Newcastle, NSW, Australia

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School of Education
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School of Psychology
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School of Nursing and Midwifery
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    ABSTRACT: Superficial dorsal horn (SDH) neurons process nociceptive information and their excitability is partly determined by the properties of voltage-gated sodium channels. Recently, we showed the excitability and action potential properties of mouse SDH neurons change markedly during early postnatal development. Here we compare sodium currents generated in neonate (P0-5) and young adult (≥P21) SDH neurons. Whole cell recordings were obtained from lumbar SDH neurons in transverse spinal cord slices (CsF internal, 32°C). Fast activating and inactivating TTX-sensitive inward currents were evoked by depolarization from a holding potential of −100 mV. Poorly clamped currents, based on a deflection in the IV relationship at potentials between −60 and −50 mV, were not accepted for analysis. Current density and decay time increased significantly between the first and third weeks of postnatal development, whereas time to peak was similar at both ages. This was accompanied by more subtle changes in activation range and steady state inactivation. Recovery from inactivation was slower and TTX-sensitivity was reduced in young adult neurons. Our study suggests sodium channel expression changes markedly during early postnatal development in mouse SDH neurons. The methods employed in this study can now be applied to future investigations of spinal cord sodium channel plasticity in murine pain models.
    Molecular Pain 12/2015; 11(1). DOI:10.1186/s12990-015-0014-5
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    ANZ Journal of Surgery 04/2015; 85(4):202-3. DOI:10.1111/ans.13002
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    ABSTRACT: Herein we present an algorithm for controlling LTI processes using an adaptive sampling interval where the controller at every sampling instant not only computes the new control command but also decides the time interval to the next sample. The approach relies on MPC where the cost function depends on the control performance as well as the cost for sampling. The paper presents a method for synthesizing such a predictive controller and gives explicit conditions for when it is stabilizing. Further it is shown that the optimization problem may be solved off-line and that the controller may be implemented as a lookup table of state feedback gains. The paper is concluded with a numerical example.
    IEEE Transactions on Control Systems Technology 02/2015; DOI:10.1109/TCST.2015.2404308


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    University Drive, 2308, Newcastle, NSW, Australia
  • Head of Institution
    Caroline McMillen
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Top publications last week by downloads

Automation in Construction 05/2009; DOI:10.1016/j.autcon.2008.10.003
Nurse education today 11/2009; 30(6):515-20. DOI:10.1016/j.nedt.2009.10.020

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