Genomic variation of HIV-1 and its effects on drug resistance and molecular epidemiology analyses

In the first part of this study human immunodeficiency virus type 1 (HIV-1) proviral DNA sequences derived from 201 clones of the C2-V3 env region and the first exon of the tat gene were obtained from six MV-1 infected heterosexual couples. These molecular data were used to confirm the epidemiological relationships. The ability of the molecular data to draw such conclusions was also tested with multiple phylogenetic analyses. The tat region was much more useful in establishing epidemiological relationships than the commonly used C2-V3.^ Subsequently, using nucleotide sequences from the first exon of the Tat gene, we tested the hypothesis that a Florida dentist (a common source) infected five of his patients in the course of dental procedures, against the null hypothesis that the dentist and each individual of the dental group independently acquired the virus within the local community. Multiple phylogenetic analyses demonstrated that the sequences of the five patients were significantly more related to each other than to sequences of the controls. Our results using Tat sequences, combined with envelope sequence data, strongly support a common phylogenetic epidemiological relationship among these five patients.^ A third study is presented, which deals with the effects of genomic variations in drug resistance. HIV-1 reverse transcriptase (RT) mutations were detected in DNA from peripheral blood mononuclear cells from 11 of 12 HIV-infected children after 11-20 months of zidovudine monotherapy. The codon 41/215 mutant combination was associated with general decline in health status. Patients developing the codon 70 mutation tended to have a better health status. ^

An autoregressive conditional filtering process to remove intraday seasonal volatility and its application to testing the noisy rational expectations model

We develop a new autoregressive conditional process to capture both the changes and the persistency of the intraday seasonal (U-shape) pattern of volatility in essay 1. Unlike other procedures, this approach allows for the intraday volatility pattern to change over time without the filtering process injecting a spurious pattern of noise into the filtered series. We show that prior deterministic filtering procedures are special cases of the autoregressive conditional filtering process presented here. Lagrange multiplier tests prove that the stochastic seasonal variance component is statistically significant. Specification tests using the correlogram and cross-spectral analyses prove the reliability of the autoregressive conditional filtering process. In essay 2 we develop a new methodology to decompose return variance in order to examine the informativeness embedded in the return series. The variance is decomposed into the information arrival component and the noise factor component. This decomposition methodology differs from previous studies in that both the informational variance and the noise variance are time-varying. Furthermore, the covariance of the informational component and the noisy component is no longer restricted to be zero. The resultant measure of price informativeness is defined as the informational variance divided by the total variance of the returns. The noisy rational expectations model predicts that uninformed traders react to price changes more than informed traders, since uninformed traders cannot distinguish between price changes caused by information arrivals and price changes caused by noise. This hypothesis is tested in essay 3 using intraday data with the intraday seasonal volatility component removed, as based on the procedure in the first essay. The resultant seasonally adjusted variance series is decomposed into components caused by unexpected information arrivals and by noise in order to examine informativeness.

A model of the hydrothermal system at Casa Diablo in Long Valley, California, based on resistivity profiles and soil mercury analyses

A description and model of the near-surface hydrothermal system at Casa Diablo, with its implications for the larger-scale hydrothermal system of Long Valley, California, is presented. The data include resistivity profiles with penetrations to three different depth ranges, and analyses of inorganic mercury concentrations in 144 soil samples taken over a 1.3 by 1.7 km area. Analyses of the data together with the mapping of active surface hydrothermal features (fumaroles, mudpots, etc.), has revealed that the relationship between the hydrothermal system, surface hydrothermal activity, and mercury anomalies is strongly controlled by faults and topography. There are, however, more subtle factors responsible for the location of many active and anomalous zones such as fractures, zones of high permeability, and interactions between hydrothermal and cooler groundwater. In addition, the near-surface location of the upwelling from the deep hydrothermal reservoir, which supplies the geothermal power plants at Casa Diablo and the numerous hot pools in the caldera with hydrothermal water, has been detected. The data indicate that after upwelling the hydrothermal water flows eastward at shallow depth for at least 2 km and probably continues another 10 km to the east, all the way to Lake Crowley.

Development of microsatellite markers in Cannabis Sativa for fingerprinting and genetic relatedness analyses

Microsatellite markers were developed for Cannabis sativa L. (marijuana) to estimate the level of polymorphism, usefulness for DNA typing (genotype identification), and to measure the genetic relationships between the different plants. Twelve different oligonucleotide probes were used to screen an enriched microsatellite library of Cannabis sativa in which 49% of the clones contained microsatellite sequences. Characterization of microsatellite loci in Cannabis revealed that GA/CT was the most abundant class of isolated microsatellites representing 50% overall. Eleven polymorphic SSR markers were developed, derived from dinucleotide motifs and eight from trinucleotide motifs. A total of 52 alleles were detected averaging 4.7 alleles/locus. The expected heterozygosity of the eleven loci ranged between 0.368 and 0.710 and the common probability of identical genotypes was 1.8 x 107. The loci identified 27 unique profiles of the 41 Cannabis samples. The eleven microsatellite markers developed in this study were found to be useful for DNA fingerprinting and for assessing genetic relationships in Cannabis.

An Autoregressive Conditional Filtering Process to remove Intraday Seasonal Volatility and its Application to Testing the Noisy Rational Expectations Model

We develop a new autoregressive conditional process to capture both the changes and the persistency of the intraday seasonal (U-shape) pattern of volatility in essay 1. Unlike other procedures, this approach allows for the intraday volatility pattern to change over time without the filtering process injecting a spurious pattern of noise into the filtered series. We show that prior deterministic filtering procedures are special cases of the autoregressive conditional filtering process presented here. Lagrange multiplier tests prove that the stochastic seasonal variance component is statistically significant. Specification tests using the correlogram and cross-spectral analyses prove the reliability of the autoregressive conditional filtering process. In essay 2 we develop a new methodology to decompose return variance in order to examine the informativeness embedded in the return series. The variance is decomposed into the information arrival component and the noise factor component. This decomposition methodology differs from previous studies in that both the informational variance and the noise variance are time-varying. Furthermore, the covariance of the informational component and the noisy component is no longer restricted to be zero. The resultant measure of price informativeness is defined as the informational variance divided by the total variance of the returns. The noisy rational expectations model predicts that uninformed traders react to price changes more than informed traders, since uninformed traders cannot distinguish between price changes caused by information arrivals and price changes caused by noise. This hypothesis is tested in essay 3 using intraday data with the intraday seasonal volatility component removed, as based on the procedure in the first essay. The resultant seasonally adjusted variance series is decomposed into components caused by unexpected information arrivals and by noise in order to examine informativeness.