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J Theiler

Publications and source records attributed to J Theiler.

15 recordsLinked to original sources
PubMed2001

Using human immunodeficiency virus type 1 sequences to infer historical features of the acquired immune deficiency syndrome epidemic and human immunodeficiency virus evolution.

In earlier work, human immunodeficiency virus type 1 (HIV-1) sequences were analysed to estimate the timing of the ancestral sequence of the main group of HIV-1, the virus that is responsible for the acquired immune deficiency syndrome pandemic, yielding a best estimate of 1931 (95% confidence interval of 1915-1941). That work will be briefly reviewed, outlining how phylogenetic tools were extended to incorporate improved evolutionary models, how the molecular clock model was adapted to incorporate variable periods of latency, and how the approach was validated by correctly estimating the timing of two historically documented dates. The advantages, limitations, and assumptions of the approach will be summarized, with particular consideration of the implications of branch length uncertainty and recombination. We have recently undertaken new phylogenetic analysis of an extremely diverse set of human immunodeficiency virus envelope sequences from the Democratic Republic of the Congo (the DRC, formerly Zaire). This analysis both corroborates and extends the conclusions of our original study. Coalescent methods were used to infer the demographic history of the HIV-1 epidemic in the DRC, and the results suggest an increase in the exponential growth rate of the infected population through time.

Acquired Immunodeficiency Syndrome↗
PubMed2000

Timing the ancestor of the HIV-1 pandemic strains.

HIV-1 sequences were analyzed to estimate the timing of the ancestral sequence of the main group of HIV-1, the strains responsible for the AIDS pandemic. Using parallel supercomputers and assuming a constant rate of evolution, we applied maximum-likelihood phylogenetic methods to unprecedented amounts of data for this calculation. We validated our approach by correctly estimating the timing of two historically documented points. Using a comprehensive full-length envelope sequence alignment, we estimated the date of the last common ancestor of the main group of HIV-1 to be 1931 (1915-41). Analysis of a gag gene alignment, subregions of envelope including additional sequences, and a method that relaxed the assumption of a strict molecular clock also supported these results.

Acquired Immunodeficiency Syndrome↗
PubMed1998

Low doses of ethanol reduce evidence for nonlinear structure in brain activity.

Recent theories of the effects of ethanol on the brain have focused on its direct actions on neuronal membrane proteins. However, neuromolecular mechanisms whereby ethanol produces its CNS effects in low doses typically used by social drinkers (e.g., 2-3 drinks, 10-25 mM, 0.05-0.125 gm/dl) remain less well understood. We propose the hypothesis that ethanol may act by introducing a level of randomness or "noise" in brain electrical activity. We investigated the hypothesis by applying a battery of tests originally developed for nonlinear time series analysis and chaos theory to EEG data collected from 32 men who had participated in an ethanol/placebo challenge protocol. Because nonlinearity is a prerequisite for chaos and because we can detect nonlinearity more reliably than chaos, we concentrated on a series of measures that quantitated different aspects of nonlinearity. For each of these measures the method of surrogate data was used to assess the significance of evidence for nonlinear structure. Significant nonlinear structure was found in the EEG as evidenced by the measures of time asymmetry, determinism, and redundancy. In addition, the evidence for nonlinear structure in the placebo condition was found to be significantly greater than that for ethanol. Nonlinear measures, but not spectral measures, were found to correlate with a subject's overall feeling of intoxication. These findings are consistent with the notion that ethanol may act by introducing a level of randomness in neuronal processing as assessed by EEG nonlinear structure.

Adolescent↗
PubMed1996

Re-examination of the evidence for low-dimensional, nonlinear structure in the human electroencephalogram.

We have re-examined single channel EEG data obtained from normal human subjects. In the original analysis, calculation of the correlation dimension with the Grassberger-Procaccia algorithm produced results consistent with and interpretation of low-dimensional behavior. The re-examination suggests that the previous indication of low-dimensional structure was an artifact of autocorrelation in the oversampled signal. Calculations with a variant of the Grassberger-Procaccia algorithm modified to be less sensitive to autocorrelations, and comparison with linear gaussian surrogate data, indicate that these data may be more appropriately modeled by linearly filtered noise. Discriminant analysis further indicates that the correlation dimension is a poor discriminator for distinguishing between EEGs recorded at rest and during periods of cognitive activity. It remains possible that the application of other nonlinear measures or the examination of multichannel EEG data may resolve structures not found in these calculations.

Adult↗
PubMedDate not supplied

Two tools to test time series data for evidence of chaos and/or nonlinearity.

Two computer programs are described for evaluating the evidence for chaos and nonlinearity in time series data. "bx" is an efficient algorithm for computing the correlation integral (from which correlation dimension can be estimated); and "surrogat" is a Fourier-transform-based algorithm for generating surrogate data consistent with a null hypothesis that the data arise as a result of a linear stochastic process.

Algorithms↗