Advances in Teaching & Learning Day Abstracts 2004

Proceedings of the Advances in Teaching & Learning Day Regional Conference held at The University of Texas Health Science Center at Houston in 2004.

Advances in Teaching & Learning Day Abstracts 2003

Proceedings of the Advances in Teaching & Learning Day Regional Conference held at The University of Texas Health Science Center at Houston in 2003.

Advances in Teaching & Learning Day Abstracts 2005

Proceedings of the Advances in Teaching & Learning Day Regional Conference held at The University of Texas Health Science Center at Houston in 2005.

Advances in Teaching & Learning Day Abstracts 2006

Proceedings of the Advances in Teaching & Learning Day Regional Conference held at The University of Texas Health Science Center at Houston in 2006.

Mechanisms of perceptual learning of depth discrimination in random dot stereograms.

Perceptual learning is a training induced improvement in performance. Mechanisms underlying the perceptual learning of depth discrimination in dynamic random dot stereograms were examined by assessing stereothresholds as a function of decorrelation. The inflection point of the decorrelation function was defined as the level of decorrelation corresponding to 1.4 times the threshold when decorrelation is 0%. In general, stereothresholds increased with increasing decorrelation. Following training, stereothresholds and standard errors of measurement decreased systematically for all tested decorrelation values. Post training decorrelation functions were reduced by a multiplicative constant (approximately 5), exhibiting changes in stereothresholds without changes in the inflection points. Disparity energy model simulations indicate that a post-training reduction in neuronal noise can sufficiently account for the perceptual learning effects. In two subjects, learning effects were retained over a period of six months, which may have application for training stereo deficient subjects.

Practitioners' Views of Family Strengths: A Delphi Study

Reported is a research study to assess the opinions of family practitioners on the status of families in Oklahoma. Researchers employed the Delphi method to achieve consensus among key informants in the family practice field about the strengths and weaknesses of Oklahoma families, threats facing families in the state, and means to strengthening family life in Oklahoma. The study yielded qualitative data from the key informants, which the researchers then condensed into response categories to feed back to informants to rate. Family practitioners identified resilience, spirituality, and access to support systems as the greatest strengths, and listed substance abuse, poverty, and generational cycles of dysfunction as the greatest weaknesses of Oklahoma families. Recommendations by these practitioners are given for improvements in addressing family needs.

Commentary on "Practitioners’ Views of Family Strengths: A Delphi Study"

Commentary on "Practitioners’ Views of Family Strengths: A Delphi Study"

Neural analogs of associative learning in {\it Aplysia\/}: Experimental and theoretical approaches

A change in synaptic strength arising from the activation of two neuronal pathways at approximately the same time is a form of associative plasticity and may underlie classical conditioning. Previously, a cellular analog of a classical conditioning protocol has been demonstrated to produce short-term associative plasticity at the connections between sensory and motor neurons in Aplysia. A similar training protocol produced long-term (24 hour) enhancement of excitatory postsynaptic potentials (EPSPs). EPSPs produced by sensory neurons in which activity was paired with a reinforcing stimulus were significantly larger than unpaired controls 24 hours after training. To examined whether the associative plasticity observed at these synapses may be involved in higher-order forms of classical conditioning, a neural analog of contingency was developed. In addition, computer simulations were used to analyze whether the associative plasticity observed in Aplysia could, in theory, account for second-order conditioning and blocking. ^

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^