Bifurcation and singularity analysis of a molecular network for the induction of long-term memory.

Withdrawal reflexes of the mollusk Aplysia exhibit sensitization, a simple form of long-term memory (LTM). Sensitization is due, in part, to long-term facilitation (LTF) of sensorimotor neuron synapses. LTF is induced by the modulatory actions of serotonin (5-HT). Pettigrew et al. developed a computational model of the nonlinear intracellular signaling and gene network that underlies the induction of 5-HT-induced LTF. The model simulated empirical observations that repeated applications of 5-HT induce persistent activation of protein kinase A (PKA) and that this persistent activation requires a suprathreshold exposure of 5-HT. This study extends the analysis of the Pettigrew model by applying bifurcation analysis, singularity theory, and numerical simulation. Using singularity theory, classification diagrams of parameter space were constructed, identifying regions with qualitatively different steady-state behaviors. The graphical representation of these regions illustrates the robustness of these regions to changes in model parameters. Because persistent protein kinase A (PKA) activity correlates with Aplysia LTM, the analysis focuses on a positive feedback loop in the model that tends to maintain PKA activity. In this loop, PKA phosphorylates a transcription factor (TF-1), thereby increasing the expression of an ubiquitin hydrolase (Ap-Uch). Ap-Uch then acts to increase PKA activity, closing the loop. This positive feedback loop manifests multiple, coexisting steady states, or multiplicity, which provides a mechanism for a bistable switch in PKA activity. After the removal of 5-HT, the PKA activity either returns to its basal level (reversible switch) or remains at a high level (irreversible switch). Such an irreversible switch might be a mechanism that contributes to the persistence of LTM. The classification diagrams also identify parameters and processes that might be manipulated, perhaps pharmacologically, to enhance the induction of memory. Rational drug design, to affect complex processes such as memory formation, can benefit from this type of analysis.

An evaluation of a Lake Houston tributary, Cypress Creek, for contamination and water quality

Introduction. Lake Houston serves as a reservoir for both recreational and drinking water for residents of Houston, Texas, and the metropolitan area. The Texas Commission on Environmental Quality (TCEQ) expressed concerns about the water quality and increasing amounts of pathogenic bacteria in Lake Houston (3). The objective of this investigation is to evaluate water quality for the presence of bacteria, nitrates, nitrites, carbon, phosphorus, dissolved oxygen, pH, turbidity, suspended solids, dissolved solids, and chlorine in Cypress Creek. The aims of this project are to analyze samples of water from Cypress Creek and to render a quantitative and graphical representation of the results. The collected information will allow for a better understanding of the aqueous environment in Cypress Creek.^ Methods. Water samples were collected in August 2009 and analyzed in the field and at UTSPH laboratory by spectrophotometry and other methods. Mapping software was utilized to develop novel maps of the sample sites using coordinates attained with the Global Positioning System (GPS). Sample sites and concentrations were mapped using Geographic Information System (GIS) software and correlated with permitted outfalls and other land use characteristic.^ Results. All areas sampled were positive for the presence of total coliform and Escherichia coli (E. coli). The presences of other water contaminants varied at each location in Cypress Creek but were under the maximum allowable limits designated by the Texas Commission on Environmental Quality. However, dissolved oxygen concentrations were elevated above the TCEQ limit of 5.0 mg/L at majority of the sites. One site had near-limit concentration of nitrates at 9.8 mg/L. Land use above this site included farm land, agricultural land, golf course, parks, residential neighborhoods, and nine permitted TCEQ effluent discharge sites within 0.5 miles upstream.^ Significance. Lake Houston and its tributary, Cypress Creek, are used as recreational waters where individuals may become exposed to microbial contamination. Lake Houston also is the source of drinking water for much of Houston/Harris and Galveston Counties. This research identified the presence of microbial contaminates in Cypress Creek above TCEQ regulatory requirements. Other water quality variables measured were in line with TCEQ regulations except for near-limit for nitrate at sample site #10, at Jarvis and Timberlake in Cypress Texas.^

The Effect of Proximity, Explicitness, and Representation of Basic Science Information on Student Clinical Problem-Solving

Problem: Medical and veterinary students memorize facts but then have difficulty applying those facts in clinical problem solving. Cognitive engineering research suggests that the inability of medical and veterinary students to infer concepts from facts may be due in part to specific features of how information is represented and organized in educational materials. First, physical separation of pieces of information may increase the cognitive load on the student. Second, information that is necessary but not explicitly stated may also contribute to the student’s cognitive load. Finally, the types of representations – textual or graphical – may also support or hinder the student’s learning process. This may explain why students have difficulty applying biomedical facts in clinical problem solving. Purpose: To test the hypothesis that three specific aspects of expository text – the patial distance between the facts needed to infer a rule, the explicitness of information, and the format of representation – affected the ability of students to solve clinical problems. Setting: The study was conducted in the parasitology laboratory of a college of veterinary medicine in Texas. Sample: The study subjects were a convenience sample consisting of 132 second-year veterinary students who matriculated in 2007. The age of this class upon admission ranged from 20-52, and the gender makeup of this class consisted of approximately 75% females and 25% males. Results: No statistically significant difference in student ability to solve clinical problems was found when relevant facts were placed in proximity, nor when an explicit rule was stated. Further, no statistically significant difference in student ability to solve clinical problems was found when students were given different representations of material, including tables and concept maps. Findings: The findings from this study indicate that the three properties investigated – proximity, explicitness, and representation – had no statistically significant effect on student learning as it relates to clinical problem-solving ability. However, ad hoc observations as well as findings from other researchers suggest that the subjects were probably using rote learning techniques such as memorization, and therefore were not attempting to infer relationships from the factual material in the interventions, unless they were specifically prompted to look for patterns. A serendipitous finding unrelated to the study hypothesis was that those subjects who correctly answered questions regarding functional (non-morphologic) properties, such as mode of transmission and intermediate host, at the family taxonomic level were significantly more likely to correctly answer clinical case scenarios than were subjects who did not correctly answer questions regarding functional properties. These findings suggest a strong relationship (p < .001) between well-organized knowledge of taxonomic functional properties and clinical problem solving ability. Recommendations: Further study should be undertaken investigating the relationship between knowledge of functional taxonomic properties and clinical problem solving ability. In addition, the effect of prompting students to look for patterns in instructional material, followed by the effect of factors that affect cognitive load such as proximity, explicitness, and representation, should be explored.