Variables affecting the precision and accuracy of the Intoxilyzer 5000

The Intoxilyzer 5000 was tested for calibration curve linearity for ethanol vapor concentration between 0.020 and 0.400g/210L with excellent linearity. Calibration error using reference solutions outside of the allowed concentration range, response to the same ethanol reference solution at different temperatures between 34 and 38$\sp\circ$C, and its response to eleven chemicals, 10 mixtures of two at the time, and one mixture of four chemicals potentially found in human breath have been evaluated. Potential interferents were chosen on the basis of their infrared signatures and the concentration range of solutions corresponding to the non-lethal blood concentration range of various volatile organic compounds reported in the literature. The result of this study indicates that the instrument calibrates with solutions outside the allowed range up to $\pm$10% of target value. Headspace FID dual column GC analysis was used to confirm the concentrations of the solutions. Increasing the temperature of the reference solution from 34 to 38$\sp\circ$C resulted in linear increases in instrument recorded ethanol readings with an average increase of 6.25%/$\sp\circ$C. Of the eleven chemicals studied during this experiment, six, isopropanol, toluene, methyl ethyl ketone, trichloroethylene, acetaldehyde, and methanol could reasonably interfere with the test at non-lethal reported blood concentration ranges, the mixtures of those six chemicals showed linear additive results with a combined effect of as much as a 0.080g/210L reading (Florida's legal limit) without any ethanol present. ^

A Suite of Prey Traits Determine Predator and Nutrient Enrichment Effects in a Tri-Trophic Food Chain

Predation, predation risk, and resource quality affect suites of prey traits that collectively impact individual fitness, population dynamics, and community structure. However, studies of multi-trophic level effects generally focus on a single prey trait, failing to capture trade-offs among suites of covarying traits that govern population responses and emergent community patterns. We used structural equation models (SEM) to summarize the non-lethal and lethal effects of crayfish, Procambarus fallax, and phosphorus (P) addition, which affected prey food quality (periphyton), on the interactive effects of behavioral, morphological, developmental, and reproductive traits of snails, Planorbella duryi. Univariate and multivariate analyses suggested trade-offs between production (growth, reproduction) and defense (foraging behavior, shell shape) traits of snails in response to non-lethal crayfish and P addition, but few lethal effects. SEM revealed that non-lethal crayfish effects indirectly limited per capita offspring standing stock by increasing refuge use, slowing individual growth, and inducing snails to produce thicker, compressed shells. The negative effects of non-lethal crayfish on snails were strongest with P addition; snails increased allocation to shell defense rather than growth or reproduction. However, compared to ambient conditions, P addition with non-lethal crayfish still yielded greater per capita offspring standing stock by speeding individual snail growth enabling them to produce more offspring that also grew faster. Increased refuge use in response to non-lethal crayfish led to a non-lethal trophic cascade that altered the spatial distribution of periphyton. Independent of crayfish effects, snails stimulated periphyton growth through nutrient regeneration. These findings illustrate the importance of studying suites of traits that reveal costs associated with inducing different traits and how expressing those traits impacts population and community level processes.

The effect of cooperative learning on the attitudes toward science and the achievement of students in a non-science majors' general biology laboratory course at an urban community college

The purpose of this study was to evaluate the effect of cooperative learning strategies on students' attitudes toward science and achievement in BSC 1005L, a non-science majors' general biology laboratory course at an urban community college. Data were gathered on the participants' attitudes toward science and cognitive biology level pre and post treatment in BSC 1005L. Elements of the Learning Together model developed by Johnson and Johnson and the Student Team-Achievement Divisions model created by Slavin were incorporated into the experimental sections of BSC 1005L.^ Four sections of BSC 1005L participated in this study. Participants were enrolled in the 1998 spring (January) term. Students met weekly in a two hour laboratory session. The treatment was administered to the experimental group over a ten week period. A quasi-experimental pretest-posttest control group design was used. Students in the cooperative learning group (n$\sb1$ = 27) were administered the Test of Science-Related Attitudes (TOSRA) and the cognitive biology test at the same time as the control group (n$\sb2$ = 19) (at the beginning and end of the term).^ Statistical analyses confirmed that both groups were equivalent regarding ethnicity, gender, college grade point average and number of absences. Independent sample t-tests performed on pretest mean scores indicated no significant differences in the TOSRA scale two or biology knowledge between the cooperative learning group and the control group. The scores of TOSRA scales: one, three, four, five, six, and seven were significantly lower in the cooperative learning group. Independent sample t-tests of the mean score differences did not show any significant differences in posttest attitudes toward science or biology knowledge between the two groups. Paired t-tests did not indicate any significant differences on the TOSRA or biology knowledge within the cooperative learning group. Paired t-tests did show significant differences within the control group on TOSRA scale two and biology knowledge. ANCOVAs did not indicate any significant differences on the post mean scores of the TOSRA or biology knowledge adjusted by differences in the pretest mean scores. Analysis of the research data did not show any significant correlation between attitudes toward science and biology knowledge. ^