Examination of the role of intestinal Fatty acid-binding protein in drug absorption using a parallel artificial membrane permeability assay

Transcellular diffusion across the absorptive epithelial cells (enterocytes) of the small intestine is the main route of absorption for most orally administered drugs. The process by which lipophilic compounds transverse the aqueous environment of the cytoplasm, however, remains poorly defined. In the present study, we have identified a structurally diverse group of lipophilic drugs that display low micromolar binding affinities for a cytosolic lipid-binding protein—intestinal fatty acid-binding protein (I-FABP). Binding to I-FABP significantly enhanced the transport of lipophilic drug molecules across a model membrane, and the degree of transport enhancement was related to both drug lipophilicity and I-FABP binding affinity. These data suggest that intracellular lipid-binding proteins such as I-FABP may enhance the membrane transport of lipophilic xenobiotics and facilitate drug access to the enterocyte cytoplasm and cytoplasmic organelles.

An artificial neural network-based hairiness prediction model for worsted wool yarns

This study evaluated the performance of multilayer perceptron (MLP) and multivariate linear regression (MLR) models for predicting the hairiness of worsted-spun wool yarns from various top, yarn and processing parameters. The results indicated that the MLP model predicted yarn hairiness more accurately than the MLR model, and should have wide mill specific applications. On the basis of sensitivity analysis, the factors that affected yarn hairiness significantly included yarn twist, ring size, average fiber length (hauteur), fiber diameter and yarn count, with twist having the greatest impact on yarn hairiness.

Environmental site assessment for abalone ranching on artificial reef

This study investigated the feasibility of ranching the abalone Haliotis rubra (Leach) and Haliotis laevigata (Donovan) on concrete artificial reefs at a site chosen by industry investors on silty bottom off Altona Beach, Port Phillip Bay, Victoria, Australia. The study started from the premise that artificial reef deployment combined with abalone stock enhancement may achieve the dual purpose of habitat and stock improvement. It evaluated the hydrodynamics of the site in relation to artificial reef structural stability and drift algal transport, the development and ecology of the artificial reef assemblage, and abalone enhancement by seeding the reefs with hatchery juvenile abalone and transplanting adult broodstock abalone. Despite a scarcity of reports on culturing abalone on artificial reef, a literature review examined broadly the functions of artificial reefs, particularly in the context of abalone enhancement, and the relationship between artificial reefs, hydrodynamics and ecology. The major finding was that the success of artificial reef projects is highly dependent on the environment of the chosen site, and that preliminary studies are essential to predict their likely success. This consideration alone provided strong justification to undertake this study. The topography at the Altona site was generally flat, with natural patches of low basalt boulder reef, offering low habitat complexity, yet supporting a diverse range of flora and fauna, including a low-density wild stock of H. rubra. Water depth was a mean of 3.3 m at low tide and 4.4 m at high tide. A single, uncomplicated, concrete artificial reef of H-shape design, was tested as abalone habitat. The hydrodynamic analysis confirmed previous studies of Port Phillip Bay, with mild current speeds of mean 0.045 m.s-1, and maximum-modelled wave height (H1/3) of 1.21 m and period (T1/3) of 4.51 sec. Water temperature ranged from 9.9şC during July to 23.8şC during January, with salinity averaging of 35.5 ppt. The site had a low probability of receiving drift algae, necessary as a food source for abalone, because of its geographic location, potentially affecting ranch productivity. Ecological monitoring of the three-year old artificial reef shows complex changes in the flora and fauna over time, particularly in respect of the sessile fauna. Key differences between the artificial reef and a nearby natural reef community were: lower cover of corallines and late colonisers, such as sponges. High levels of sedimentation were recorded at the Altona site. Hatchery juvenile H. laevigata, with mean survival of 15% after two years and a mean annual growth rate of 39 mm, showed the most promise for outplanting. In comparison, for hatchery juvenile H. rubra, mean survival was 9% after three years and mean annual growth rate was 22 mm. No natural recruitment of H. laevigata was recorded on artificial reef despite transplants of adult broodstock on the reefs. Natural recruitment of H. rubra was also low and insufficient to reliably contribute to abalone ranch stock. Having examined the hydrodynamic, ecological and enhancement attributes of the Altona site, the study concluded that the site was marginally viable for abalone ranching, and that an alternative site near Werribee, 20 km further southwest, had superior attributes for growth and survival of abalone.

Hierarchical development of training database for artificial neural network-based damage identification

Though serving as an effective means for damage identification, the capability of an artificial neural network (ANN) for quantitative prediction is substantially dependent on the amount of training data. In virtue of a concept of “Digital Damage Fingerprints” (DDF), a hierarchical approach for the development of training databases was proposed for ANN-based damage identification. With the object of exploiting the capability of ANN to address the key questions: “Is there damage?” and “Where is the damage?”, the amount of training data (damage cases) was increased progressively. Mutuality was established between the quantity of training data and the accuracy of answers to the two questions of interest, and was experimentally validated by identifying the position of actual damage in carbon fibre-reinforced composite laminates. The results demonstrate that such a hierarchical approach is capable of offering prediction as to the presence and location of damage individually, with substantially reduced computational cost and effort in the development of the ANN training database.

Design and degradation modelling through artificial neural networks

Automotive is one of the major manufacturing industries in Australia that requires extensive reliability test for the components used in vehicles. To achieve a shorter time-to-market and a highly reliable product while reducing the amount of physical prototyping, there is a growing need for better understanding on the effect that the design parameters have on the degradation of the product. This paper presents comprehensive descriptions of applying Artificial Neural Network (ANN) to capture the relationships between design and degradation. Consequently, two models of different practical significance are created as the result of the work. The vision of the models is to be used by the testers and designers as a guideline in design evaluation, so that time-consuming and expensive iterations of the product developmental cycle can be reduced substantially. The degradation of the folding force of a mechanical system is used to illustrate our approach.

Identification and classification of animal fibres using artificial neural networks

It has been an important and challenging task to classify and evaluate the contents in wool blends. Quantitative characterisation of animal fibre scale patterns has attracted considerable attention, since it is the major evidence for identification and subsequent classification purpose. Although techniques such as imaging processing and linear demarcation functions have been used to identify unknown fibre type with some success, a more comprehensive approach is required to perform this task. In this paper, a new approach is presented, which employs non-linear demarcation functions by using an artificial neural network (ANN). Based on scale pattern features extracted by using image processing techniques the artificial neural network (ANN) model is to classify mohair and merino fibres. It is observed that the techniques developed in this work are very effective and have the potential to be applied to other animal fibres.

Nutritional related aspects of the artificial propagation and larval rearing of Macquarie perch, Macquaria Australasica (Cuvier, 1830)

Studies the underlying role of nutrition in the lack of response of captive fish to hypophysation. Aspects studied include morphological characteristics, histology of ovaries, proximate analysis, fatty and amino acid profiles of oocytes, muscle, liver and diets of wild and tank-reared fish, egg and larval quality, amino acid composition of eggs and larvae at different developmental stages, larval feeding and hormone treatments.

Managing alternative stable states in constructed wetlands using artificial substrates

Algal blooms are a management concern in shallow water bodies. This project investigated the use of artificial substrates to enhance biofilm growth and shift primary production from the open water to artificial surfaces. This resulted in a shift from algal dominated wetland back to a clear water macrophyte dominated wetland.