On the dual structure of the auditory brainstem response in dogs

Objective: To use the over-complete discrete wavelet transform (OCDWT) to further examine the dual structure of auditory brainstem response (ABR) in the dog. Methods: ABR waveforms recorded from 20 adult dogs at supra-threshold (90 and 70 dBnHL) and threshold (0-15 dBSL) levels were decomposed using a six level OCDWT and reconstructed at individual scales (frequency ranges) A6 (0-391 Hz), D6 (391-781 Hz), and D5 (781-1563 Hz). Results: At supra-threshold stimulus levels, the A6 scale (0-391 Hz) showed a large amplitude waveform with its prominent wave corresponding in latency with ABR waves II/III; the D6 scale (391-781 Hz) showed a small amplitude waveform with its first four waves corresponding in latency to ABR waves I, II/III, V, and VI; and the D5 scale (781-1563 Hz) showed a large amplitude, multiple peaked waveform with its first six waves corresponding in latency to ABR waves I, II, III, IV, V, and VI. At threshold stimulus levels (0-15 dBSL), the A6 scale (0-391 Hz) continued to show a relatively large amplitude waveform, but both the D6 and D5 scales (391781 and 781-1563 Hz, respectively) now showed relatively small amplitude waveforms. Conclusions: A dual structure exists within the ABR of the dog, but its relative structure changes with stimulus level. Significance: The ABR in the dog differs from that in the human both in the relative contributions made by its different frequency components, and the way these components change with stimulus level. (c) 2006 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Biodegradation of the cyanobacterial toxin microcystin LR in natural water and biologically active slow sand filters

A bacterium (MJ-PV) previously demonstrated to degrade the cyanobacterial toxin microcystin LR, was investigated for bioremediation applications in natural water microcosms and biologically active slow sand filters. Enhanced degradation of microcystin LR was observed with inoculated (1 x 10(6) cell/mL) treatments of river water dosed with microcystin LR (> 80% degradation within 2 days) compared to uninoculated controls. Inoculation of MJ-PV at lower concentrations (1 x 10(2)-1 x 10(5)cells/mL) also demonstrated enhanced microcystin LR degradation over control treatments. Polymerase chain reactions (PCR) specifically targeting amplification of 16S rDNA of MJ-PV and the gene responsible for initial degradation of microcystin LR (mlrA) were successfully applied to monitor the presence of the bacterium in experimental trials. No amplified products indicative of an endemic MJ-PV population were observed in uninoculated treatments indicating other bacterial strains were active in degradation of microcystin LR, Pilot scale biologically active slow sand filters demonstrated degradation of microcystin LR irrespective of MJ-PV bacterial inoculation. PCR analysis detected the MJ-PV population at all locations within the sand filters where microcystin degradation was measured. Despite not observing enhanced degradation of microcystin LR in inoculated columns compared to uninoculated column, these studies demonstrate the effectiveness of a low-technology water treatment system like biologically active slow sand filters for removal of microcystins from reticulated water supplies. Crown Copyright (c) 2006 Published by Elsevier Ltd. All rights reserved.

Short-term electricity price forecasting using wavelet and SVM techniques

In deregulated electricity market, modeling and forecasting the spot price present a number of challenges. By applying wavelet and support vector machine techniques, a new time series model for short term electricity price forecasting has been developed in this paper. The model employs both historical price and other important information, such as load capacity and weather (temperature), to forecast the price of one or more time steps ahead. The developed model has been evaluated with the actual data from Australian National Electricity Market. The simulation results demonstrated that the forecast model is capable of forecasting the electricity price with a reasonable forecasting accuracy.