Modelling, nitrogen dynamics in sugarcane systems: Recent advances and applications

Substantial amounts of nitrogen (N) fertiliser are necessary for commercial sugarcane production because of the large biomass produced by sugarcane crops. Since this fertiliser is a substantial input cost and has implications if N is lost to the environment, there are pressing needs to optimise the supply of N to the crops' requirements. The complexity of the N cycle and the strong influence of climate, through its moderation of N transformation processes in the soil and its impact on N uptake by crops, make simulation-based approaches to this N management problem attractive. In this paper we describe the processes to be captured in modelling soil and plant N dynamics in sugarcane systems, and review the capability for modelling these processes. We then illustrate insights gained into improved management of N through simulation-based studies for the issues of crop residue management, irrigation management and greenhouse gas emissions. We conclude by identifying processes not currently represented in the models used for simulating N cycling in sugarcane production systems, and illustrate ways in which these can be partially overcome in the short term. (c) 2005 Elsevier B.V. All rights reserved.

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results We show that GPNN has high power to detect even relatively small genetic effects (2–3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (

The diagnostic interview for psychoses (DIP): development, reliability and applications

Background. We describe the development, reliability and applications of the Diagnostic Interview for Psychoses (DIP), a comprehensive interview schedule for psychotic disorders. Method. The DIP is intended for use by interviewers with a clinical background and was designed to occupy the middle ground between fully structured, lay-administered schedules, and semi-structured., psychiatrist-administered interviews. It encompasses four main domains: (a) demographic data; (b) social functioning and disability; (c) a diagnostic module comprising symptoms, signs and past history ratings; and (d) patterns of service utilization Lind patient-perceived need for services. It generates diagnoses according to several sets of criteria using the OPCRIT computerized diagnostic algorithm and can be administered either on-screen or in a hard-copy format. Results. The DIP proved easy to use and was well accepted in the field. For the diagnostic module, inter-rater reliability was assessed on 20 cases rated by 24 clinicians: good reliability was demonstrated for both ICD-10 and DSM-III-R diagnoses. Seven cases were interviewed 2-11 weeks apart to determine test-retest reliability, with pairwise agreement of 0.8-1.0 for most items. Diagnostic validity was assessed in 10 cases, interviewed with the DIP and using the SCAN as 'gold standard': in nine cases clinical diagnoses were in agreement. Conclusions. The DIP is suitable for use in large-scale epidemiological studies of psychotic disorders. as well as in smaller Studies where time is at a premium. While the diagnostic module stands on its own, the full DIP schedule, covering demography, social functioning and service utilization makes it a versatile multi-purpose tool.

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background: The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results: We show that GPNN has high power to detect even relatively small genetic effects (2-3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (

Quantification of geological uncertainty and risk using stochastic simulation and applications in the coal mining industry

Stochastic simulation is a recognised tool for quantifying the spatial distribution of geological uncertainty and risk in earth science and engineering. Metals mining is an area where simulation technologies are extensively used; however, applications in the coal mining industry have been limited. This is particularly due to the lack of a systematic demonstration illustrating the capabilities these techniques have in problem solving in coal mining. This paper presents two broad and technically distinct areas of applications in coal mining. The first deals with the use of simulation in the quantification of uncertainty in coal seam attributes and risk assessment to assist coal resource classification, and drillhole spacing optimisation to meet pre-specified risk levels at a required confidence. The second application presents the use of stochastic simulation in the quantification of fault risk, an area of particular interest to underground coal mining, and documents the performance of the approach. The examples presented demonstrate the advantages and positive contribution stochastic simulation approaches bring to the coal mining industry