No description available.

No description available.

No description available.

No description available.

This research project focuses on using a multiproxy approach to discriminate between overwash and non-hurricane marsh sediments within the bed of a coastal lake. 3 marsh cores were collected in an area of McFaddin National Wildlife Refuge just south of Clam Lake that are known to contain 4 hurricane overwash deposits, Ike, Rita, Carla, and Audrey. LOI and XRF analysis were used to determine the signature of the hurricane overwash layers. 3 more cores were collected from Clam Lake where there are no visible sand layers. The elemental signature of the overwash layers found in the marsh cores was used to run a hierarchical cluster analysis on the lake cores. This was able to determine the effectiveness of XRF's ability to distinguish between hurricane overwash and marsh sediments. The combination of cluster analysis, LOI, and XRF can tentatively identify hurricane overwash deposits in a coastal lake, however, it is more successful in the marsh cores. Results in the lake cores are somewhat inconsistent and uncertain, possibly because there may have not been enough overwash deposits to identity or that the XRF analysis needs more distinct sand layers to distinguish between overwash and marsh.

Expressed sequence tag database is a rich and fast growing source of data for gene expression analysis and drug discovery. Clustering of raw EST data is a necessary step for further analysis and one of the most challenging problems of modem computational biology.

Expressed sequence tag database is a rich and fast growing source of data for gene expression analysis and drug discovery. Clustering of raw EST data is a necessary step for further analysis and one of the most challenging problems of modem computational biology.

Philosophiae Doctor - PhD / Summary: Expressed sequence tag database is a rich and fast growing source of data for gene expression analysis and drug discovery. Clustering of raw EST data is a necessary step for further analysis and one of the most challenging problems of modem computational biology. There are a few systems, designed for this purpose and a few more are currently under development. These systems are reviewed in the "Literature and software review". Different strategies of supervised and unsupervised clustering are discussed, as well as sequence comparison techniques, such as based on alignment or oligonucleotide compositions. Analysis of potential bottlenecks and estimation of computation complexity of EST clustering is done in Chapter 2. This chapter also states the goals for the research and justifies the need for new algorithm that has to be fast, but still sensitive to relatively short (40 bp) regions of local similarity. A new sequence comparison algorithm is developed and described in Chapter 3. This algorithm has a linear computation complexity and sufficient sensitivity to detect short regions of local similarity between nucleotide sequences. The algorithm utilizes an asymmetric approach, when one of the compared sequences is presented in a form of oligonucleotide table, while the second sequence is in standard, linear form. A short window is moved along the linear sequence and all overlapping oligonucleotides of a constant length in the frame are compared for the oligonucleotide table. The result of 85 comparison of two sequencesis a single figure, which can be compared to a threshold. For each measure of sequence similarity a probability of false positive and false negative can be estimated. The algorithm was set up and implemented to recognize matching ESTs with overlapping regions of 40bp with 95% identity, which is better than resolution ability of contemporary EST clustering tools. This algorithm was used as a sequence comparison engine for two EST clustering programs, described in Chapter 4. These programs implement two different strategies: stringent and loose clustering. Both are tested on small, but realistic benchmark data sets and show the results, similar to one of the best existing clustering programs, D2_cluster, but with a significant advantage in speed and sensitivity to small overlapping regions of ESTs. On three different CPUs the new algorithm run at least two times faster, leaving less singletons and producing bigger clusters. With parallel optimization this algorithm is capable of clustering millions of ESTs on relatively inexpensive computers. The loose clustering variant is a highly portable application, relying on third-party software for cluster assembly. It was built to the same specifications as D2_cluster and can be immediately included into the ST ACKPack package for EST clustering. The stringent clustering program produces already assembled clusters and can apprehend alternatively processed variants during the clustering process. / South Africa

published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

In the last few years, two major contributions to the asteroid database have been the eight-color and thermal radiometric surveys. The former consists of broad-band photometric measurements through eight filters spanning the 0.3 to 1.1 μm wavelength range. The latter consists of thermal flux measurements at 10 and/or 20 μm, and when combined with measures of the reflected light, can yield reliable estimates of their geometric albedos. Visual display of the eight-color survey data can be simplified by reducing the dimensionality of the problem. A principal components analysis was performed to accomplish this task. The analysis shows that 95 percent of the information contained in the seven independent color indices is contained in two principal components. This result is due to the fact that most asteroid spectra can be explained in terms of two absorption features, one at ultraviolet and the other at near-infrared wavelengths. The photometric and radiometric data sets were also used, along with cluster analysis techniques, to produce an improved asteroid taxonomic system. Seven major classes are now recognized and are designated A, C, D, E, M, P, and S. Three interesting minor classes are also identified: B, F, and G. Marginal evidence for an eighth major class, here called T, exists in the data, but the reality of this class awaits confirmation by further observations of potential members. Three asteroids do not fall into any of the above classes and are assigned unique designations: R (349 Dembowska), Q (1862 Apollo), and V (4 Vesta). Four E-class asteroids are now known to exist in the main belt, yet nearly twice this number exist in or near the Hungaria region. Twenty eight D-class asteroids have been identified in the outer belt, where they represent a significant fraction of the population. Five D asteroids exist in the main belt, which one lying near the inner edge of the belt, which is dominated by S-class asteroids. Two of the interesting minor classes are associated with particular dynamical families. The Nysa family, with the single exception of Nysa itself, consists entirely of class F asteroids, while the B asteroids are found almost exclusively in the Themis family. The earth-approaching population is represented by at least two objects similar to Vesta and Dembowska, which are as many as are in the entire main belt, while most of the earth-approachers are of class S.