlinear and non-Gaussian random properties in these sounds. Nonetheless, existing tools are limited to analysing voices displaying near periodicity, and do not account for this inherent biophysical nonlinearity and non-Gaussian randomness, often using linear signal processing methods insensitive to these properties. They do not directly measure the two main biophysical symptoms of disorder: complex nonlinear aperiodicity, and turbulent, aeroacoustic, non-Gaussian randomness. Often these tools cannot be applied to more severe disordered voices, limiting their clinical usefulness. METHODS: This paper introduces two new tools to speech analysis: recurrence and fractal scaling, which overcome the range limitations of existing tools by addressing directly these two symptoms of disorder, together reproducing a "hoarseness" diagram. A simple bootstrapped classifier then uses these two features to distinguish normal from disordered voices. RESULTS: On a large database of subjects with a wide variety of voice disorders, these new techniques can distinguish normal from disordered cases, using quadratic discriminant analysis, to overall correct classification performance of 91.8 +/- 2.0%. The true positive classification performance is 95.4 +/- 3.2%, and the true negative performance is 91.5 +/- 2.3% (95% confidence). This is shown to outperform all combinations of the most popular classical tools. CONCLUSION: Given the very large number of arbitrary parameters and computational complexity of existing techniques, these new techniques are far simpler and yet achieve clinically useful classification performance using only a basic classification technique. They do so by exploiting the inherent nonlinearity and turbulent randomness in disordered voice signals. They are widely applicable to the whole range of disordered voice phenomena by design. These new measures could therefore be used for a variety of practical clinical purposes.

Likelihood-based artefact detection in continuously-acquired patient vital signs.

Robust continuous monitoring of patient vital signs (VS) is limited by artefactual data yielding measurements that are not representative of the patient's physiology. These artefacts are typified by several distinct "archetypes". We present several of these archetypal artefacts for heart rate (HR) monitoring, and propose a light weight, real-time algorithm to remove the majority of these artefacts. Most artefacts are not identifiable by their values in absolute terms, but instead by their values relative to other measurements nearby in time. We model temporally-proximate measurements as independent and identically distributed (i.i.d.) samples from a Gamma distribution. Measurements with low likelihood with respect to the distribution are candidates for artefact removal. This lightweight algorithm is important for real-time deployment on wearable sensors, which are becoming increasingly common in hospital and home care. The clinical applicability of artefact-removal is demonstrated in its ability to enhance patient deterioration detection. A Kalman filter-based patient monitoring algorithm is shown to improve early warning of deterioration when the proposed artefact-removal algorithm is used. We demonstrate this real-time system with patient data from a clinical trial that we have undertaken.

Past penguin colony responses to explosive volcanism on the Antarctic Peninsula.

Changes in penguin populations on the Antarctic Peninsula have been linked to several environmental factors, but the potentially devastating impact of volcanic activity has not been considered. Here we use detailed biogeochemical analyses to track past penguin colony change over the last 8,500 years on Ardley Island, home to one of the Antarctic Peninsula's largest breeding populations of gentoo penguins. The first sustained penguin colony was established on Ardley Island c. 6,700 years ago, pre-dating sub-fossil evidence of Peninsula-wide occupation by c. 1,000 years. The colony experienced five population maxima during the Holocene. Overall, we find no consistent relationships with local-regional atmospheric and ocean temperatures or sea-ice conditions, although the colony population maximum, c. 4,000-3,000 years ago, corresponds with regionally elevated temperatures. Instead, at least three of the five phases of penguin colony expansion were abruptly ended by large eruptions from the Deception Island volcano, resulting in near-complete local extinction of the colony, with, on average, 400-800 years required for sustainable recovery.

Data-adaptive test statistics for microarray data.

MOTIVATION: An important task in microarray data analysis is the selection of genes that are differentially expressed between different tissue samples, such as healthy and diseased. However, microarray data contain an enormous number of dimensions (genes) and very few samples (arrays), a mismatch which poses fundamental statistical problems for the selection process that have defied easy resolution. RESULTS: In this paper, we present a novel approach to the selection of differentially expressed genes in which test statistics are learned from data using a simple notion of reproducibility in selection results as the learning criterion. Reproducibility, as we define it, can be computed without any knowledge of the 'ground-truth', but takes advantage of certain properties of microarray data to provide an asymptotically valid guide to expected loss under the true data-generating distribution. We are therefore able to indirectly minimize expected loss, and obtain results substantially more robust than conventional methods. We apply our method to simulated and oligonucleotide array data. AVAILABILITY: By request to the corresponding author.

Bayesian Gaussian processes for identifying the deteriorating patient.

The step-down unit (SDU) is a high-acuity hospital environment, to which patients may be sent after discharge from the intensive care unit (ICU). About 1- in-7 patients will deteriorate in the SDU and require emergency readmission to the ICU. Upon readmission, these patients experience significantly higher mortality risks and lengths of stay. Gaussian process regression (GPR) models are proposed as a flexible, principled, probabilistic method to address the clinical need to monitor continuously patient time-series of vital signs acquired in the SDU. The proposed GPR models focus on the robust forecasting of patient heart rate time-series and on the early detection of patient deterioration. The proposed methods are tested with an SDU data set from the University of Pittsburgh Medical Center, comprising 333 patients, 59 of whom had at least one verified clinical emergency event. Results suggest that GPR-based heart rate monitoring provides superior advanced warning of deterioration compared to the current clinical practice of rules-based thresholding, and slightly outperforms the current state-of-the-art kernel density method, which requires 4 additional vital sign features.

A theoretical analysis of the selection of differentially expressed genes.

A great deal of recent research has focused on the challenging task of selecting differentially expressed genes from microarray data ("gene selection"). Numerous gene selection algorithms have been proposed in the literature, but it is often unclear exactly how these algorithms respond to conditions like small sample sizes or differing variances. Choosing an appropriate algorithm can therefore be difficult in many cases. In this paper we propose a theoretical analysis of gene selection, in which the probability of successfully selecting differentially expressed genes, using a given ranking function, is explicitly calculated in terms of population parameters. The theory developed is applicable to any ranking function which has a known sampling distribution, or one which can be approximated analytically. In contrast to methods based on simulation, the approach presented here is computationally efficient and can be used to examine the behavior of gene selection algorithms under a wide variety of conditions, even when the number of genes involved runs into the tens of thousands. The utility of our approach is illustrated by comparing three widely-used gene selection methods.

Cognitive tasks for driving a brain-computer interfacing system: a pilot study.

Different cognitive tasks were investigated for use with a brain-computer interface (BCI). The main aim was to evaluate which two of several candidate tasks lead to patterns of electroencephalographic (EEG) activity that could be differentiated most reliably and, therefore, produce the highest communication rate. An optimal signal processing method was also sought to enhance differentiation of EEG profiles across tasks. In ten normal subjects (five male), aged 29-54 years, EEG activity was recorded from four channels during cognitive tasks grouped in pairs, and performed alternately. Four imagery tasks were: spatial navigation around a familiar environment; auditory imagery of a familiar tune; and right and left motor imagery of opening and closing the hand. Signal processing methodology included autoregressive (AR) modeling and classification based on logistic regression and a nonlinear generative classifier. The highest communication rate was found using the navigation and auditory imagery tasks. In terms of classification performance and, hence, possible communication rate, these results were significantly better (p < 0.05) than those obtained with the classical pairing of motor tasks involving imaginary movements of the left and right hands. In terms of EEG data analysis, a nonlinear classification model provided more robust results than a linear model (p << 0.01), and a lower AR model order than those used in previous work was found to be effective. These findings have implications for establishing appropriate methods to operate BCI systems, particularly for disabled people who may experience difficulty with motor tasks, even motor imagery.