Biomedical subjects

D A Stacey

Publications and source records attributed to D A Stacey.

4 recordsLinked to original sources

PubMed2002

Influence of temperature on pea aphid Acyrthosiphon pisum (Hemiptera: Aphididae) resistance to natural enemy attack.

The ability to resist or avoid natural enemy attack is a critically important insect life history trait, yet little is understood of how these traits may be affected by temperature. This study investigated how different genotypes of the pea aphid Acyrthosiphon pisum Harris, a pest of leguminous crops, varied in resistance to three different natural enemies (a fungal pathogen, two species of parasitoid wasp and a coccinellid beetle), and whether expression of resistance was influenced by temperature. Substantial clonal variation in resistance to the three natural enemies was found. Temperature influenced the number of aphids succumbing to the fungal pathogen Erynia neoaphidis Remaudière & Hennebert, with resistance increasing at higher temperatures (18 vs. 28 degrees C). A temperature difference of 5 degrees C (18 vs. 23 degrees C) did not affect the ability of A. pisum to resist attack by the parasitoids Aphidius ervi Haliday and A. eadyi Starý, González & Hall. Escape behaviour from foraging coccinellid beetles (Hippodamia convergens Guerin-Meneville) was not directly influenced by aphid clone or temperature (16 vs. 21 degrees C). However, there were significant interactions between clone and temperature (while most clones did not respond to temperature, one was less likely to escape at 16 degrees C), and between aphid clone and ladybird presence (some clones showed greater changes in escape behaviour in response to the presence of foraging coccinellids than others). Therefore, while larger temperature differences may alter interactions between Acyrthosiphon pisum and an entomopathogen, there is little evidence to suggest that smaller changes in temperature will alter pea aphid-natural enemy interactions.

Animals↗

PubMed2001

Artificial neural network model for the generation of muscle activation patterns for human locomotion.

Skilled locomotor behaviour requires information from various levels within the central nervous system (CNS). Mathematical models have permitted researchers to simulate various mechanisms in order to understand the organization of the locomotor control system. While it is difficult to adequately characterize the numerous inputs to the locomotor control system, an alternative strategy may be to use a kinematic movement plan to represent the complex inputs to the locomotor control system based on the possibility that the CNS may plan movements at a kinematic level. We propose the use of artificial neural network (ANN) models to represent the transformation of a kinematic plan into the necessary motor patterns. Essentially, kinematic representation of the actual limb movement was used as the input to an ANN model which generated the EMG activity of 8 muscles of the lower limb and trunk. Data from a wide variety of gait conditions was necessary to develop a robust model that could accommodate various environmental conditions encountered during everyday activity. A total of 120 walking strides representing normal walking and ten conditions where the normal gait was modified in terms of cadence, stride length, stance width or required foot clearance. The final network was assessed on its ability to predict the EMG activity on individual walking trials as well as its ability to represent the general activation pattern of a particular gait condition. The predicted EMG patterns closely matched those recorded experimentally, exhibiting the appropriate magnitude and temporal phasing required for each modification. Only 2 of the 96 muscle/gait conditions had RMS errors above 0.10, only 5 muscle/gait conditions exhibited correlations below 0.80 (most were above 0.90) and only 25 muscle/gait conditions deviated outside the normal range of muscle activity for more than 25% of the gait cycle. These results indicate the ability of single network ANNs to represent the transformation between a kinematic movement plan and the necessary muscle activations for normal steady state locomotion but they were also able to generate muscle activation patterns for conditions requiring changes in walking speed, foot placement and foot clearance. The abilities of this type of network have implications towards both the fundamental understanding of the control of locomotion and practical realizations of artificial control systems for use in rehabilitation medicine.

Adult↗

PubMed1998

Simple artificial neural network models can generate basic muscle activity patterns for human locomotion at different speeds.

A neural network model has been developed to represent the shaping function of a central pattern generator (CPG) for human locomotion. The model was based on cadence and electromyographic data obtained from a single human subject who walked on a treadmill. The only input to the model was the fundamental timing of the gait cycle (stride rate) in the form of sine and cosine waveforms whose period was equal to the stride duration. These simple signals were then shaped into the respective muscle activation patterns of eight muscles of the lower limb and trunk. A network with a relatively small number of hidden units trained with back-propagation was able to produce an excellent representation of both the amplitude and timing characteristics of the EMGs over a range of walking speeds. The results are further discussed with respect to the dependence of some muscles upon sensory feedback and other inputs not explicitly presented to the model.

Algorithms↗

PubMed1995

Modelling the time-keeping function of the central pattern generator for locomotion using artificial sequential neural network.

The paper investigates the ability of a sequential neural network to model the time-keeping function (fundamental frequency oscillation) of a central pattern generator for locomotion. The intention is not to strive for biological fidelity, but rather to ensure that the network obeys the organisational and operational principles of central pattern generators developed through empirical research. The timing function serves to produce the underlying locomotor rhythm which can be transformed by nonlinear static shaping functions to construct the necessary locomotor activation patterns. Using two levels of tonic activations in the form of a step increase, a network consisting of nine processing units was successfully trained to output both sine and cosine waveforms, whose frequencies were modified in response to the level of input activation. The network's ability to generalise was demonstrated by appropriately scaling the frequency of oscillation in response to a range of input amplitudes, both within and outside the values on which it was trained. A notable and fortunate result was the model's failure to oscillate in the absence of input activation, which is a necessary property of the CPG model. It was further demonstrated that the oscillation frequency of the output waveforms exhibited both a high temporal stability and a very low sensitivity to input noise. The results indicate that the sequential neural network is a suitable candidate to model the time-keeping functions of the central pattern generator for locomotion.

Humans↗