A systematic approach to error isolation in computerized wastewater simulation models

Activated sludge models are used extensively in the study of wastewater treatment processes. While various commercial implementations of these models are available, there are many people who need to code models themselves using the simulation packages available to them, Quality assurance of such models is difficult. While benchmarking problems have been developed and are available, the comparison of simulation data with that of commercial models leads only to the detection, not the isolation of errors. To identify the errors in the code is time-consuming. In this paper, we address the problem by developing a systematic and largely automated approach to the isolation of coding errors. There are three steps: firstly, possible errors are classified according to their place in the model structure and a feature matrix is established for each class of errors. Secondly, an observer is designed to generate residuals, such that each class of errors imposes a subspace, spanned by its feature matrix, on the residuals. Finally. localising the residuals in a subspace isolates coding errors. The algorithm proved capable of rapidly and reliably isolating a variety of single and simultaneous errors in a case study using the ASM 1 activated sludge model. In this paper a newly coded model was verified against a known implementation. The method is also applicable to simultaneous verification of any two independent implementations, hence is useful in commercial model development.

Murine ventricular L-type Ca2+ current is enhanced by zinterol via beta(1)-adrenoceptors, and is reduced in TG4 mice overexpressing the human beta(2)-adrenoceptor

1 The functional coupling of B-2-adrenoceptors (beta (2)-ARs) to murine L-type Ca2+ current (I-Ca(L)) was investigated with two different approaches. The beta (2)-AR signalling cascade was activated either with the beta (2)-AR selective agonist zinterol (myocytes from wild-type mice), or by spontaneously active, unoccupied beta (2)-ARs (myocytes from TG4 mice with 435 fold overexpression of human beta (2)-ARs). Ca2+ and Ba2+ currents were recorded in the whole-cell and cell-attached configuration of the patch- clamp technique, respectively. 2 Zinterol (10 muM) significantly increased I-Ca(L) amplitude of wild-type myocytes by 19+/-5%, and this effect was markedly enhanced after inactivation of Gi-proteins with pertussis-toxin (PTX; 76+/-13% increase). However, the effect of zinterol was entirely mediated by the beta (1)-AR subtype, since it was blocked by the beta (1)-AR selective antagonist CGP 20712A (300 nM). The beta (2)-AR selective antagonist ICI 118,551 (50 nM) did not affect the response of I-Ca(L) to zinterol. 3 In myocytes with beta (2)-AR overexpression I-Ca(L) was not stimulated by the activated signalling cascade. On the contrary, I-Ca(L) was lower in TG4 myocytes and a significant reduction of single-channel activity was identified as a reason for the lower whole-cell I-Ca(L). The beta (2)-AR inverse agonist ICI 118,551 did not further decrease I-Ca(L). PTX-treatment increased current amplitude to values found in control myocytes. 4 In conclusion, there is no evidence for beta (2)-AR mediated increases of I-Ca(L) in wild-type mouse ventricular myocytes. Inactivation of Gi-proteins does not unmask beta (2)-AR responses to zinterol, but augments beta (1)-AR mediated increases of I-Ca(L). In the mouse model of beta (2)-AR overexpression I-Ca(L) is reduced due to tonic activation of Gi-proteins.

Linear models of simple cells: Correspondence to real cell responses and space spanning properties

Despite their limitations, linear filter models continue to be used to simulate the receptive field properties of cortical simple cells. For theoreticians interested in large scale models of visual cortex, a family of self-similar filters represents a convenient way in which to characterise simple cells in one basic model. This paper reviews research on the suitability of such models, and goes on to advance biologically motivated reasons for adopting a particular group of models in preference to all others. In particular, the paper describes why the Gabor model, so often used in network simulations, should be dropped in favour of a Cauchy model, both on the grounds of frequency response and mutual filter orthogonality.

Immune-derived opioids and peripheral antinociception

1. Recent findings have suggested a significant involvement of the immune system in the control of pain. Immune cells contain opioid peptides that are released within inflamed tissue and act at opioid receptors on peripheral sensory nerve endings. It is also apparent that different types of lymphocytes contain P-endorphin, memory T cells containing more beta -endorphin than naive cells. 2. These findings highlight an integral link between immune cell migration and inflammatory pain, The present review highlights immune system involvement in the site-directed control of inflammatory pain. 3. Full-length mRNA transcripts for opioid precursor proteins are expressed in immune cells. Increased expression of pro-opiomelanocortin mRNA and beta -endorphin has been demonstrated in stimulated lymphocytes and lymphocytes from animals with inflammation. 4. Cytokines and corticotropin-releasing factor (CRF) release opioids from immune cells, Potent peripheral analgesia due to direct injection of CRF can be blocked by antagonists to CRF, antibodies to opioid peptides, antisense to CRF and opioid receptor-specific antagonists. The release of opioid peptides from lymphocytes is calcium dependent and opioid receptor specific. Furthermore, endogenous sources of opioid peptides produce potent analgesia when implanted into the spinal cord. 5. Activated immune cells migrate directly to inflamed tissue using cell adhesion molecules to adhere to the epithelial surface of the vasculature in inflamed tissue. Lymphocytes that have been activated can express opioid peptides, Memory type T cells that contain opioid peptides are present within inflamed tissue; naive cells are not present in inflamed tissue and do not contain opioid peptides, Inhibiting the migration of memory type T cells into inflamed tissue by blocking selectins results in reduced numbers of beta -endorphin containing cells, a reduced quantity of beta -endorphin in inflamed paws and reduced stress- and CRF-induced peripheral analgesia. 6. Immunosuppression is associated with increased pain in patients. Moreover, immunosuppression results in decreased lymphocyte numbers as well as decreased analgesia in animal models.

Ataxia-telangiectasia: chronic activation of damage-responsive functions is reduced by alpha-lipoic acid

Cells from patients with the genetic disorder ataxia-telangiectasia (A-T) are hypersensitive to ionizing radiation and radiomimetic agents, both of which generate reactive oxygen species capable of causing oxidative damage to DNA and other macromolecules. We describe in A-T cells constitutive activation of pathways that normally respond to genotoxic stress, Basal levels of p53 and p21(WAF1/CIP1), phosphorylation on serine 15 of p53, and the Tyr15-phosphorylated form of cdc2 are chronically elevated in these cells. Treatment of A-T cells with the antioxidant alpha -lipoic acid significantly reduced the levels of these proteins, pointing to the involvement of reactive oxygen species in their chronic activation. These findings suggest that the absence of functional ATM results in a mild but continuous state of oxidative stress, which could account for several features of the pleiotropic phenotype of A-T.

Discrimination of adsorption kinetic models for the description of hydrocarbon adsorption in activated carbon

Five kinetic models for adsorption of hydrocarbons on activated carbon are compared and investigated in this study. These models assume different mass transfer mechanisms within the porous carbon particle. They are: (a) dual pore and surface diffusion (MSD), (b) macropore, surface, and micropore diffusion (MSMD), (c) macropore, surface and finite mass exchange (FK), (d) finite mass exchange (LK), and (e) macropore, micropore diffusion (BM) models. These models are discriminated using the single component kinetic data of ethane and propane as well as the multicomponent kinetics data of their binary mixtures measured on two commercial activated carbon samples (Ajax and Norit) under various conditions. The adsorption energetic heterogeneity is considered for all models to account for the system. It is found that, in general, the models assuming diffusion flux of adsorbed phase along the particle scale give better description of the kinetic data.

Power of the joint segregation analysis method for testing mixed major-gene and polygene inheritance models of quantitative traits

Understanding the genetic architecture of quantitative traits can greatly assist the design of strategies for their manipulation in plant-breeding programs. For a number of traits, genetic variation can be the result of segregation of a few major genes and many polygenes (minor genes). The joint segregation analysis (JSA) is a maximum-likelihood approach for fitting segregation models through the simultaneous use of phenotypic information from multiple generations. Our objective in this paper was to use computer simulation to quantify the power of the JSA method for testing the mixed-inheritance model for quantitative traits when it was applied to the six basic generations: both parents (P-1 and P-2), F-1, F-2, and both backcross generations (B-1 and B-2) derived from crossing the F-1 to each parent. A total of 1968 genetic model-experiment scenarios were considered in the simulation study to quantify the power of the method. Factors that interacted to influence the power of the JSA method to correctly detect genetic models were: (1) whether there were one or two major genes in combination with polygenes, (2) the heritability of the major genes and polygenes, (3) the level of dispersion of the major genes and polygenes between the two parents, and (4) the number of individuals examined in each generation (population size). The greatest levels of power were observed for the genetic models defined with simple inheritance; e.g., the power was greater than 90% for the one major gene model, regardless of the population size and major-gene heritability. Lower levels of power were observed for the genetic models with complex inheritance (major genes and polygenes), low heritability, small population sizes and a large dispersion of favourable genes among the two parents; e.g., the power was less than 5% for the two major-gene model with a heritability value of 0.3 and population sizes of 100 individuals. The JSA methodology was then applied to a previously studied sorghum data-set to investigate the genetic control of the putative drought resistance-trait osmotic adjustment in three crosses. The previous study concluded that there were two major genes segregating for osmotic adjustment in the three crosses. Application of the JSA method resulted in a change in the proposed genetic model. The presence of the two major genes was confirmed with the addition of an unspecified number of polygenes.

Immune responses to ISCOM (R) formulations in animal and primate models

ISCOMs(R) are typically 40 nm cage-like structures comprising antigen, saponin, cholesterol and phospholipid. ISCOMs(R) have been shown to induce antibody responses and activate T helper cells and cyrolytic T lymphocytes in a number of animal species, including non-human primates. Recent clinical studies have demonstrated that ISCOMs(R) are also able to induce antibody and cellular immune responses in humans. This review describes the current understanding of the ability of ISCOMs(R) to induce immune responses and the mechanisms underlying this property. Recent progress in the characterisation and manufacture of ISCOMs(R) will also be discussed. (C) 2001 Elsevier Science Ltd. All rights reserved.

Hemoglobin-degrading, aspartic proteases of blood-feeding parasites - Substrate specificity revealed by homology models

Blood-feeding parasites, including schistosomes, hookworms, and malaria parasites, employ aspartic proteases to make initial or early cleavages in ingested host hemoglobin. To better understand the substrate affinity of these aspartic proteases, sequences were aligned with and/or three-dimensional, molecular models were constructed of the cathepsin D-like aspartic proteases of schistosomes and hookworms and of plasmepsins of Plasmodium falciparum and Plasmodium vivax, using the structure of human cathepsin D bound to the inhibitor pepstatin as the template. The catalytic subsites S5 through S4' were determined for the modeled parasite proteases. Subsequently, the crystal structure of mouse renin complexed with the nonapeptidyl inhibitor t-butyl-CO-His-Pro-Phe-His-Leu [CHOHCH2]Leu-Tyr-Tyr-Ser-NH2 (CH-66) was used to build homology models of the hemoglobin-degrading peptidases docked with a series of octapeptide substrates. The modeled octapeptides included representative sites in hemoglobin known to be cleaved by both Schistosoma japonicum cathepsin D and human cathepsin D, as well as sites cleaved by one but not the other of these enzymes. The peptidase-octapeptide substrate models revealed that differences in cleavage sites were generally attributable to the influence of a single amino acid change among the P5 to P4' residues that would either enhance or diminish the enzymatic affinity. The difference in cleavage sites appeared to be more profound than might be expected from sequence differences in the enzymes and hemoglobins. The findings support the notion that selective inhibitors of the hemoglobin-degrading peptidases of blood-feeding parasites at large could be developed as novel anti-parasitic agents.

Advances in application of climate prediction in agriculture

Agricultural ecosystems and their associated business and government systems are diverse and varied. They range from farms, to input supply businesses, to marketing and government policy systems, among others. These systems are dynamic and responsive to fluctuations in climate. Skill in climate prediction offers considerable opportunities to managers via its potential to realise system improvements (i.e. increased food production and profit and/or reduced risks). Realising these opportunities, however, is not straightforward as the forecasting skill is imperfect and approaches to applying the existing skill to management issues have not been developed and tested extensively. While there has been much written about impacts of climate variability, there has been relatively little done in relation to applying knowledge of climate predictions to modify actions ahead of likely impacts. However, a considerable body of effort in various parts of the world is now being focused on this issue of applying climate predictions to improve agricultural systems. In this paper, we outline the basis for climate prediction, with emphasis on the El Nino-Southern Oscillation phenomenon, and catalogue experiences at field, national and global scales in applying climate predictions to agriculture. These diverse experiences are synthesised to derive general lessons about approaches to applying climate prediction in agriculture. The case studies have been selected to represent a diversity of agricultural systems and scales of operation. They also represent the on-going activities of some of the key research and development groups in this field around the world. The case studies include applications at field/farm scale to dryland cropping systems in Australia, Zimbabwe, and Argentina. This spectrum covers resource-rich and resource-poor farming with motivations ranging from profit to food security. At national and global scale we consider possible applications of climate prediction in commodity forecasting (wheat in Australia) and examine implications on global wheat trade and price associated with global consequences of climate prediction. In cataloguing these experiences we note some general lessons. Foremost is the value of an interdisciplinary systems approach in connecting disciplinary Knowledge in a manner most suited to decision-makers. This approach often includes scenario analysis based oil simulation with credible models as a key aspect of the learning process. Interaction among researchers, analysts and decision-makers is vital in the development of effective applications all of the players learn. Issues associated with balance between information demand and supply as well as appreciation of awareness limitations of decision-makers, analysts, and scientists are highlighted. It is argued that understanding and communicating decision risks is one of the keys to successful applications of climate prediction. We consider that advances of the future will be made by better connecting agricultural scientists and practitioners with the science of climate prediction. Professions involved in decision making must take a proactive role in the development of climate forecasts if the design and use of climate predictions are to reach their full potential. (C) 2001 Elsevier Science Ltd. All rights reserved.

The plausibility of long-wavelength stress correlation or stress magnitude as a mechanism for precursory seismicity: Results from two simple elastic models

Observations of accelerating seismic activity prior to large earthquakes in natural fault systems have raised hopes for intermediate-term eartquake forecasting. If this phenomena does exist, then what causes it to occur? Recent theoretical work suggests that the accelerating seismic release sequence is a symptom of increasing long-wavelength stress correlation in the fault region. A more traditional explanation, based on Reid's elastic rebound theory, argues that an accelerating sequence of seismic energy release could be a consequence of increasing stress in a fault system whose stress moment release is dominated by large events. Both of these theories are examined using two discrete models of seismicity: a Burridge-Knopoff block-slider model and an elastic continuum based model. Both models display an accelerating release of seismic energy prior to large simulated earthquakes. In both models there is a correlation between the rate of seismic energy release with the total root-mean-squared stress and the level of long-wavelength stress correlation. Furthermore, both models exhibit a systematic increase in the number of large events at high stress and high long-wavelength stress correlation levels. These results suggest that either explanation is plausible for the accelerating moment release in the models examined. A statistical model based on the Burridge-Knopoff block-slider is constructed which indicates that stress alone is sufficient to produce accelerating release of seismic energy with time prior to a large earthquake.