Capillary network models for transport in packed beds: Considerations of pore aspect ratio

The conventional analysis for the estimation of the tortuosity factor for transport in porous media is modified here to account for the effect of pore aspect ratio. Structural models of the porous medium are also constructed for calculating the aspect ratio as a function of porosity. Comparison of the model predictions with the extensive data of Currie (1960) for the effective diffusivity of hydrogen in packed beds shows good agreement with a network model of randomly oriented intersecting pores for porosities upto about 50 percent, which is the region of practical interest. The predictions based on this network model are also found to be in better agreement with the data of Currie than earlier expressions developed for unconsolidated and grainy media.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.

Changes in three-dimensional architecture of microfilaments in cultured vascular smooth muscle cells during phenotypic modulation

To investigate changes in the three-dimensional microfilament architecture of vascular smooth muscle cells (SMC) during the process of phenotypic modulation, rabbit aortic SMCs cultured under different conditions and at different time points were either labelled with fluorescein-conjugated probes to cytoskeletal and contractile proteins for observation by confocal laser scanning microscopy, or extracted with Triton X-100 for scanning electron microscopy. Densely seeded SMCs in primary culture, which maintain a contractile phenotype, display prominent linear myofilament bundles (stress fibres) that are present throughout the cytoplasm with alpha-actin filaments predominant in the central part and beta-actin filaments in the periphery of the cell. Intermediate filaments form a meshed network interconnecting the stress fibres and linking directly to the nucleus. Moderately and sparsely seeded SMCs, which modulate toward the synthetic phenotype during the first 5 days of culture, undergo a gradual redistribution of intermediate filaments from the perinuclear region toward the peripheral cytoplasm and a partial disassembly of stress fibres in the central part of the upper cortex of the cytoplasm, with an obvious decrease in alpha-actin and myosin staining. These changes are reversed in moderately seeded SMCs by day 8 of culture when they have reached confluence. The results reveal two changes in microfilament architecture in SMCs as they undergo a change in phenotype: the redistribution of intermediate filaments probably due to an increase in synthetic organelles in the perinuclear area, and the partial disassembly of stress fibres which may reflect a degradation of contractile components.

Modeling the fluid-flow-induced stress and collapse in a dendritic network

An analytical approach to the stress development in the coherent dendritic network during solidification is proposed. Under the assumption that stresses are developed in the network as a result of the friction resisting shrinkage-induced interdendritic fluid flow, the model predicts the stresses in the solid. The calculations reflect the expected effects of postponed dendrite coherency, slower solidification conditions, and variations of eutectic volume fraction and shrinkage. Comparing the calculated stresses to the measured shear strength of equiaxed mushy zones shows that it is possible for the stresses to exceed the strength, thereby resulting in reorientation or collapse of the dendritic network.

Computational binding assays of antigenic peptides

Computer models can be combined with laboratory experiments for the efficient determination of (i) peptides that bind MHC molecules and (ii) T-cell epitopes. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures. This requires the definition of standards and experimental protocols for model application. We describe the requirements for validation and assessment of computer models. The utility of combining accurate predictions with a limited number of laboratory experiments is illustrated by practical examples. These include the identification of T-cell epitopes from IDDM-, melanoma- and malaria-related antigens by combining computational and conventional laboratory assays. The success rate in determining antigenic peptides, each in the context of a specific HLA molecule, ranged from 27 to 71%, while the natural prevalence of MHC-binding peptides is 0.1-5%.

Knowledge discovery and data mining in biological databases

The new technologies for Knowledge Discovery from Databases (KDD) and data mining promise to bring new insights into a voluminous growing amount of biological data. KDD technology is complementary to laboratory experimentation and helps speed up biological research. This article contains an introduction to KDD, a review of data mining tools, and their biological applications. We discuss the domain concepts related to biological data and databases, as well as current KDD and data mining developments in biology.

Sulfite : Cytochrome c oxidoreductase from Thiobacillus novellus - Purification, characterization, and molecular biology of a heterodimeric member of the sulfite oxidase family

Direct oxidation of sulfite to sulfate occurs in various photo- and chemotrophic sulfur oxidizing microorganisms as the final step in the oxidation of reduced sulfur compounds and is catalyzed by sulfite:cytochrome c oxidoreductase (EC 1.8.2.1), Here we show that the enzyme from Thiobacillus novellus is a periplasmically located alpha beta heterodimer, consisting of a 40.6-kDa subunit containing a molybdenum cofactor and an 8.8-kDa monoheme cytochrome c(552) smbunit (midpoint redox potential, Em(8.0) = +280 mV), The organic component of the molybdenum cofactor was identified as molybdopterin contained in a 1:1 ratio to the Mo content of the enzyme. Electron paramagnetic resonance spectroscopy revealed the presence of a sulfite-inducible Mo(V) signal characteristic of sulfite:acceptor oxidoreductases. However, pH-dependent changes in the electron paramagnetic resonance signal were not detected. Kinetic studies showed that the enzyme exhibits a ping-pong mechanism involving two reactive sites. K-m values for sulfite and cytochrome c(550) were determined to be 27 and 4 mu M, respectively; the enzyme was found to be reversibly inhibited by sulfate and various buffer ions. The sorAB genes, which encode the enzyme, appear to form an operon, which is preceded by a putative extracytoplasmic function-type promoter and contains a hairpin loop termination structure downstream of sorB. While SorA exhibits significant similarities to known sequences of eukaryotic and bacterial sulfite:acceptor oxidoreductases, SorB does not appear to be closely related to any known c-type cytochromes.

Heterogeneous distribution of isoactins in cultured vascular smooth muscle cells does not reflect segregation of contractile and cytoskeletal domains

We have previously demonstrated that or-smooth muscle (alpha -SM) actin is predominantly distributed in the central region and beta -non-muscle (beta -NM) actin in the periphery of cultured rabbit aortic smooth muscle cells (SMCs). To determine whether this reflects a special form of segregation of contractile and cytoskeletal components in SMCs, this study systematically investigated the distribution relationship of structural proteins using high-resolution confocal laser scanning fluorescent microscopy. Not only isoactins but also smooth muscle myosin heavy chain, alpha -actinin, vinculin, and vimentin were heterogeneously distributed in the cultured SMCs. The predominant distribution of beta -NM actin in the cell periphery was associated with densely distributed vinculin plaques and disrupted or striated myosin and ol-actinin aggregates, which may reflect a process of stress fiber assembly during cell spreading and focal adhesion formation. The high-level labeling of alpha -SM actin in the central portion of stress fibers was related to continuous myosin and punctate alpha -actinin distribution, which may represent the maturation of the fibrillar structures. The findings also suggest that the stress fibers, in which actin and myosin filaments organize into sar-comere-like units with alpha -actinin-rich dense bodies analogous to Z-lines, are the contractile vimentin structures of cultured SMCs that link to the network of vimentin-containing intermediate alpha -actinin filaments through the dense bodies and dense plaques.

Biology and production attributes of the farmed Rusa deer

Rusa deer were introduced to Queensland in the 1970s and 1980s, and they now are about half of the farmed deer herd. Rusa tolerate the subtropical climatic and disease environments. Rusa venison has a low fat content and is acceptable to consumers. Protein and energy requirements are similar to values for other tropical deer. Growth may be limited by the low protein content of tropical grasses during winter. Rusa deer could contribute to the diversity of the Australian livestock industries.

An object-oriented neural network approach to short-term traffic forecasting

This paper discusses an object-oriented neural network model that was developed for predicting short-term traffic conditions on a section of the Pacific Highway between Brisbane and the Gold Coast in Queensland, Australia. The feasibility of this approach is demonstrated through a time-lag recurrent network (TLRN) which was developed for predicting speed data up to 15 minutes into the future. The results obtained indicate that the TLRN is capable of predicting speed up to 5 minutes into the future with a high degree of accuracy (90-94%). Similar models, which were developed for predicting freeway travel times on the same facility, were successful in predicting travel times up to 15 minutes into the future with a similar degree of accuracy (93-95%). These results represent substantial improvements on conventional model performance and clearly demonstrate the feasibility of using the object-oriented approach for short-term traffic prediction. (C) 2001 Elsevier Science B.V. All rights reserved.

Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding

Peptides that induce and recall T-cell responses are called T-cell epitopes. T-cell epitopes may be useful in a subunit vaccine against malaria. Computer models that simulate peptide binding to MHC are useful for selecting candidate T-cell epitopes since they minimize the number of experiments required for their identification. We applied a combination of computational and immunological strategies to select candidate T-cell epitopes. A total of 86 experimental binding assays were performed in three rounds of identification of HLA-All binding peptides from the six preerythrocytic malaria antigens. Thirty-six peptides were experimentally confirmed as binders. We show that the cyclical refinement of the ANN models results in a significant improvement of the efficiency of identifying potential T-cell epitopes. (C) 2001 by Elsevier Science Inc.

Enzymology and molecular biology of prokaryotic sulfite oxidation

Despite its toxicity, sulfite plays a key role in oxidative sulfur metabolism and there are even some microorganisms which can use it as sole electron source. Sulfite is the main intermediate in the oxidation of sulfur compounds to sulfate, the major product of most dissimilatory sulfur-oxidizing prokaryotes. Two pathways of sulfite oxidation are known: (1) direct oxidation to sulfate catalyzed by a sulfite: acceptor oxidoreductase, which is thought to be a molybdenum-containing enzyme; (2) indirect oxidation under the involvement of the enzymes adenylylsulfate (APS) reductase and ATP sulfurylase and/or adenylylsulfate phosphate adenylyltransferase with APS as an intermediate. The latter pathway allows substrate phosphorylation and occurs in the bacterial cytoplasm. Direct oxidation appears to have a wider distribution; however, a redundancy of pathways has been described for diverse photo- or chemotrophic, sulfite-oxidizing prokaryotes. In many pro- and also eukaryotes sulfite is formed as a degradative product from molecules containing sulfur as a heteroatom. In these organisms detoxification of sulfite is generally achieved by direct oxidation to sulfate. (C) 2001 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.

Computational immunology: The coming of age

The explosive growth in biotechnology combined with major advancesin information technology has the potential to radically transformimmunology in the postgenomics era. Not only do we now have readyaccess to vast quantities of existing data, but new data with relevanceto immunology are being accumulated at an exponential rate. Resourcesfor computational immunology include biological databases and methodsfor data extraction, comparison, analysis and interpretation. Publiclyaccessible biological databases of relevance to immunologists numberin the hundreds and are growing daily. The ability to efficientlyextract and analyse information from these databases is vital forefficient immunology research. Most importantly, a new generationof computational immunology tools enables modelling of peptide transportby the transporter associated with antigen processing (TAP), modellingof antibody binding sites, identification of allergenic motifs andmodelling of T-cell receptor serial triggering.

Methods for prediction of peptide binding to MHC molecules: A comparative study

Background: A variety of methods for prediction of peptide binding to major histocompatibility complex (MHC) have been proposed. These methods are based on binding motifs, binding matrices, hidden Markov models (HMM), or artificial neural networks (ANN). There has been little prior work on the comparative analysis of these methods. Materials and Methods: We performed a comparison of the performance of six methods applied to the prediction of two human MHC class I molecules, including binding matrices and motifs, ANNs, and HMMs. Results: The selection of the optimal prediction method depends on the amount of available data (the number of peptides of known binding affinity to the MHC molecule of interest), the biases in the data set and the intended purpose of the prediction (screening of a single protein versus mass screening). When little or no peptide data are available, binding motifs are the most useful alternative to random guessing or use of a complete overlapping set of peptides for selection of candidate binders. As the number of known peptide binders increases, binding matrices and HMM become more useful predictors. ANN and HMM are the predictive methods of choice for MHC alleles with more than 100 known binding peptides. Conclusion: The ability of bioinformatic methods to reliably predict MHC binding peptides, and thereby potential T-cell epitopes, has major implications for clinical immunology, particularly in the area of vaccine design.

Aspects of the biology of the yabby Trypea australiensis (Dana) (Decapoda : Thalassinidea) and the potential of burrow counts as an indirect measure of population density

The thalassinidean shrimp Trypea australiensis (the yabby) commonly occurs on intertidal sandflats and subtidal regions of sheltered embayments and estuaries along the east coast of Australia and is harvested commercially and recreationally for use as bait by anglers. The potential for counts of burrow openings to provide a reliable indirect estimate of the abundance of yabbies was examined on intertidal sandflats on North Stradbroke Island (Queensland, Australia). The relationship between the number of burrow openings and the abundance of yabbies was generally poor and also varied significantly through time, casting doubt on previous estimates of abundance for this species based on unvalidated hole counts. Spatial and temporal variation in population density, the size at maturity and the reproductive period of the yabby were also assessed. Except for an initial peak in abundance as a result of recruitment, the density of yabbies was constant throughout the study but considerably less than that estimated from a previous study in the same area. Ovigerous females were recorded at 3 mm carapace length (CL) which is smaller than previously recorded for this species and thalassinideans in general. Small ovigerous females were found throughout the study, including the summer months, which is unusual for thalassinideans in the intertidal zone. It was hypothesised that in the intertidal zone, small female yabbies may be able to balance the metabolic demands of reproduction and respiration at higher temperatures than can larger females allowing them to reproduce in the warmer months.