Search

Summary: To reduce operational costs and to ensure security of supply, gas distribution networks require accurate forecasts of the demand for gas. Among domestic and commercial customers, demand relates primarily to the weather and patterns of life and work. Public holidays have a pronounced effect which often spreads into neighbouring days. We call this spread the 'proximity effect'. Traditionally, the days over which the proximity effect is felt are prespecified in fixed windows around each holiday, allowing no uncertainty in their identification. We are motivated by an application to modelling daily gas demand in two large British regions. We introduce a novel model which does not fix the days on which the proximity effect is felt. Our approach uses a four‐state, non‐homogeneous hidden Markov model, with cyclic dynamics, where the classification of days as public holidays is observed, but the assignment of days as 'pre‐holiday', 'post‐holiday' or 'normal' days is unknown. The number of days to the preceding and succeeding holidays guide transitions between states. We apply Bayesian inference and illustrate the benefit of our modelling approach. A version of the model is now being used by one of the UK's regional distribution networks. [ABSTRACT FROM AUTHOR]

• MAGE is multi-dynamic in that it models temporal fluctuations in FC independently from fluctuations in the mean of the activity. • MAGE reveals stronger changes in FC over time than single-dynamic approaches, such as sliding window correlations. • Multi-dynamic modelling provides an explanation and a solution as to why resting fMRI FC has previously looked so stable. • MAGE models fMRI data as a set of reoccurring brain states, and importantly, these states do not have to be binary and mutually exclusive (e.g., multiple states can be active at one time-point). • MAGE estimated time-varying FC is a better predictor of behavioural variability in the resting-state fMRI data than established methods. [Display omitted] The activity of functional brain networks is responsible for the emergence of time-varying cognition and behaviour. Accordingly, time-varying correlations (Functional Connectivity) in resting fMRI have been shown to be predictive of behavioural traits, and psychiatric and neurological conditions. Typically, methods that measure time varying Functional Connectivity (FC), such as sliding windows approaches, do not separately model when changes occur in the mean activity levels from when changes occur in the FC, therefore conflating these two distinct types of modulation. We show that this can bias the estimation of time-varying FC to appear more stable over time than it actually is. Here, we propose an alternative approach that models changes in the mean brain activity and in the FC as being able to occur at different times to each other. We refer to this method as the Multi-dynamic Adversarial Generator Encoder (MAGE) model, which includes a model of the network dynamics that captures long-range time dependencies, and is estimated on fMRI data using principles of Generative Adversarial Networks. We evaluated the approach across several simulation studies and resting fMRI data from the Human Connectome Project (1003 subjects), as well as from UK Biobank (13301 subjects). Importantly, we find that separating fluctuations in the mean activity levels from those in the FC reveals much stronger changes in FC over time, and is a better predictor of individual behavioural variability. [ABSTRACT FROM AUTHOR]

• A novel stochastic daily rainfall modelling framework, the Advanced Meteorology Explorer, is developed based on hidden Markov models and copulas. • This framework allows for the simulation of physically consistent synthetic daily rainfall data, coherently in space and time, on a high-resolution grid. • Simulations are shown to accurately capture rainfall occurrence and intensity, as well as long-duration drought behaviour. • These simulations can be effectively used for drought and flood risk assessment in any industry impacted by rainfall. Synthetic rainfall simulations from stochastic models are commonly used for water resource management, as they are able to provide a wider range of meteorological conditions than those seen in the observed record. Here, we present a novel stochastic rainfall modelling framework, the Advanced Meteorology Explorer (AME), which combines and extends existing methods to enhance model flexibility, and meet a number of key water industry needs. This framework allows for the simulation of physically consistent synthetic daily rainfall data, coherently in space and time, on a high-resolution grid over a region of interest. The AME uses an advanced hidden Markov model structure within a Bayesian hierarchical framework to represent daily rainfall at a set of locations in a region, conditional on important climate drivers. The climate drivers included in the rainfall model at each location are able to vary using penalised regression, ensuring a transferable model that can be applied to different locations without adaptation. The dependence between locations is modelled following a flexible copula approach, able to capture varying dependence structures within the data, allowing for spatially coherent simulations at the modelled locations. Simulations are then interpolated to a high-resolution grid using a terrain adjusted inverse-distance weighted interpolation method. The AME framework is applied to 105 years (1914–2018) of daily rainfall data at 39 sites in the Greater Anglian region of the UK, and used to generate 1000 alternative realisations of the same period on a 5 km grid over the region. Validation of these simulations shows how the AME framework is able to accurately capture rainfall occurrence and intensity, as well as long-duration meteorological drought behaviour, important for quantifying water resource risk in this dry region. This framework has the potential to be applied to other regions, incorporate additional weather variables and the effect of climate change, and the resulting simulations can be used for environmental risk assessment in any industry impacted by rainfall. [ABSTRACT FROM AUTHOR]

In 1996, the discovery of variant Creutzfeldt-Jakob disease in the UK raised serious concerns about a large-scale epidemic. These concerns have been heightened by the recent discovery of people in Britain who were infected through blood transfusion. The outbreak of variant Creutzfeldt-Jakob disease in France emerged more recently with 23 cases observed to date. We use a hidden Markov model to predict the scale of the epidemic in France. As accurate data on the most important epidemiological parameters are scarce, we incorporate estimates from previous studies. Parameter estimation is performed by using Markov chain Monte Carlo methods from which credible intervals for our predictions are obtained. The sensitivity of these predictions to important assumptions regarding population exposure is assessed. [ABSTRACT FROM AUTHOR]

Genome3D consortium is a collaborative project involving protein structure prediction and annotation resources developed by six world-leading structural bioinformatics groups, based in the United Kingdom (namely Blundell, Murzin, Gough, Sternberg, Orengo, and Jones). The main objective of Genome3D serves as a common portal to provide both predicted models and annotations of proteins in model organisms, using several resources developed by these labs such as CATH-Gene3D, DOMSERF, pDomTHREADER, PHYRE, SUPERFAMILY, FUGUE/TOCATTA, and VIVACE. These resources primarily use SCOP- and/or CATH-based protein domain assignments. Another objective of Genome3D is to compare structural classifications of protein domains in CATH and SCOP databases and to provide a consensus mapping of CATH and SCOP protein superfamilies. CATH/SCOP mapping analyses led to the identification of total of 1429 consensus superfamilies.Currently, Genome3D provides structural annotations for ten model organisms, including Homo sapiens, Arabidopsis thaliana, Mus musculus, Escherichia coli, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Plasmodium falciparum, Staphylococcus aureus, and Schizosaccharomyces pombe. Thus, Genome3D serves as a common gateway to each structure prediction/annotation resource and allows users to perform comparative assessment of the predictions. It, thus, assists researchers to broaden their perspective on structure/function predictions of their query protein of interest in selected model organisms.

Environmental heterogeneity shapes the uneven distribution of resources available to foragers, and is ubiquitous in nature. Optimal foraging theory predicts that an animal's ability to exploit resource patches is key to foraging success. However, the potential fitness costs and benefits of foraging in a heterogeneous environment are difficult to measure empirically. Heterogeneity may provide higher-quality foraging opportunities, or alternatively could increase the cost of resource acquisition because of reduced patch density or increased competition. Here, we study the influence of physical environmental heterogeneity on behaviour and reproductive success of black-legged kittiwakes, Rissa tridactyla. From GPS tracking data at 15 colonies throughout their British and Irish range, we found that environments that were physically more heterogeneous were associated with longer trip duration, more time spent foraging while away from the colony, increased overlap of foraging areas between individuals and lower breeding success. These results suggest that there is greater competition between individuals for finite resources in more heterogeneous environments, which comes at a cost to reproduction. Resource hotspots are often considered beneficial, as individuals can learn to exploit them if sufficiently predictable. However, we demonstrate here that such fitness gains can be countered by greater competition in more heterogeneous environments.