A cognitive schematic paradigm for expository essay writing in secondary schools

The methodology outlined in this study for teaching exposit ory writing to advanced (five year phase) grade eleven students is based on the assumption that writing as a problem solving strategy is a high level cognitive skill . In adhering to this assumption, a cognitively based schematic organizer known as a cross-classification chart was tested for its effectiveness a t the planning stage of the writing process . Results were not significant in any of the three components that were evaluated; however , a post- hoc analysis undertaken because of recorded observed data indicated a significant difference in the mean score on the Organization component for the treatment subgroup using the cross- classification organizer . Furthermore, the treatment group's positive response from the attitude survey towards planning writing is encouraging enough that replication and extension of the application of schema theory to wri ting should be pursued in cross-section and longitud i nal studies.

Human Endogenous Retrovirus (HERV) Insertional Polymorphisms

Human endogenous retroviruses (HERVs) are the result of ancient germ cell infections of human germ cells by exogenous retroviruses. HERVs belong to the long terminal repeat (LTR) group of retrotransposons that comprise ~8% of the human genome. The majority of the HERVs documented have been truncated and/or incurred lethal mutations and no longer encode functional genes; however a very small number of HERVs seem to maintain functional in making new copies by retrotranspositon as suggested by the identification of a handful of polymorphic HERV insertions in human populations. The objectives of this study were to identify novel insertion of HERVs via analysis of personal genomic data and survey the polymorphism levels of new and known HERV insertions in the human genome. Specifically, this study involves the experimental validation of polymorphic HERV insertion candidates predicted by personal genome-based computation prediction and survey the polymorphism level within the human population based on a set of 30 diverse human DNA samples. Based on computational analysis of a limited number of personal genome sequences, PCR genotyping aided in the identification of 15 dimorphic, 2 trimorphic and 5 fixed full-length HERV-K insertions not previously investigated. These results suggest that the proliferation rate of HERVKs, perhaps also other ERVs, in the human genome may be much higher than we previously appreciated and the recently inserted HERVs exhibit a high level of instability. Throughout this study we have observed the frequent presence of additional forms of genotypes for these HERV insertions, and we propose for the first time the establishment of new genotype reporting nomenclature to reflect all possible combinations of the pre-integration site, solo-LTR and full-length HERV alleles.

Hyperspectral Mineral Identification using SVM and SOM

Remote sensing techniques involving hyperspectral imagery have applications in a number of sciences that study some aspects of the surface of the planet. The analysis of hyperspectral images is complex because of the large amount of information involved and the noise within that data. Investigating images with regard to identify minerals, rocks, vegetation and other materials is an application of hyperspectral remote sensing in the earth sciences. This thesis evaluates the performance of two classification and clustering techniques on hyperspectral images for mineral identification. Support Vector Machines (SVM) and Self-Organizing Maps (SOM) are applied as classification and clustering techniques, respectively. Principal Component Analysis (PCA) is used to prepare the data to be analyzed. The purpose of using PCA is to reduce the amount of data that needs to be processed by identifying the most important components within the data. A well-studied dataset from Cuprite, Nevada and a dataset of more complex data from Baffin Island were used to assess the performance of these techniques. The main goal of this research study is to evaluate the advantage of training a classifier based on a small amount of data compared to an unsupervised method. Determining the effect of feature extraction on the accuracy of the clustering and classification method is another goal of this research. This thesis concludes that using PCA increases the learning accuracy, and especially so in classification. SVM classifies Cuprite data with a high precision and the SOM challenges SVM on datasets with high level of noise (like Baffin Island).