VALIDATION OF A 'DISPLACEMENT TOMOGRAPHY' INVERSION METHOD FOR MODELING SHEET INTRUSIONS

The study of volcano deformation data can provide information on magma processes and help assess the potential for future eruptions. In employing inverse deformation modeling on these data, we attempt to characterize the geometry, location and volume/pressure change of a deformation source. Techniques currently used to model sheet intrusions (e.g., dikes and sills) often require significant a priori assumptions about source geometry and can require testing a large number of parameters. Moreover, surface deformations are a non-linear function of the source geometry and location. This requires the use of Monte Carlo inversion techniques which leads to long computation times. Recently, ‘displacement tomography’ models have been used to characterize magma reservoirs by inverting source deformation data for volume changes using a grid of point sources in the subsurface. The computations involved in these models are less intensive as no assumptions are made on the source geometry and location, and the relationship between the point sources and the surface deformation is linear. In this project, seeking a less computationally intensive technique for fracture sources, we tested if this displacement tomography method for reservoirs could be used for sheet intrusions. We began by simulating the opening of three synthetic dikes of known geometry and location using an established deformation model for fracture sources. We then sought to reproduce the displacements and volume changes undergone by the fractures using the sources employed in the tomography methodology. Results of this validation indicate the volumetric point sources are not appropriate for locating fracture sources, however they may provide useful qualitative information on volume changes occurring in the surrounding rock, and therefore indirectly indicate the source location.

NON-CHROMATOGRAPHIC PURIFICATION OF SYNTHETIC BIO-OLIGOMERS

Synthetic oligonucleotides and peptides have found wide applications in industry and academic research labs. There are ~60 peptide drugs on the market and over 500 under development. The global annual sale of peptide drugs in 2010 was estimated to be $13 billion. There are three oligonucleotide-based drugs on market; among them, the FDA newly approved Kynamro was predicted to have a $100 million annual sale. The annual sale of oligonucleotides to academic labs was estimated to be $700 million. Both bio-oligomers are mostly synthesized on automated synthesizers using solid phase synthesis technology, in which nucleoside or amino acid monomers are added sequentially until the desired full-length sequence is reached. The additions cannot be complete, which generates truncated undesired failure sequences. For almost all applications, these impurities must be removed. The most widely used method is HPLC. However, the method is slow, expensive, labor-intensive, not amendable for automation, difficult to scale up, and unsuitable for high throughput purification. It needs large capital investment, and consumes large volumes of harmful solvents. The purification costs are estimated to be more than 50% of total production costs. Other methods for bio-oligomer purification also have drawbacks, and are less favored than HPLC for most applications. To overcome the problems of known biopolymer purification technologies, we have developed two non-chromatographic purification methods. They are (1) catching failure sequences by polymerization, and (2) catching full-length sequences by polymerization. In the first method, a polymerizable group is attached to the failure sequences of the bio-oligomers during automated synthesis; purification is achieved by simply polymerizing the failure sequences into an insoluble gel and extracting full-length sequences. In the second method, a polymerizable group is attached to the full-length sequences, which are then incorporated into a polymer; impurities are removed by washing, and pure product is cleaved from polymer. These methods do not need chromatography, and all drawbacks of HPLC no longer exist. Using them, purification is achieved by simple manipulations such as shaking and extraction. Therefore, they are suitable for large scale purification of oligonucleotide and peptide drugs, and also ideal for high throughput purification, which currently has a high demand for research projects involving total gene synthesis. The dissertation will present the details about the development of the techniques. Chapter 1 will make an introduction to oligodeoxynucleotides (ODNs), their synthesis and purification. Chapter 2 will describe the detailed studies of using the catching failure sequences by polymerization method to purify ODNs. Chapter 3 will describe the further optimization of the catching failure sequences by polymerization ODN purification technology to the level of practical use. Chapter 4 will present using the catching full-length sequence by polymerization method for ODN purification using acid-cleavable linker. Chapter 5 will make an introduction to peptides, their synthesis and purification. Chapter 6 will describe the studies using the catching full-length sequence by polymerization method for peptide purification.

APPLICATION OF AN IMPUTATION METHOD FOR GEOSPATIAL INVENTORY OF FOREST STRUCTURAL ATTRIBUTES ACROSS MULTIPLE SPATIAL SCALES IN THE LAKE STATES, U.S.A.

Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable success in augmenting limited sample plot data to generate stand- and landscape-level spatial predictions of forest inventory attributes. Further enhancement of forest inventory approaches that couple field measurements with cutting edge remotely sensed and geospatial datasets are essential to sustainable forest management. We evaluated a novel Random Forest based k Nearest Neighbors (RF-kNN) imputation approach to couple remote sensing and geospatial data with field inventory collected by different sampling methods to generate forest inventory information across large spatial extents. The forest inventory data collected by the FIA program of US Forest Service was integrated with optical remote sensing and other geospatial datasets to produce biomass distribution maps for a part of the Lake States and species-specific site index maps for the entire Lake State. Targeting small-area application of the state-of-art remote sensing, LiDAR (light detection and ranging) data was integrated with the field data collected by an inexpensive method, called variable plot sampling, in the Ford Forest of Michigan Tech to derive standing volume map in a cost-effective way. The outputs of the RF-kNN imputation were compared with independent validation datasets and extant map products based on different sampling and modeling strategies. The RF-kNN modeling approach was found to be very effective, especially for large-area estimation, and produced results statistically equivalent to the field observations or the estimates derived from secondary data sources. The models are useful to resource managers for operational and strategic purposes.