Soil Governance: Accessing Cross-Disciplinary Perspectives

Soil provides the foundation for agricultural and environmental systems, and are subject to a complex governance regime of property rights and secondary impacts from industry and domestic land use. Complex natural resource management issues require approaches to governance that acknowledge uncertainty and complexity. Theories of next generation environmental governance assume that inclusion of diverse perspectives will improve reform directions and encourage behaviour change. This paper reports on a qualitative survey of an international workshop that brought together cross-disciplinary perspectives to address the challenges of soil governance. Results reveal the challenges of communicating effectively across disciplines. The findings suggest that strategies for improved soils governance must focus on increasing communications with community stakeholders and engaging land managers in designing shared governance regimes. The need for more conscious articulation of the challenges of cross-disciplinary environments is discussed and strategies for increasing research collaboration in soils governance are suggested. The identified need for more systematic approaches to cross-disciplinary learning, including reporting back of cross-disciplinary initiatives to help practitioners learn from past experience, forms part of the rationale for this paper

Mutational signatures in colon cancer.

ObjectiveRecently, many tumor sequencing studies have inferred and reported on mutational signatures, short nucleotide patterns at which particular somatic base substitutions appear more often. A number of signatures reflect biological processes in the patient and factors associated with cancer risk. Our goal is to infer mutational signatures appearing in colon cancer, a cancer for which environmental risk factors vary by cancer subtype, and compare the signatures to those in adult stem cells from normal colon. We also compare the mutational signatures to others in the literature.ResultsWe apply a probabilistic mutation signature model to somatic mutations previously reported for six adult normal colon stem cells and 431 colon adenocarcinomas. We infer six mutational signatures in colon cancer, four being specific to tumors with hypermutation. Just two signatures explained the majority of mutations in the small number of normal aging colon samples. All six signatures are independently identified in a series of 295 Chinese colorectal cancers

A Theory of Mind investigation into the appreciation of visual jokes in schizophrenia

BACKGROUND: There is evidence that groups of people with schizophrenia have deficits in Theory of Mind (ToM) capabilities. Previous studies have found these to be linked to psychotic symptoms (or psychotic symptom severity) particularly the presence of delusions and hallucinations. METHODS: A visual joke ToM paradigm was employed where subjects were asked to describe two types of cartoon images, those of a purely Physical nature and those requiring inferences of mental states for interpretation, and to grade them for humour and difficulty. Twenty individuals with a DSM-lV diagnosis of schizophrenia and 20 healthy matched controls were studied. Severity of current psychopathology was measured using the Krawiecka standardized scale of psychotic symptoms. IQ was estimated using the Ammons and Ammons quick test. RESULTS: Individuals with schizophrenia performed significantly worse than controls in both conditions, this difference being most marked in the ToM condition. No relationship was found for poor ToM performance and psychotic positive symptomatology, specifically delusions and hallucinations. CONCLUSION: There was evidence for a compromised ToM capability in the schizophrenia group on this visual joke task. In this instance this could not be linked to particular symptomatology

Integrating the processes in the evolutionary system of domestication

Genetics has long been used as a source of evidence to understand domestication origins. A recent shift in the emphasis of archaeological evidence from a rapid transition paradigm of hunter-gatherers to agriculturalists, to a protracted transition paradigm has highlighted how the scientific framework of interpretation of genetic data was quite dependent on archaeological evidence, resulting in a period of discord in which the two evidence types appeared to support different paradigms. Further examination showed that the discriminatory power of the approaches employed in genetics was low, and framed within the rapid paradigm rather than testing it. In order to interpret genetic data under the new protracted paradigm it must be taken into account how that paradigm changes our expectations of genetic diversity. Preliminary examination suggests that a number of features that constituted key evidence in the rapid paradigm are likely to be interpreted very differently in the protracted paradigm. Specifically, in the protracted transition the mode and mechanisms involved in the evolution of the domestication syndrome have become much more influential in the shape of genetic diversity. The result is that numerous factors interacting over several levels of organization in a domestication system need to be taken into account in order to understand the evolution of the process. This presents a complex problem of integration of different data types which is difficult to describe formally. One possible way forward is to use Bayesian approximation approaches that allow complex systems to be measured in a way that does not require such formality

Non-linear regression models for Approximate Bayesian Computation

Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the curse of dimensionality when the number of summary statistics is increased. Here we propose a machine-learning approach to the estimation of the posterior density by introducing two innovations. The new method fits a nonlinear conditional heteroscedastic regression of the parameter on the summary statistics, and then adaptively improves estimation using importance sampling. The new algorithm is compared to the state-of-the-art approximate Bayesian methods, and achieves considerable reduction of the computational burden in two examples of inference in statistical genetics and in a queueing model.Comment: 4 figures; version 3 minor changes; to appear in Statistics and Computin

Simulation-based model selection for dynamical systems in systems and population biology

Computer simulations have become an important tool across the biomedical sciences and beyond. For many important problems several different models or hypotheses exist and choosing which one best describes reality or observed data is not straightforward. We therefore require suitable statistical tools that allow us to choose rationally between different mechanistic models of e.g. signal transduction or gene regulation networks. This is particularly challenging in systems biology where only a small number of molecular species can be assayed at any given time and all measurements are subject to measurement uncertainty. Here we develop such a model selection framework based on approximate Bayesian computation and employing sequential Monte Carlo sampling. We show that our approach can be applied across a wide range of biological scenarios, and we illustrate its use on real data describing influenza dynamics and the JAK-STAT signalling pathway. Bayesian model selection strikes a balance between the complexity of the simulation models and their ability to describe observed data. The present approach enables us to employ the whole formal apparatus to any system that can be (efficiently) simulated, even when exact likelihoods are computationally intractable.Comment: This article is in press in Bioinformatics, 2009. Advance Access is available on Bioinformatics webpag

Adaptive approximate Bayesian computation for complex models

Approximate Bayesian computation (ABC) is a family of computational techniques in Bayesian statistics. These techniques allow to fi t a model to data without relying on the computation of the model likelihood. They instead require to simulate a large number of times the model to be fi tted. A number of re finements to the original rejection-based ABC scheme have been proposed, including the sequential improvement of posterior distributions. This technique allows to de- crease the number of model simulations required, but it still presents several shortcomings which are particu- larly problematic for costly to simulate complex models. We here provide a new algorithm to perform adaptive approximate Bayesian computation, which is shown to perform better on both a toy example and a complex social model.Comment: 14 pages, 5 figure

Correlation structures in applied probability

This thesis examines consequences of correlation structure in three areas of applied probability: mathematical population genetics, birth processes, and "exchangeable" measures on distributive lattices. The first three chapters concern probabilistic models in genetics. Initially we generalize the Moran model to allow more than one individual to reproduce per generation, investigating the effect of this on the behaviour of the model. The conclusion is that while things apparently happen faster, the basic properties are the same. This model also serves to unify conventional neutral theory, as it links the Moran model to the Wright-Fisher model. We then consider aspects of the neutral theory. Commonly a neutral model is supposed in which successive generations behave independently. This may well be unrealistic. Here we take the Moran model and adapt it to allow for correlations in offspring numbers between generations. An analysis of the model shows that the conditional distribution of allele frequencies is unchanged, although the expected number of alleles present decreases. Similar results are also obtained when correlation is introduced to the more general model with more than one reproducer per generation. In each case the approach involves a detailed study of the genealogy of the models. Next we consider the effect of correlation in Markov Birth Processes. We show that if the birth rates form a super(sub) linear sequence then the sizes of its families are positively(negatively) correlated. From this we prove a conjecture of Faddy which says that if the birth rates of a process X(t) are super(sub)-linear then the variance ratio V (t) (defined as VarX(t)/(EX(t)[EX(t)/X(0)-1])) is greater than (less than) 1. Finally we study correlation inequalities. The FKG Inequality is a well known result giving sufficient conditions for positive correlations in probability measures on distributive lattices. There are few analogous results concerning negative correlation. We give sufficient conditions for a particular form of negative correlation when the underlying distributions possess a certain exchangeability property