Cyclic labellings with constraints at two distances

Motivated by problems in radio channel assignment, we consider the vertex-labelling of graphs with non-negative integers. The objective is to minimise the span of the labelling, subject to constraints imposed at graph distances one and two. We show that the minimum span is (up to rounding) a piecewise linear function of the constraints, and give a complete specification, together with associated optimal assignments, for trees and cycles

International Workshop on Nutrient Balances for Sustainable Agricultural Production and Natural Resource Management in Southeast Asia, Bangkok, Thailand, 20-22 February 2001: selected papers and presentations

Soil management / Soil properties / Soil fertility / Soil degradation / Crop production / Farmers / Agricultural extension / Farming systems / Sustainability / Rice / Cassava / Vegetables / Maize / Fertilizers / Decision support tools / Economic aspects

Risk models and scores for type 2 diabetes: Systematic review

This article is published under a Creative Commons Attribution Non Commercial (CC BY-NC 3.0) licence that allows reuse subject only to the use being non-commercial and to the article being fully attributed (http://creativecommons.org/licenses/by-nc/3.0).Objective - To evaluate current risk models and scores for type 2 diabetes and inform selection and implementation of these in practice. Design - Systematic review using standard (quantitative) and realist (mainly qualitative) methodology. Inclusion - criteria Papers in any language describing the development or external validation, or both, of models and scores to predict the risk of an adult developing type 2 diabetes. Data sources - Medline, PreMedline, Embase, and Cochrane databases were searched. Included studies were citation tracked in Google Scholar to identify follow-on studies of usability or impact. Data extraction - Data were extracted on statistical properties of models, details of internal or external validation, and use of risk scores beyond the studies that developed them. Quantitative data were tabulated to compare model components and statistical properties. Qualitative data were analysed thematically to identify mechanisms by which use of the risk model or score might improve patient outcomes. Results - 8864 titles were scanned, 115 full text papers considered, and 43 papers included in the final sample. These described the prospective development or validation, or both, of 145 risk prediction models and scores, 94 of which were studied in detail here. They had been tested on 6.88 million participants followed for up to 28 years. Heterogeneity of primary studies precluded meta-analysis. Some but not all risk models or scores had robust statistical properties (for example, good discrimination and calibration) and had been externally validated on a different population. Genetic markers added nothing to models over clinical and sociodemographic factors. Most authors described their score as “simple” or “easily implemented,” although few were specific about the intended users and under what circumstances. Ten mechanisms were identified by which measuring diabetes risk might improve outcomes. Follow-on studies that applied a risk score as part of an intervention aimed at reducing actual risk in people were sparse. Conclusion - Much work has been done to develop diabetes risk models and scores, but most are rarely used because they require tests not routinely available or they were developed without a specific user or clear use in mind. Encouragingly, recent research has begun to tackle usability and the impact of diabetes risk scores. Two promising areas for further research are interventions that prompt lay people to check their own diabetes risk and use of risk scores on population datasets to identify high risk “hotspots” for targeted public health interventions.Tower Hamlets, Newham, and City and Hackney primary care trusts and National Institute of Health Research

Mathematical models in physiology

Computational modelling of biological processes and systems has witnessed a remarkable development in recent years. The search-term (modelling OR modeling) yields over 58000 entries in PubMed, with more than 34000 since the year 2000: thus, almost two-thirds of papers appeared in the last 5–6 years, compared to only about one-third in the preceding 5–6 decades.\ud \ud The development is fuelled both by the continuously improving tools and techniques available for bio-mathematical modelling and by the increasing demand in quantitative assessment of element inter-relations in complex biological systems. This has given rise to a worldwide public domain effort to build a computational framework that provides a comprehensive theoretical representation of integrated biological function—the Physiome.\ud \ud The current and next issues of this journal are devoted to a small sub-set of this initiative and address biocomputation and modelling in physiology, illustrating the breadth and depth of experimental data-based model development in biological research from sub-cellular events to whole organ simulations

Long-Term Impact of Leucaena-Based Grazing Systems on Soil Acidity

Soil acidification and land degradation issues are assuming increasing importance in Australia and challenging the concept of sustainablity of current land management systems. In this study the impact of a 22 year old Leucaena leucocephala / Urochloa mosambicensis (Leucaena) pasture production system on soil acidification and selected soil chemical properties was compared to an adjacent Urochloa mosambicensis (Sabi) area. Significant acidification and cation depletion was observed to 70 cm under the Leucaena when compared to the Sabi system. The net acidification rate for the Leucaena system was estimated to be 2.73 kmol H+ ha-1 yr-1 of which 0.17 kmol H+ ha-1 yr-1 was estimated to have originated from animal product removal. These preliminary results bring into question the long-term sustainability of these legume based production systems

What can artificial life offer ecology? (abstract)

Artificial life is the simulation and synthesis of living systems, and ALife models show how interactions between simple entities give rise to complex effects. Ecology is the study of the distribution and abundance of organisms, and ecological modelling involves fitting a linear model to a large data set and using that model to identify key causal factors at work in a complex ecosystem. We are interested in whether the individualbased modelling approach of ALife can be usefully employed in ecology. ALife models are “opaque thought experiments” (Di Paolo et al., 2000, Proc. ALife VII, p.497). They show that a phenomenon can arise from a given set of assumptions in cases where the implication is not clear from intuition alone: e.g., that spatial structure in a population can lead to altruistic behaviour. This type of modelling can be useful to ecology by showing the plausibility of a novel concept or process, which in turn suggests new natural experiments and new forms of data to collect. However, we argue that ALife models can go beyond this “proof of concept” role and serve as a direct account of data in the same way that statistical models do. We focus on a typical problem from ecology: the effect of clearing powerline corridors through a forest on the local wildlife populations (Clarke et al., 2006,Wildlife Research, 33, p.615). The real data set in this case is complex and, of course, we don’t know the true effects that underlie it. We therefore generated a fictional data set that reflects aspects of the original problem while allowing complete control over the simulated environment. The idea is to construct a test case for looking at the relative success of different modelling approaches. We know the true picture because we generated the data, but which modelling approach will get closer to the truth? The fitting of generalized linear models as is conventional in ecology, or the use of individual-based simulations as in ALife? Statistical models are fitted using some variant of the method of maximum likelihood: given the data, which of the models in the family we’re considering (e.g., a linear regression) makes the observed data most plausible? When dealing with simulations, however, it is difficult to establish that one model is a better fit to data than another. Simulations have many parameters, it may be difficult to determine a level of granularity at which the simulation output is supposed to “match” the data, and there will be no analytically tractable likelihood function. These problems are solved by the method of indirect inference (Gouri´eroux et al., 1993, J. Applied Econometrics, 8, p.S85) in which an auxiliary model is fitted to both the real data and to the output from competing simulation models. The best simulation model is the one producing the closest match to the data in terms of fitted parameter values in the auxiliary model. Using indirect inference with our fictional data set we demonstrate that ALife simulation models can be fitted to realistic ecological data, that they can out-compete standard statistical approaches, and that they can thus be used in ecology for more than just conceptual exploration

Estimating Small Area Income Deprivation: An Iterative Proportional Fitting Approach

Small area estimation and in particular the estimation of small area income deprivation has potential value in the development of new or alternative components of multiple deprivation indices. These new approaches enable the development of income distribution threshold based as opposed to benefit count based measures of income deprivation and so enable the alignment of regional and national measures such as the Households Below Average Income with small area measures. This paper briefly reviews a number of approaches to small area estimation before describing in some detail an iterative proportional fitting based spatial microsimulation approach. This approach is then applied to the estimation of small area HBAI rates at the small area level in Wales in 2003-5. The paper discusses the results of this approach, contrasts them with contemporary ‘official’ income deprivation measures for the same areas and describes a range of ways to assess the robustness of the results

Knowledge-based vision and simple visual machines

The vast majority of work in machine vision emphasizes the representation of perceived objects and events: it is these internal representations that incorporate the 'knowledge' in knowledge-based vision or form the 'models' in model-based vision. In this paper, we discuss simple machine vision systems developed by artificial evolution rather than traditional engineering design techniques, and note that the task of identifying internal representations within such systems is made difficult by the lack of an operational definition of representation at the causal mechanistic level. Consequently, we question the nature and indeed the existence of representations posited to be used within natural vision systems (i.e. animals). We conclude that representations argued for on a priori grounds by external observers of a particular vision system may well be illusory, and are at best place-holders for yet-to-be-identified causal mechanistic interactions. That is, applying the knowledge-based vision approach in the understanding of evolved systems (machines or animals) may well lead to theories and models that are internally consistent, computationally plausible, and entirely wrong