Neuroprotectant effects of iso-osmolar D-mannitol to prevent Pacific ciguatoxin-1 induced alterations in neuronal excitability: A comparison with other osmotic agents and free radical scavengers

The basis for the neuroprotectant effect of D-mannitol in reducing the sensory neurological disturbances seen in ciguatera poisoning, is unclear. Pacific ciguatoxin-1 (P-CTX-1), at a concentration 10 nM, caused a statistically significant swelling of rat sensory dorsal root ganglia (DRG) neurons that was reversed by hyperosmolar 50 MM D-mannitol. However, using electron paramagnetic resonance (EPR) spectroscopy, it was found that P-CTX-1 failed to generate hydroxyl free radicals at concentrations of toxin that caused profound effects on neuronal excitability. Whole-cell patch-clamp recordings from DRG neurons revealed that both hyper- and iso-osmolar 50 MM D-mannitol prevented the membrane depolarisation and repetitive firing of action potentials induced by P-CTX-1. In addition, both hyper- and iso-osmolar 50 MM D-mannitol prevented the hyperpolarising shift in steady-state inactivation and the rise in leakage current through tetrodotoxin (TTX)-sensitive Na-v channels, as well as the increased rate of recovery from inactivation of TTX-resistant Nav channels induced by P-CTX-1. D-Mannitol also reduced, but did not prevent, the inhibition of peak TTX-sensitive and TTX-resistant I-Na amplitude by P-CTX-1. Additional experiments using hyper- and isoosmolar D-sorbitol, hyperosmolar sucrose and the free radical scavenging agents Trolox (R) and L-ascorbic acid showed that these agents, unlike D-mannitol, failed to prevent the effects of P-CTX-1 on spike electrogenesis and Na-v channel gating. These selective actions of D-mannitol indicate that it does not act purely as an osmotic agent to reduce swelling of nerves, but involves a more complex action dependent on the Nav channel subtype, possibly to alter or reduce toxin association. (c) 2005 Elsevier Ltd. All rights reserved.

Adaptive management and its role in managing Great Barrier Reef water quality

Adaptive management is the pathway to effective conservation, use and management of Australia’s coastal catchments and waterways. While the concepts of adaptive management are not new, applications involving both assessment and management responses are indeed limited at the national and regional scales. This paper outlines the components of a systematic framework for linking scientific knowledge, existing tools, planning approaches and participatory processes to achieve healthy regional partnerships between community, industry, government agencies and science providers to overcome institutional barriers and uncoordinated monitoring. The framework developed by the Coastal CRC (www.coastal.crc.org.au/amf/amf_index.htm) is hierarchical in the way it displays information to allow associated frameworks to be integrated, and represents a construct in which processes, information, decision tools and outcomes are brought together in a structured and transparent way for adaptive catchment and coastal management. This paper proposes how an adaptive management approach could be used to benefit the implementation of the Reef Water Quality Protection Plan (RWQPP).

Proceedings of the Eighth Australian and New Zealand Intelligent Information Systems Conference

These are the full proceedings of the conference.

A modified particle filter for simultaneous robot localization and landmark tracking in an indoor environment

This paper presents the implementation of a modified particle filter for vision-based simultaneous localization and mapping of an autonomous robot in a structured indoor environment. Through this method, artificial landmarks such as multi-coloured cylinders can be tracked with a camera mounted on the robot, and the position of the robot can be estimated at the same time. Experimental results in simulation and in real environments show that this approach has advantages over the extended Kalman filter with ambiguous data association and various levels of odometric noise.

Performance comparisons between greedy and lagrange algorithms in adaptive MIMO MC-CDMA systems

This paper describes two algorithms for adaptive power and bit allocations in a multiple input multiple output multiple-carrier code division multiple access (MIMO MC-CDMA) system. The first is the greedy algorithm, which has already been presented in the literature. The other one, which is proposed by the authors, is based on the use of the Lagrange multiplier method. The performances of the two algorithms are compared via Monte Carlo simulations. At present stage, the simulations are restricted to a single user MIMO MC-CDMA system, which is equivalent to a MIMO OFDM system. It is assumed that the system operates in a frequency selective fading environment. The transmitter has a partial knowledge of the channel whose properties are measured at the receiver. The use of the two algorithms results in similar system performances. The advantage of the Lagrange algorithm is that is much faster than the greedy algorithm. ©2005 IEEE

UQ RoboRoos: Achieving power and agility in a small size robot

The UQ RoboRoos have been developed to participate in the RoboCup robot soccer small size league over several years. RoboCup 2001 saw a focus on the mechanical design of the RoboRoos, with the introduction of an omni-directional drive system and a high power kicker. The change in mechanical design had implications for the rest of the system particularly navigation and multi-robot planning. In addition, the overhead vision system was upgraded to improve reliability and robustness.

Hippocampal models for simultaneous localisation and mapping on an autonomous robot

To navigate successfully in a novel environment a robot needs to be able to Simultaneously Localize And Map (SLAM) its surroundings. The most successful solutions to this problem so far have involved probabilistic algorithms, but there has been much promising work involving systems based on the workings of part of the rodent brain known as the hippocampus. In this paper we present a biologically plausible system called RatSLAM that uses competitive attractor networks to carry out SLAM in a probabilistic manner. The system can effectively perform parameter self-calibration and SLAM in onedimension. Tests in two dimensional environments revealed the inability of the RatSLAM system to maintain multiple pose hypotheses in the face of ambiguous visual input. These results support recent rat experimentation that suggest current competitive attractor models are not a complete solution to the hippocampal modelling problem.

Representation and learning of visual information for pose recognition

Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.