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.

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. ^

Learning in the cerebellar nucleus is necessary for acquisition of Pavlovian eyelid conditioning

The cumulative work presented here supports the hypothesis that plasticity in the cerebellar cortex and cerebellar nuclei mediates a simple associative form of motor teaming-Pavlovian eyelid conditioning. It was previously demonstrated that focal ablative lesions of cerebellar anterior lobe or pharmacological block of the cerebellar cortex output disrupted the timing of the conditioned eyeblink response, unmasking a response with a relatively fixed and very short latency to onset. The results of this thesis demonstrate that the short-latency responses are due to associative learning. Unpaired training does not support the acquisition of short-latency responses while the rate of acquisition of short-latency responses during paired training is approximately the same as that of timed conditioned responses. The acquisition of short-latency responses is dependent on an intact cerebellar cortex. Both ablative lesions of the cerebellar cortex and inactivation of cerebellar cortex output with picrotoxin block the acquisition of short-latency responses. However, once the short-latency responses are acquired neither disconnection of cerebellar cortex nor inactivation of the cerebellar nucleus block reacquisition. The results are consistent with the proposal that plasticity in the cerebellar cortex is necessary for learning the timing of conditioned responses, plasticity in the interpositus nucleus mediates the short latency responses, and cerebellar cortical output and mossy fiber input are necessary for the acquisition of short latency responses. ^

A descriptive study of the sociodemographic, health and developmental factors associated with slow learning in children from upper to middle social class families in Texas

"Slow Learners" is a term used to describe children with an IQ range of 70-89 on a standardized individual intelligence test (i.e. with a standard deviation of either 15 or 16). They have above retarded, but below average intelligence and potential to learn. If the factors associated with the etiology of slow learning in children can be identified, it may be possible to hypothesize causal relationships which can be tested by intervention studies specifically designed to prevent slow learning. If effective, these may ultimately reduce the incidence of school dropouts and their cost to society. To date, there is little information about variables which may be etiologically significant. In an attempt to identify such etiologic factors this study examines the sociodemographic characteristics, prenatal history (hypertension, smoking, infections, medication, vaginal bleeding, etc.), natal history (length of delivery, Apgar score, birth trauma, resuscitation, etc.), neonatal history (infections, seizures, head trauma, etc.), developmental history (health problems, developmental milestones and growth during infancy and early childhood), and family history (educational level of the parents, occupation, history of similar condition in the family, etc.) of a series of children defined as slow learners. The study is limited to children from middle to high socioeconomic families in order to exclude the possible confounding variable of low socioeconomic status, and because a descriptive study of this group has not been previously reported. ^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Active Learning in Nursing Education

ALINE is a pedagogical model developed to aid nursing faculty transition from passive to active learning. Based on constructionist theory, ALINE serves as a tool for organizing curriculum for online and classroom based interaction and permits positioning the student as the active player and the instructor, the facilitator to nursing competency.