General and special education teachers' knowledge of and attitudes toward students with HIV/AIDS

AIDS education is mandated in schools throughout the United States to educate students about the disease. Teachers are expected to assume the major role of disseminating this information; therefore it is reasonable to question how knowledgeable teachers are about HIV/AIDS and where their information is coming from. This study explored the knowledge and attitudes of general and special education teachers toward students with HIV/AIDS and investigated whether a relationship between knowledge and attitudes existed. Information was collected using the AIDS Knowledge and Attitude Survey (AKAS). The sample was limited to certified teachers resulting in 318 participants.^ Research questions were analyzed using descriptive statistics, frequencies, t-tests, one-way analysis of variance (ANOVA), Scheffe post hoc analysis, and Pearson Product-Moment Correlation. Results indicated that general and special education teachers did not have complete knowledge about HIV/AIDS. Participants were knowledgeable regarding modes of transmission which may be the area of greatest concern for teachers, however, deficiencies were present within teachers' knowledge of general statements and facts and pathology. Among the ten demographic variables analyzed, six (gender, race/ethnicity, level of education, certification, instructional level taught, and classroom AIDS instruction) contained statistical significance.^ Analysis of attitudes indicated that general and special education teachers' overall attitudes toward students with HIV/AIDS were generally positive within clusters of Instruction and Fear, but not within Sensitivity and Communication. Among the ten demographic variables used for analysis only three (age, graduate enrollment status, and classroom AIDS instruction) produced statistical significance. Results found statistically significant relationships between Total Knowledge, all knowledge subtests, Fear, and Overall Attitudes. Statistical significance was also located on Total Knowledge, Pathology and Transmission knowledge subtests, and Sensitivity, as well as between Pathology and Instruction, and General Statements and Facts and Communication.^ The only variable determined to have statistical significance on both knowledge and attitudes was classroom AIDS instruction. Participants with previous AIDS instruction showed greater knowledge and possessed more positive attitudes. A review of previous research indicated training to be effective in increasing knowledge and fostering more favorable behavior toward persons with AIDS. Therefore, this study finds AIDS training to be beneficial for all teachers and is recommended during preservice education or through inservices for teachers already in the field. ^

Molecular markers and human history: A tale of two haplogroup systems

To chronicle demographic movement across African Asian corridors, a variety of molecular (sequence analysis, restriction mapping and denaturing high performance liquid chromatography etc.) and statistical (correspondence analysis, AMOVA, calculation of diversity indices and phylogenetic inference, etc.) techniques were employed to assess the phylogeographic patterns of mtDNA control region and Y chromosomal variation among 14 sub-Saharan, North African and Middle Eastern populations. The patterns of genetic diversity revealed evidence of multiple migrations across several African Asian passageways as well within the African continent itself. The two-part analysis uncovered several interesting results which include the following: (1) a north (Egypt and Middle East Asia) to south (sub-Saharan Africa) partitioning of both mtDNA and Y chromosomal haplogroup diversity, (2) a genetic diversity gradient in sub-Saharan Africa from east to west, (3) evidence in favor of the Levantine Corridor over the Horn of Africa as the major genetic conduit since the Last Glacial Maximum, (4) a substantially higher mtDNA versus Y chromosomal sub-Saharan component in the Middle East collections, (5) a higher representation of East versus West African mtDNA haplotypes in the Arabian Peninsula populations versus no such bias in the Levant groups and lastly, (6) genetic remnants of the Bantu demographic expansion in sub-Saharan Africa. ^

Statistical analysis and optimal classification of blood cell populations using Gaussian distributions

This dissertation develops a new figure of merit to measure the similarity (or dissimilarity) of Gaussian distributions through a novel concept that relates the Fisher distance to the percentage of data overlap. The derivations are expanded to provide a generalized mathematical platform for determining an optimal separating boundary of Gaussian distributions in multiple dimensions. Real-world data used for implementation and in carrying out feasibility studies were provided by Beckman-Coulter. It is noted that although the data used is flow cytometric in nature, the mathematics are general in their derivation to include other types of data as long as their statistical behavior approximate Gaussian distributions. ^ Because this new figure of merit is heavily based on the statistical nature of the data, a new filtering technique is introduced to accommodate for the accumulation process involved with histogram data. When data is accumulated into a frequency histogram, the data is inherently smoothed in a linear fashion, since an averaging effect is taking place as the histogram is generated. This new filtering scheme addresses data that is accumulated in the uneven resolution of the channels of the frequency histogram. ^ The qualitative interpretation of flow cytometric data is currently a time consuming and imprecise method for evaluating histogram data. This method offers a broader spectrum of capabilities in the analysis of histograms, since the figure of merit derived in this dissertation integrates within its mathematics both a measure of similarity and the percentage of overlap between the distributions under analysis. ^

Statistical analysis and meta-analysis of microarray data

The microarray technology provides a high-throughput technique to study gene expression. Microarrays can help us diagnose different types of cancers, understand biological processes, assess host responses to drugs and pathogens, find markers for specific diseases, and much more. Microarray experiments generate large amounts of data. Thus, effective data processing and analysis are critical for making reliable inferences from the data. ^ The first part of dissertation addresses the problem of finding an optimal set of genes (biomarkers) to classify a set of samples as diseased or normal. Three statistical gene selection methods (GS, GS-NR, and GS-PCA) were developed to identify a set of genes that best differentiate between samples. A comparative study on different classification tools was performed and the best combinations of gene selection and classifiers for multi-class cancer classification were identified. For most of the benchmarking cancer data sets, the gene selection method proposed in this dissertation, GS, outperformed other gene selection methods. The classifiers based on Random Forests, neural network ensembles, and K-nearest neighbor (KNN) showed consistently god performance. A striking commonality among these classifiers is that they all use a committee-based approach, suggesting that ensemble classification methods are superior. ^ The same biological problem may be studied at different research labs and/or performed using different lab protocols or samples. In such situations, it is important to combine results from these efforts. The second part of the dissertation addresses the problem of pooling the results from different independent experiments to obtain improved results. Four statistical pooling techniques (Fisher inverse chi-square method, Logit method. Stouffer's Z transform method, and Liptak-Stouffer weighted Z-method) were investigated in this dissertation. These pooling techniques were applied to the problem of identifying cell cycle-regulated genes in two different yeast species. As a result, improved sets of cell cycle-regulated genes were identified. The last part of dissertation explores the effectiveness of wavelet data transforms for the task of clustering. Discrete wavelet transforms, with an appropriate choice of wavelet bases, were shown to be effective in producing clusters that were biologically more meaningful. ^