Behavioral and cellular analysis of classical conditioning of feeding behavior in Aplysia

In classical conditioning, an associative form of learning, animals learn to associate two stimuli. Cellular and molecular mechanisms for the induction and consolidation of associative learning and memory at the level of single cells and synaptic connections have been studied in both vertebrate and invertebrate animals. The majority of studies, however, relied on aversive stimuli to induce learning. This bias may limit the extent to which identified mechanisms generalize to other forms of associative learning and memory, such as appetitive forms. The goal of the present study was to develop a classical conditioning procedure for the marine mollusk Aplysia californica using appetitive reinforcement, and to analyze associative learning using behavioral and electrophysiological techniques. ^ Using tactile stimulation of the lips as the conditional stimulus (CS) and food as the unconditional stimulus (US) a training protocol was developed that reliably induced classical conditioning of feeding behavior. Memory persisted for at least 24 hours. The gross organization of reinforcement-mediating pathways was analyzed in additional behavioral experiments. Moreover, neurophysiological correlates of classical conditioning were identified and characterized in an in vitro preparation containing the circuitry for feeding behavior. In vitro stimulation of a nerve (AT4) that may mediate the CS during training, resulted in a greater number of buccal motor patterns (BMPs) in brains from conditioned animals, as compared to control animals. The majority of these BMPs were ingestion-like, consistent with the increased number of bites in response to the CS after classical conditioning. Moreover, classical conditioning correlated with increased excitatory synaptic input to BMP-initiating neuron B31/32, in response to stimulation of AT 4, as compared to controls. The expression of the correlates of classical conditioning identified in this study was specific to stimulation of AT 4, which is consistent the stimulus specificity that is characteristic for classical conditioning. ^ The identification of cellular correlates of classical conditioning documented here provides the basis for future, more detailed analyses of an appetitive form of associative learning and memory, that may extend the working knowledge of the cellular and molecular mechanisms for associative plasticity in general. ^

New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^

Assessment of the effect on statistical power of regression model misspecification by using techniques of mathematical statistics and simulation study

Objectives. This paper seeks to assess the effect on statistical power of regression model misspecification in a variety of situations. ^ Methods and results. The effect of misspecification in regression can be approximated by evaluating the correlation between the correct specification and the misspecification of the outcome variable (Harris 2010).In this paper, three misspecified models (linear, categorical and fractional polynomial) were considered. In the first section, the mathematical method of calculating the correlation between correct and misspecified models with simple mathematical forms was derived and demonstrated. In the second section, data from the National Health and Nutrition Examination Survey (NHANES 2007-2008) were used to examine such correlations. Our study shows that comparing to linear or categorical models, the fractional polynomial models, with the higher correlations, provided a better approximation of the true relationship, which was illustrated by LOESS regression. In the third section, we present the results of simulation studies that demonstrate overall misspecification in regression can produce marked decreases in power with small sample sizes. However, the categorical model had greatest power, ranging from 0.877 to 0.936 depending on sample size and outcome variable used. The power of fractional polynomial model was close to that of linear model, which ranged from 0.69 to 0.83, and appeared to be affected by the increased degrees of freedom of this model.^ Conclusion. Correlations between alternative model specifications can be used to provide a good approximation of the effect on statistical power of misspecification when the sample size is large. When model specifications have known simple mathematical forms, such correlations can be calculated mathematically. Actual public health data from NHANES 2007-2008 were used as examples to demonstrate the situations with unknown or complex correct model specification. Simulation of power for misspecified models confirmed the results based on correlation methods but also illustrated the effect of model degrees of freedom on power.^