Selective separation of the major whey proteins using ion exchange membranes

Synthetic microporous membranes with functional groups covalently attached were used to selectively separate beta-lactoglobulin, BSA, and alpha-lactalbumin from rennet whey. The selectivity and membrane performance of strong (quaternary ammonium) and weak (diethylamine) ion-exchange membranes were studied using breakthrough curves, measurement of binding capacity, and protein composition of the elution fraction to determine the binding behavior of each membrane. When the weak and strong anion exchange membranes were saturated with whey, they were both selective primarily for beta-lactoglobulin with less than 1% of the eluate consisting of alpha-lactalbumin or BSA. The binding capacity of a pure alpha-lactoglobulin solution was in excess of 1.5 mg/cm(2) of membrane. This binding capacity was reduced to approximately 1.2 mg/cm(2) when using a rennet whey solution (pH 6.4). This reduction in protein binding capacity can be explained by both the competitive effects of other whey proteins and the effect of ions present in whey. Using binary solution breakthrough curves and rennet whey breakthrough curves, it was shown that alpha-lactalbumin and BSA were displaced from the strong and weak anion exchange membranes by beta-lactoglobulin. Finally, the effect of ionic strength on the binding capacity of individual proteins for each membrane was determined by comparing model protein solutions in milk permeate (pH 6.4) and a 10 mM sodium phosphate buffer (pH 6.4). Binding capacities of beta-lactoglobulin, alpha-lactalbumin, and BSA in milk permeate were reduced by as much as 50%. This reduction in capacity coupled with the low binding capacity of current ion exchange membranes are 2 serious considerations for selectively separating complex and concentrated protein solutions.

Liposomes and skin: From drug delivery to model membranes

The early eighties saw the introduction of liposomes as skin drug delivery systems, initially promoted primarily for localised effects with minimal systemic delivery. Subsequently, a novel ultradeformable vesicular system (termed "Transfersomes" by the inventors) was reported for transdermal delivery with an efficiency similar to subcutaneous injection. Further research illustrated that the mechanisms of liposome action depended on the application regime and the vesicle composition and morphology. Ethical, health and supply problems with human skin have encouraged researchers to use skin models. 'IYaditional models involved polymer membranes and animal tissue, but whilst of value for release studies, such models are not always good mimics for the complex human skin barrier, particularly with respect to the stratum corneal intercellular lipid domains. These lipids have a multiply bilayered organization, a composition and organization somewhat similar to liposomes, Consequently researchers have used vesicles as skin model membranes. Early work first employed phospholipid liposomes and tested their interactions with skin penetration enhancers, typically using thermal analysis and spectroscopic analyses. Another approach probed how incorporation of compounds into liposomes led to the loss of entrapped markers, analogous to "fluidization" of stratum corneum lipids on treatment with a penetration enhancer. Subsequently scientists employed liposomes formulated with skin lipids in these types of studies. Following a brief description of the nature of the skin barrier to transdermal drug delivery and the use of liposomes in drug delivery through skin, this article critically reviews the relevance of using different types of vesicles as a model for human skin in permeation enhancement studies, concentrating primarily on liposomes after briefly surveying older models. The validity of different types of liposome is considered and traditional skin models are compared to vesicular model membranes for their precision and accuracy as skin membrane mimics. (c) 2008 Elsevier B.V. All rights reserved.

Effects of divided attention and speeded responding on implicit and explicit retrieval of artificial grammar knowledge

The artificial grammar (AG) learning literature (see, e.g., Mathews et al., 1989; Reber, 1967) has relied heavily on a single measure of implicitly acquired knowledge. Recent work comparing this measure (string classification) with a more indirect measure in which participants make liking ratings of novel stimuli (e.g., Manza & Bornstein, 1995; Newell & Bright, 2001) has shown that string classification (which we argue can be thought of as an explicit, rather than an implicit, measure of memory) gives rise to more explicit knowledge of the grammatical structure in learning strings and is more resilient to changes in surface features and processing between encoding and retrieval. We report data from two experiments that extend these findings. In Experiment 1, we showed that a divided attention manipulation (at retrieval) interfered with explicit retrieval of AG knowledge but did not interfere with implicit retrieval. In Experiment 2, we showed that forcing participants to respond within a very tight deadline resulted in the same asymmetric interference pattern between the tasks. In both experiments, we also showed that the type of information being retrieved influenced whether interference was observed. The results are discussed in terms of the relatively automatic nature of implicit retrieval and also with respect to the differences between analytic and nonanalytic processing (Whittlesea Price, 2001).

Prediction of Parkinson's disease tremor onset using artificial neural networks

In this paper we present the initial results using an artificial neural network to predict the onset of Parkinson's Disease tremors in a human subject. Data for the network was obtained from implanted deep brain electrodes. A tuned artificial neural network was shown to be able to identify the pattern of the onset tremor from these real time recordings.

Identification of Parkinson’s disease tremor onset using artificial neural networks

In this paper we consider the possibility of using an artificial neural network to accurately identify the onset of Parkinson’s Disease tremors in human subjects. Data for the network is obtained by means of deep brain implantation in the human brain. Results presented have been obtained from a practical study (i.e. real not simulated data) but should be regarded as initial trials to be discussed further. It can be seen that a tuned artificial neural network can act as an extremely effective predictor in these circumstances.

Cross-resistance following artificial selection for increased defense against parasitoids in Drosophila melanogaster

An increase in resistance to one natural enemy may result in no correlated change, a positive correlated change, or a negative correlated change in the ability of the host or prey to resist other natural enemies. The type of specificity is important in understanding the evolutionary response to natural enemies and was studied here in a Drosaphila-parasitoid system. Drosophila melanogaster lines selected for increased larval resistance to the endoparasitoid wasps Asobara tabida or Leptopilina boulardi were exposed to attack by A. tabida, L. boulardi and Leptopilina heterotama at 15 degrees C, 20 degrees C, and 25 degrees C. In general, encapsulation ability increased with temperature, with the exception of the lines selected against L. boulardi, which showed the opposite trend. Lines selected against L, boulardi showed large increases in resistance against all three parasitoid species, and showed similar levels of defense against A. tabida to the lines selected against that parasitoid. In contrast, lines selected against A. tabida showed a large increase in resistance to A. tabida and generally to L. heterotoma, but displayed only a small change in their ability to survive attack by L. boulardi. Such asymmetries in correlated responses to selection for increased resistance to natural enemies may influence host-parasitoid community structure.

Artificial keys for botanical identification using a multilayer perceptron neural network (MLP)

In this paper, practical generation of identification keys for biological taxa using a multilayer perceptron neural network is described. Unlike conventional expert systems, this method does not require an expert for key generation, but is merely based on recordings of observed character states. Like a human taxonomist, its judgement is based on experience, and it is therefore capable of generalized identification of taxa. An initial study involving identification of three species of Iris with greater than 90% confidence is presented here. In addition, the horticulturally significant genus Lithops (Aizoaceae/Mesembryanthemaceae), popular with enthusiasts of succulent plants, is used as a more practical example, because of the difficulty of generation of a conventional key to species, and the existence of a relatively recent monograph. It is demonstrated that such an Artificial Neural Network Key (ANNKEY) can identify more than half (52.9%) of the species in this genus, after training with representative data, even though data for one character is completely missing.

Using self-organising feature maps for the control of artificial organisms

Variations on the standard Kohonen feature map can enable an ordering of the map state space by using only a limited subset of the complete input vector. Also it is possible to employ merely a local adaptation procedure to order the map, rather than having to rely on global variables and objectives. Such variations have been included as part of a hybrid learning system (HLS) which has arisen out of a genetic-based classifier system. In the paper a description of the modified feature map is given, which constitutes the HLSs long term memory, and results in the control of a simple maze running task are presented, thereby demonstrating the value of goal related feedback within the overall network.

Biomimetic triblock copolymer membranes: from aqueous solutions to solid supports

In the present paper, we studied the preparation of biomimetic triblock copolymer (ABA) membranes in aqueous solution and their deposition into solid supports. The self-assembly structures of the ABA in aqueous solution was investigated by using optical microscopy, dynamic light scattering, electron microscopy (EM) and SAXS. Spherical and tubular polymersomes were found at the highest concentrations investigated. The mechanism of deposition on solid supports (mica and glass) was elucidated by using atomic force microscopy (AFM). The deposition results in the formation of a uniform defect-free membrane at suitable polymer concentrations.

Object detecting artificial retina

This paper describes the novel use of agent and cellular neural Hopfield network techniques in the design of a self-contained, object detecting retina. The agents, which are used to detect features within an image, are trained using the Hebbian method which has been modified for the cellular architecture. The success of each agent is communicated with adjacent agents in order to verify the detection of an object. Initial work used the method to process bipolar images. This has now been extended to handle grey scale images. Simulations have demonstrated the success of the method and further work is planned in which the device is to be implemented in hardware.