Fate of swallowed interleukin 8 and TNF alpha and effect on the wistar rat gut

To evaluate the passage of cytokines through the gastrointestinal tract, we investigated the digestion of interleukin-8 (IL-8) and tumour necrosis factor α (TNFα), in vitro and in vivo, and their propensity to induce intestinal inflammation. We serially immuno-assayed IL-8 and TNFα solutions co-incubated with each of three pancreatin preparations at pH 4.5 and pH 8. We gavaged IL-8, TNFα and marker into 15 Wistar rats, and measured their faecal cytokine concentrations by ELISA and histologically examined their guts. IL-8 immunoreactivity was extinguished by all pancreatin preparations after 1 h of incubation at 37 °C. TNFα concentration progressively fell from 1 to 4 h with all enzyme preparations. Buffer control samples maintained their cytokine concentrations throughout incubation. No IL-8 or TNFα was detected in any rat faecal pellets. There was no significant proinflammatory effect of the gavaged cytokines on rat intestine. IL-8 and TNFα in aqueous solution could well be fully digested in the CF gut when transit time is normal and exogenous enzymes are provided, although cytokines swallowed in viscous sputum may be protected from such digestion. Copyright © 2011 Elsevier B.V. All rights reserved

An automated failure mode and effect analysis based on high-level design specificication with behavior trees

Formal methods have significant benefits for developing safety critical systems, in that they allow for correctness proofs, model checking safety and liveness properties, deadlock checking, etc. However, formal methods do not scale very well and demand specialist skills, when developing real-world systems. For these reasons, development and analysis of large-scale safety critical systems will require effective integration of formal and informal methods. In this paper, we use such an integrative approach to automate Failure Modes and Effects Analysis (FMEA), a widely used system safety analysis technique, using a high-level graphical modelling notation (Behavior Trees) and model checking. We inject component failure modes into the Behavior Trees and translate the resulting Behavior Trees to SAL code. This enables us to model check if the system in the presence of these faults satisfies its safety properties, specified by temporal logic formulas. The benefit of this process is tool support that automates the tedious and error-prone aspects of FMEA.

Modelling socially optimal land allocations for sugar cane growing in North Queensland: a linked mathematical programming and choice modelling study

A modelling framework is developed to determine the joint economic and environmental net benefits of alternative land allocation strategies. Estimates of community preferences for preservation of natural land, derived from a choice modelling study, are used as input to a model of agricultural production in an optimisation framework. The trade-offs between agricultural production and environmental protection are analysed using the sugar industry of the Herbert River district of north Queensland as an example. Spatially-differentiated resource attributes and the opportunity costs of natural land determine the optimal tradeoffs between production and conservation for a range of sugar prices.

Combined hydraulic and biological modelling and full-scale validation of SBR process

The biological reactions during the settling and decant periods of Sequencing Batch Reactors (SBRs) are generally ignored as they are not easily measured or described by modelling approaches. However, important processes are taking place, and in particular when the influent is fed into the bottom of the reactor at the same time (one of the main features of the UniFed process), the inclusion of these stages is crucial for accurate process predictions. Due to the vertical stratification of both liquid and solid components, a one-dimensional hydraulic model is combined with a modified ASM2d biological model to allow the prediction of settling velocity, sludge concentration, soluble components and biological processes during the non-mixed periods of the SBR. The model is calibrated on a full-scale UniFed SBR system with tracer breakthrough tests, depth profiles of particulate and soluble compounds and measurements of the key components during the mixed aerobic period. This model is then validated against results from an independent experimental period with considerably different operating parameters. In both cases, the model is able to accurately predict the stratification and most of the biological reactions occurring in the sludge blanket and the supernatant during the non-mixed periods. Together with a correct description of the mixed aerobic period, a good prediction of the overall SBR performance can be achieved.

Method for determining reaction rate of mild steel containers during melting of magnesium-aluminium alloys and effect of aluminium content on directionally solidified microstructures

A magnesium alloy of eutectic composition (33 wt-'%Al) was directionally solidified in mild steel tubes at two growth rates, 32 and 580 mum s(-1,) in a temperature gradient between 10 and 20 K mm(-1). After directional solidification, the composition of each specimen varied dramatically, from 32'%Al in the region that had remained solid to 18%Al (32 mum s(-1) specimen) and 13%Al (580 mum s(-1) specimen) at the plane that had been quenched from the eutectic temperature. As the aluminium content decreased, the microstructure contained an increasing volume fraction of primary magnesium dendrites and the eutectic morphology gradually changed from lamellar to partially divorced. The reduction in aluminium content was caused by the growth of an Al-Fe phase ahead of the Mg-Al growth front. Most of the growth of the Al-Fe phase occurred during the remelting period before directional solidification. The thickness of the Al-Fe phase increased with increased temperature and time of contact with the molten Mg-Al alloy. (C) 2003 Maney Publishing.

Applying ontologies to business and systems modelling techniques and perspectives: Lessons learned

For many years in the area of business systems analysis and design, practitioners and researchers alike have been searching for some comprehensive basis on which to evaluate, compare, and engineer techniques that are promoted for use in the modelling of systems' requirements. To date, while many frameworks, factors, and facets have been forthcoming, none appear to be based on a sound theory. In light of this dilemma, over the last 10 years, attention has been devoted by researchers to the use of ontology to provide some theoretical basis for the advancement of the business systems modelling discipline. This paper outlines how we have used a particular ontology for this purpose over the last five years. In particular we have learned that the understandability and the applicability of the selected ontology must be clear for IS professionals, the results of any ontological evaluation must be tempered by economic efficiency considerations of the stakeholders involved, and ontologies may have to be focused for the business purpose and type of user involved in the modelling situation.

Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems

New tools derived from advances in molecular biology have not been widely adopted in plant breeding for complex traits because of the inability to connect information at gene level to the phenotype in a manner that is useful for selection. In this study, we explored whether physiological dissection and integrative modelling of complex traits could link phenotype complexity to underlying genetic systems in a way that enhanced the power of molecular breeding strategies. A crop and breeding system simulation study on sorghum, which involved variation in 4 key adaptive traits-phenology, osmotic adjustment, transpiration efficiency, stay-green-and a broad range of production environments in north-eastern Australia, was used. The full matrix of simulated phenotypes, which consisted of 547 location-season combinations and 4235 genotypic expression states, was analysed for genetic and environmental effects. The analysis was conducted in stages assuming gradually increased understanding of gene-to-phenotype relationships, which would arise from physiological dissection and modelling. It was found that environmental characterisation and physiological knowledge helped to explain and unravel gene and environment context dependencies in the data. Based on the analyses of gene effects, a range of marker-assisted selection breeding strategies was simulated. It was shown that the inclusion of knowledge resulting from trait physiology and modelling generated an enhanced rate of yield advance over cycles of selection. This occurred because the knowledge associated with component trait physiology and extrapolation to the target population of environments by modelling removed confounding effects associated with environment and gene context dependencies for the markers used. Developing and implementing this gene-to-phenotype capability in crop improvement requires enhanced attention to phenotyping, ecophysiological modelling, and validation studies to test the stability of candidate genetic regions.