Carbohydrate-based anti-adhesive inhibition of Vibrio cholerae toxin binding to GM1-OS immobilized into artificial planar lipid membranes

We have studied 'food grade' sialyloligosaccharides (SOS) as anti-adhesive drugs or receptor analogues, since the terminal sialic acid residue has already been shown to contribute significantly to the adhesion and pathogenesis of the Vibrio cholerae toxin (Ctx). GM1-oligosaccharide (GM1-OS) was immobilized into a supporting POPC lipid bilayer onto a surface plasmon resonance (SPR) chip, and the interaction between uninhibited Ctx and GM1-OS-POPC was measured. SOS inhibited 94.7% of the Ctx binding to GM1-OS-POPC at 10 mg/mL. The SOS EC50 value of 5.521 mg/mL is high compared with 0.2811 mu g/mL (182.5 pM or 1.825 x 10(-10) M) for GM1-OS. The commercially available sialyloligosaccharide (SOS) mixture Sunsial E (R) is impure, containing one monosialylated and two disialylated oligosaccharides in the ratio 9.6%. 6.5% and 17.5%, respectively, and 66.4% protein. However, these inexpensive food-grade molecules are derived from egg yolk and could be used to fortify conventional food additives, by way of emulsifiers, sweeteners and/or preservatives. The work further supports our hypothesis that SOS could be a promising natural anti-adhesive glycomimetic against Ctx and prevent subsequent onset of disease. (C) 2009 Elsevier Ltd. 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).

ICT implants: the invasive future of identity?

Is the human body a suitable place for a microchip? Such discussion is no longer hypothetical - in fact in reality it has not been so for some years. Restorative devices such as pacemakers and cochlear implants have become well established, yet these sophisticated devices form notably intimate links between technology and the body. More recent developments in engineering technologies have meant that the integration of silicon with biology is now reaching new levels - with devices which interact directly with the brain. As medical technologies continue to advance, their potential benefits for human enhancement will become increasingly attractive, and so we need to seriously consider where this may take us. In this paper, an attempt is made to demonstrate that, in the medical context, the foundations of more advanced implantable enhancement technologies are already notably progressed, and that they are becoming more science fact than is widely considered. A number of wider moral, ethical and legal issues stem from enhancement applications and it is difficult to foresee the social consequences, the fundamental changes on our very conception of self and the impact on our identity of adoption long term. As a result, it is necessary to acknowledge the possibilities and is timely to have debate to address the wider implications these possibilities may bring.

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.

Extending the human nervous system through Internet implants - Experimentation and impact

It is now possible to directly link the human nervous system to a computer and thence onto the Internet. From an electronic and mental viewpoint this means that the Internet becomes an extension of the human nervous system (and vice versa). Such a connection on a regular or mass basis will have far reaching effects for society. In this article the authors discuss their own practical implant self-experimentation, especially insofar as it relates to extending the human nervous system. Trials involving an intercontinental link up are described. As well as technical aspects of the work, social, moral and ethical issues, as perceived by the authors, are weighed against potential technical gains. The authors also look at technical limitations inherent in the co-evolution of Internet implanted individuals as well as the future distribution of intelligence between human and machine.

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.