John Paul Gosling

Continuous Effector CD8+ T Cell Production in a Controlled Persistent Infection Is Sustained by a Proliferative Intermediate Population

Highly functional CD8(+) effector T (Teff) cells can persist in large numbers during controlled persistent infections, as exemplified by rare HIV-infected individuals who control the virus. Here we examined the cellular mechanisms that maintain ongoing T effector responses using a mouse model for persistent Toxoplasma gondii infection. In mice expressing the protective MHC-I molecule, H-2L(d), a dominant T effector response against a single parasite antigen was maintained without a contraction phase, correlating with ongoing presentation of the dominant antigen. Large numbers of short-lived Teff cells were continuously produced via a proliferative, antigen-dependent intermediate (Tint) population with a memory-effector hybrid phenotype. During an acute, resolved infection, decreasing antigen load correlated with a sharp drop in the Tint cell population and subsequent loss of the ongoing effector response. Vaccination approaches aimed at the development of Tint populations might prove effective against pathogens that lead to chronic infection.

Evaluating uncertainty in convective cloud microphysics using statistical emulation

The microphysical properties of convective clouds determine their radiative effects on climate, the amount and intensity of precipitation as well as dynamical features. Realistic simulation of these cloud properties presents a major challenge. In particular, because models are complex and slow to run, we have little understanding of how the considerable uncertainties in parameterized processes feed through to uncertainty in the cloud responses. Here we use statistical emulation to enable a Monte Carlo sampling of a convective cloud model to quantify the sensitivity of 12 cloud properties to aerosol concentrations and nine model parameters representing the main microphysical processes. We examine the response of liquid and ice-phase hydrometeor concentrations, precipitation, and cloud dynamics for a deep convective cloud in a continental environment. Across all cloud responses, the concentration of the Aitken and accumulation aerosol modes and the collection efficiency of droplets by graupel particles have the most influence on the uncertainty. However, except at very high aerosol concentrations, uncertainties in precipitation intensity and amount are affected more by interactions between drops and graupel than by large variations in aerosol. The uncertainties in ice crystal mass and number are controlled primarily by the shape of the crystals, ice nucleation rates, and aerosol concentrations. Overall, although aerosol particle concentrations are an important factor in deep convective clouds, uncertainties in several processes significantly affect the reliability of complex microphysical models. The results suggest that our understanding of aerosol-cloud interaction could be greatly advanced by extending the emulator approach to models of cloud systems.

Nonparametric elicitation for heavy-tailed prior distributions

In the context of statistical analysis, elicitation is the process of translating someone’s beliefs about some uncertain quantities into a probability distribution. The person’s judgements about the quantities are usually fitted to some member of a convenient parametric family. This approach does not allow for the possibility that any number of distributions could fit the same judgements. In this paper, elicitation of an expert’s beliefs is treated as any other inference problem: the facilitator of the elicitation exercise has prior beliefs about the form of the expert’s density function, the facilitator elicits judgements about the density function, and the facilitator’s beliefs about the expert’s density function are updated in the light of these judgements. This paper investigates prior beliefs about an expert’s density function and shows how many different types of judgement can be handled by this method. This elicitation method begins with the belief that the expert’s density will roughly have the shape of a t density. This belief is then updated through a Gaussian process model using judgements from the expert. The method gives a framework for quantifying the facilitator’s uncertainty about a density given judgements about the mean and percentiles of the expert’s distribution. A property of Gaussian processes can be manipulated to include judgements about the derivatives of the density, which allows the facilitator to incorporate mode judgements and judgements on the sign of the density at any given point. The benefit of including the second type of judgement is that substantial computational time can be saved.

SHELF: The Sheffield Elicitation Framework

The Sheffield elicitation framework is an expert knowledge elicitation framework that has been devised over a number of years and many substantial expert knowledge elicitation exercises to give a transparent and reliable way of collecting expert opinions. The framework is based on the principles of behavioural aggregation where a facilitator-guided group interact and share information to arrive at a consensus. It was originally designed for helping to elicit judgements about single uncertain variables, but, in recent years, the framework and the associated software implementations have been extended to accommodate judgements about more complex multidimensional variables and geographically-dispersed experts. In this chapter, we discuss the aims and foundations of the framework, its extensions and its notable applications.

A Bayes Linear Approach to Weight-of-Evidence Risk Assessment for Skin Allergy

We introduce a strategy for quantifying and synthesising uncertainty about elements of a risk assessment using Bayes linear methods. We view the population of subjective belief structures and the use of Bayes linear adjustments as a flexible and transparent tool for risk assessors who want to quantify their uncertainty about hazard based on disparate sources of information. For motivation, we use an application of the strategy to human skin sensitisation risk assessment where there are many competing sources of information available.

An Efficient Screening Method for Computer Experiments

Computer simulators of real-world processes are often computationally expensive and require many inputs. The problem of the computational expense can be handled using emulation technology; however, highly multidimensional input spaces may require more simulator runs to train and validate the emulator. We aim to reduce the dimensionality of the problem by screening the simulator’s inputs for nonlinear effects on the output rather than distinguishing between negligible and active effects. Our proposed method is built upon the elementary effects (EE) method for screening and uses a threshold value to separate the inputs with linear and nonlinear effects. The technique is simple to implement and acts in a sequential way to keep the number of simulator runs down to a minimum, while identifying the inputs that have nonlinear effects. The algorithm is applied on a set of simulated examples and a rabies disease simulator where we observe run savings ranging between 28% and 63% compared with the batch EE method. Supplementary materials for this article are available online.

Predicting rainy seasons: quantifying the beliefs of prophets

In general, meteorologists find it difficult to make seasonal predictions in the north-east region of Brazil due to the contrasting atmospheric phenomena that take place there. The rain prophets claim to be able to predict the seasonal weather by observing the behavior of nature. Their predictions have a strong degree of subjectivity; this makes science (especially meteorology) disregard these predictions, which could be a relevant source of information for prediction models. In this article, we regard the prophets’ knowledge from a subjectivist point of view: we apply elicitation of expert knowledge techniques to extract their opinions and convert them into probability densities that represent their predictions of forthcoming rainy seasons.

Modelling consumer intakes of vegetable oils and fats

Vegetable oils and fats make up a significant part of the energy intake in typical European diets. However, their use as ingredients in a diverse range of different foods means that their consumption is often hidden, especially when oils and fats are used for cooking. As a result, there are no reliable estimates of the consumption of different vegetable oils and fats in the diet of European consumers for use in, for example, nutritional assessments or chemical risk assessments. We have developed an innovative model to estimate the consumption of vegetable oils and fats by European Union consumers using the European Union consumption databases and elements of probabilistic modelling. A key feature of the approach is the assessment of uncertainty in the modelling assumptions that can be used to build user confidence and to guide future development. Graphical Abstract

Robust detection of exotic infectious diseases in animal herds: A comparative study of three decision methodologies under severe uncertainty

When animals are transported and pass through customs, some of them may have dangerous infectious diseases. Typically, due to the cost of testing, not all animals are tested: a reasonable selection must be made. How to test effectively whilst avoiding costly disease outbreaks? First, we extend a model proposed in the literature for the detection of invasive species to suit our purpose, and we discuss the main sources of model uncertainty, many of which are hard to quantify. Secondly, we explore and compare three decision methodologies on the problem at hand, namely, Bayesian statistics, info-gap theory and imprecise probability theory, all of which are designed to handle severe uncertainty. We show that, under rather general conditions, every info-gap solution is maximal with respect to a suitably chosen imprecise probability model, and that therefore, perhaps surprisingly, the set of maximal options can be inferred at least partly-and sometimes entirely-from an info-gap analysis.

Identification of transcript regulatory patterns in cell differentiation

Motivation Studying transcript regulatory patterns in cell differentiation is critical in understanding its complex nature of the formation and function of different cell types. This is done usually by measuring gene expression at different stages of the cell differentiation. However, if the gene expression data available are only from the mature cells, we have some challenges in identifying transcript regulatory patterns that govern the cell differentiation. Results We propose to exploit the information of the lineage of cell differentiation in terms of correlation structure between cell types. We assume that two different cell types that are close in the lineage will exhibit many common genes that are co‐expressed relative to those that are far in the lineage. Current analysis methods tend to ignore this correlation by testing for differential expression assuming some sort of independence between cell types. We employ a Bayesian approach to estimate the posterior distribution of the mean of expression in each cell type, by taking into account the cell formation path in the lineage. This enables us to infer genes that are specific in each cell type, indicating the genes are involved in directing the cell differentiation to that particular cell type. We illustrate the method using gene expression data from a study of haematopoiesis. Availability and implementation R codes to perform the analysis are available in http://www1.maths.leeds.ac.uk/˜arief/R/CellDiff/. Contact a.gusnanto@leeds.ac.uk Supplementary information Supplementary data are available at Bioinformatics online.