Biomedical subjects

Edward L Ionides

Publications and source records attributed to Edward L Ionides.

3 recordsLinked to original sources

PubMed2006

Patterns of influenza-associated mortality among US elderly by geographic region and virus subtype, 1968-1998.

The regular seasonality of influenza in temperate countries is recognized, but regional differences in patterns of influenza-related mortality are poorly understood. Identifying patterns could improve epidemic prediction and prevention. The authors analyzed the monthly percentage of deaths attributable to pneumonia and influenza among people aged 65 or more years in the contiguous United States, 1968-1998. The local Moran's I test for spatial autocorrelation and correlograms assessing space-time synchrony within each influenza season were applied to detect and to characterize mortality patterns. Western US regions experienced epidemics of greater magnitude than did eastern regions. Positive spatial autocorrelation (two-sided p = 0.001) revealed the similarity in influenza mortality of neighboring states, with several western states forming a focus of high mortality. In transmission seasons dominated by virus subtype A(H3N2), mortality was correlated at a high and consistent level across the United States (mean correlation = 0.56, standard deviation = 0.134). However, when subtype A(H1N1) or type B dominated, the average synchrony was lower (mean correlation = 0.23, standard deviation = 0.058). These novel analyses suggest that causes of spatial heterogeneity (e.g., large-scale environmental drivers and population movement) have impacted influenza-associated mortality.

Aged↗

PubMed2005

Naive coadaptive cortical control.

The ability to control a prosthetic device directly from the neocortex has been demonstrated in rats, monkeys and humans. Here we investigate whether neural control can be accomplished in situations where (1) subjects have not received prior motor training to control the device (naive user) and (2) the neural encoding of movement parameters in the cortex is unknown to the prosthetic device (naive controller). By adopting a decoding strategy that identifies and focuses on units whose firing rate properties are best suited for control, we show that naive subjects mutually adapt to learn control of a neural prosthetic system. Six untrained Long-Evans rats, implanted with silicon micro-electrodes in the motor cortex, learned cortical control of an auditory device without prior motor characterization of the recorded neural ensemble. Single- and multi-unit activities were decoded using a Kalman filter to represent an audio "cursor" (90 ms tone pips ranging from 250 Hz to 16 kHz) which subjects controlled to match a given target frequency. After each trial, a novel adaptive algorithm trained the decoding filter based on correlations of the firing patterns with expected cursor movement. Each behavioral session consisted of 100 trials and began with randomized decoding weights. Within 7 +/- 1.4 (mean +/- SD) sessions, all subjects were able to significantly score above chance (P < 0.05, randomization method) in a fixed target paradigm. Training lasted 24 sessions in which both the behavioral performance and signal to noise ratio of the peri-event histograms increased significantly (P < 0.01, ANOVA). Two rats continued training on a more complex task using a bilateral, two-target control paradigm. Both subjects were able to significantly discriminate the target tones (P < 0.05, Z-test), while one subject demonstrated control above chance (P < 0.05, Z-test) after 12 sessions and continued improvement with many sessions achieving over 90% correct targets. Dynamic analysis of binary trial responses indicated that early learning for this subject occurred during session 6. This study demonstrates that subjects can learn to generate neural control signals that are well suited for use with external devices without prior experience or training.

Action Potentials↗

PubMed2003

Stochastic models for cell motion and taxis.

Certain biological experiments investigating cell motion result in time lapse video microscopy data which may be modeled using stochastic differential equations. These models suggest statistics for quantifying experimental results and testing relevant hypotheses, and carry implications for the qualitative behavior of cells and for underlying biophysical mechanisms. Directional cell motion in response to a stimulus, termed taxis, has previously been modeled at a phenomenological level using the Keller-Segel diffusion equation. The Keller-Segel model cannot distinguish certain modes of taxis, and this motivates the introduction of a richer class of models which is nevertheless still amenable to statistical analysis. A state space model formulation is used to link models proposed for cell velocity to observed data. Sequential Monte Carlo methods enable parameter estimation via maximum likelihood for a range of applicable models. One particular experimental situation, involving the effect of an electric field on cell behavior, is considered in detail. In this case, an Ornstein- Uhlenbeck model for cell velocity is found to compare favorably with a nonlinear diffusion model.

Algorithms↗