Two-pulse rapid remote surface contamination measurement.

This project demonstrated the feasibility of a 'pump-probe' optical detection method for standoff sensing of chemicals on surfaces. Such a measurement uses two optical pulses - one to remove the analyte (or a fragment of it) from the surface and the second to sense the removed material. As a particular example, this project targeted photofragmentation laser-induced fluorescence (PF-LIF) to detect of surface deposits of low-volatility chemical warfare agents (LVAs). Feasibility was demonstrated for four agent surrogates on eight realistic surfaces. Its sensitivity was established for measurements on concrete and aluminum. Extrapolations were made to demonstrate relevance to the needs of outside users. Several aspects of the surface PF-LIF physical mechanism were investigated and compared to that of vapor-phase measurements. The use of PF-LIF as a rapid screening tool to 'cue' more specific sensors was recommended. Its sensitivity was compared to that of Raman spectroscopy, which is both a potential 'confirmer' of PF-LIF 'hits' and is also a competing screening technology

Applied Plasma Research

Contains research objectives, summary of research and reports on four research projects.National Science Foundation (Grant GK-28282X1)National Science Foundation (Grant GK-33843

Plasma Dynamics

Contains reports on ten research projects split into two sections.National Science Foundation (Grant ENG77-00340)U.S. Department of Energy (Contract EY-76-S-02-2766)U.S. Air Force - Office of Scientific Research (Grant AFOSR-77-3143)U.S. Department of Energy (Contract ET-78-C-01-3019)U.S. Department of Energy (Contract ET-78-S-02-4681)U.S. Department of Energy (Contract ET-78-S-02-4682)U.S. Department of Energy (Grant EG-77-G-01-4107)U.S. Department of Energy (Contract ET-78-S-02-4714)U.S. Department of Energy (Contract ET-78-S-02-4886)U.S. Department of Energy (Contract ET-78-S-02-4690

Plasma Dynamics

Contains reports on ten research projects divided into two sections.National Science Foundation (Grant ENG79-07047)U.S. Air Force - Office of Scientific Research (Grant AFOSR-77-3143)U.S. Department of Energy (Contract DE-ACO2-78ET51013)U.S. Department of Energy (Contract DE-ASO2-78ET53073.AO02)U.S. Department of Energy (Contract ET-78-S-02-4682)U.S. Department of Energy (Contract DE-AS02-78ET53074)U.S. Department of Energy (Contract DE-ASO2-78ET53050)U.S. Department of Energy (Contract DE-AS02-78ET51002)U.S. Department of Energy (Contract DE-ASO2-78ET53076

Plasma Dynamics

Contains research objectives and summary of research on eighteen research projects split into seven sections and reports on four research projects.U.S. Atomic Energy Commission (Contract AT(l1-1)-3070)National Science Foundation (Grant GK-37979X1

Plasma Dynamics

Contains research objectives and summary of research on twenty-one projects split into three sections, with four sub-sections in the second section and reports on twelve research projects.National Science Foundation (Grant ENG75-06242)U.S. Energy Research and Development Administration (Contract E(11-1)-2766)U.S. Energy Research and Development Agency (Contract E(11-1)-3070)U.S. Energy Research and Development Administration (Contract E(11-1)-3070)Research Laboratory of Electronics, M.I.T. Industrial Fellowshi

Plasma Dynamics

Contains reports on seventeen research projects split into two sections.National Science Foundation (Grant ENG77-00340)U. S. Energy Research and Development Administration (Contract E(11-1)-2766)U. S. Energy Research and Development Administration (Contract EY-76-S-02-2766)U. S. Air Force - Office of Scientific Research (Grant AFOSR-77-3143)U. S. Department of Energy (Grant EG-77-G-01-4107

Plasma Dynamics

Contains research objectives and summary of research on nineteen research projects split into five sections.National Science Foundation (Grant ENG75-06242-A01)U.S. Energy Research and Development Administration (Contract E(11-1)-2766)U.S. Air Force - Office of Scientific Research (Grant AFOSR-77-3143)U.S. Energy Research and Development Administration (Contract EY-76-C2-02-3070.*000

High-Resolution Mapping of Gene Expression Using Association in an Outbred Mouse Stock

Quantitative trait locus (QTL) analysis is a powerful tool for mapping genes for complex traits in mice, but its utility is limited by poor resolution. A promising mapping approach is association analysis in outbred stocks or different inbred strains. As a proof of concept for the association approach, we applied whole-genome association analysis to hepatic gene expression traits in an outbred mouse population, the MF1 stock, and replicated expression QTL (eQTL) identified in previous studies of F2 intercross mice. We found that the mapping resolution of these eQTL was significantly greater in the outbred population. Through an example, we also showed how this precise mapping can be used to resolve previously identified loci (in intercross studies), which affect many different transcript levels (known as eQTL “hotspots”), into distinct regions. Our results also highlight the importance of correcting for population structure in whole-genome association studies in the outbred stock

Quantitative Models of the Mechanisms That Control Genome-Wide Patterns of Transcription Factor Binding during Early Drosophila Development

Transcription factors that drive complex patterns of gene expression during animal development bind to thousands of genomic regions, with quantitative differences in binding across bound regions mediating their activity. While we now have tools to characterize the DNA affinities of these proteins and to precisely measure their genome-wide distribution in vivo, our understanding of the forces that determine where, when, and to what extent they bind remains primitive. Here we use a thermodynamic model of transcription factor binding to evaluate the contribution of different biophysical forces to the binding of five regulators of early embryonic anterior-posterior patterning in Drosophila melanogaster. Predictions based on DNA sequence and in vitro protein-DNA affinities alone achieve a correlation of ∼0.4 with experimental measurements of in vivo binding. Incorporating cooperativity and competition among the five factors, and accounting for spatial patterning by modeling binding in every nucleus independently, had little effect on prediction accuracy. A major source of error was the prediction of binding events that do not occur in vivo, which we hypothesized reflected reduced accessibility of chromatin. To test this, we incorporated experimental measurements of genome-wide DNA accessibility into our model, effectively restricting predicted binding to regions of open chromatin. This dramatically improved our predictions to a correlation of 0.6–0.9 for various factors across known target genes. Finally, we used our model to quantify the roles of DNA sequence, accessibility, and binding competition and cooperativity. Our results show that, in regions of open chromatin, binding can be predicted almost exclusively by the sequence specificity of individual factors, with a minimal role for protein interactions. We suggest that a combination of experimentally determined chromatin accessibility data and simple computational models of transcription factor binding may be used to predict the binding landscape of any animal transcription factor with significant precision