The authors evaluated a series of homework assignments featuring counterstereotypical examples of scientists in an introductory biology class. Following the intervention, students exhibited nonstereotypical views of scientists and conveyed an enhanced ability to personally relate to scientists. These shifts correlated with science interest and course grades.

A negative ion electrospray ionization tandem mass spectrometric technique was developed for the analysis of glycerophospholipids. Examination of the product ion mass spectrum of the deprotonated molecular ion provided sufficient information to identify both the class of glycerophospholipid and the molecular weights of the two fatty acid moieties. This technique was applied to the profiling of glycerophospholipids present in the chloroform/methanol extracts of four different bacterial species. The principal bacterial phospholipids detected by this technique were phosphatidylglycerols and diphosphatidylglycerols, accompanied by small amounts of phosphatidylethanolamines for two of the bacterial species examined. The fatty acid composition of the phosphatidylglycerols for each bacteria was determined by tandem mass spectrometry and presented graphically. Differences in the fatty acid composition for each bacterial species were readily apparent from a visual examination of the data sets.

Detection and identification of pathogenic bacteria and their protein toxins play a crucial role in a proper response to natural or terrorist-caused outbreaks of infectious diseases. The recent availability of whole genome sequences of priority bacterial pathogens opens new diagnostic possibilities for identification of bacteria by retrieving their genomic or proteomic information. We describe a method for identification of bacteria based on tandem mass spectrometric (MS/MS) analysis of peptides derived from bacterial proteins. This method involves bacterial cell protein extraction, trypsin digestion, liquid chromatography MS/MS analysis of the resulting peptides, and a statistical scoring algorithm to rank MS/MS spectral matching results for bacterial identification. To facilitate spectral data searching, a proteome database was constructed by translating genomes of bacteria of interest with fully or partially determined sequences. In this work, a prototype database was constructed by the automated analysis of 87 publicly available, fully sequenced bacterial genomes with the GLIMMER gene finding software. MS/MS peptide spectral matching for peptide sequence assignment against this proteome database was done by SEQUEST. To gauge the relative significance of the SEQUEST-generated matching parameters for correct peptide assignment, discriminant function (DF) analysis of these parameters was applied and DF scores were used to calculate probabilities of correct MS/MS spectra assignment to peptide sequences in the database. The peptides with DF scores exceeding a threshold value determined by the probability of correct peptide assignment were accepted and matched to the bacterial proteomes represented in the database. Sequence filtering or removal of degenerate peptides matched with multiple bacteria was then performed to further improve identification. It is demonstrated that using a preset criterion with known distributions of discriminant function scores and probabilities of correct peptide sequence assignments, a test bacterium within the 87 database microorganisms can be unambiguously identified.