Final Technical Report on Radioxenon Event Analysis

This is a final deliverable report for the Advanced Spectral Analysis for Radioxenon project with a focus on radioxenon event categorization

Structured inquiry-based learning: Drosophila GAL4 enhancer trap characterization in an undergraduate laboratory course.

We have developed and tested two linked but separable structured inquiry exercises using a set of Drosophila melanogaster GAL4 enhancer trap strains for an upper-level undergraduate laboratory methods course at Bucknell University. In the first, students learn to perform inverse PCR to identify the genomic location of the GAL4 insertion, using FlyBase to identify flanking sequences and the primary literature to synthesize current knowledge regarding the nearest gene. In the second, we cross each GAL4 strain to a UAS-CD8-GFP reporter strain, and students perform whole mount CNS dissection, immunohistochemistry, confocal imaging, and analysis of developmental expression patterns. We have found these exercises to be very effective in teaching the uses and limitations of PCR and antibody-based techniques as well as critical reading of the primary literature and scientific writing. Students appreciate the opportunity to apply what they learn by generating novel data of use to the wider research community

Final Technical Report on Radioxenon Event Analysis

This is a final deliverable report for the Advanced Spectral Analysis for Radioxenon project with a focus on radioxenon event categorization

In silico identification software (ISIS): a machine learning approach to tandem mass spectral identification of lipids

Liquid chromatography-mass spectrometry-based metabolomics has gained importance in the life sciences, yet it is not supported by software tools for high throughput identification of metabolites based on their fragmentation spectra. An algorithm (ISIS: in silico identification software) and its implementation are presented and show great promise in generating in silico spectra of lipids for the purpose of structural identification. Instead of using chemical reaction rate equations or rules-based fragmentation libraries, the algorithm uses machine learning to find accurate bond cleavage rates in a mass spectrometer employing collision-induced dissociation tandem mass spectrometry. A preliminary test of the algorithm with 45 lipids from a subset of lipid classes shows both high sensitivity and specificity

Structured Inquiry-Based Learning: Drosophila GAL4 Enhancer Trap Characterization in an Undergraduate Laboratory Course

We have developed and tested two linked but separable structured inquiry exercises using a set of Drosophila melanogaster GAL4 enhancer trap strains for an upper-level undergraduate laboratory methods course at Bucknell University. In the first, students learn to perform inverse PCR to identify the genomic location of the GAL4 insertion, using FlyBase to identify flanking sequences and the primary literature to synthesize current knowledge regarding the nearest gene. In the second, we cross each GAL4 strain to a UAS-CD8-GFP reporter strain, and students perform whole mount CNS dissection, immunohistochemistry, confocal imaging, and analysis of developmental expression patterns. We have found these exercises to be very effective in teaching the uses and limitations of PCR and antibody-based techniques as well as critical reading of the primary literature and scientific writing. Students appreciate the opportunity to apply what they learn by generating novel data of use to the wider research community

In silico identification software (ISIS): a machine learning approach to tandem mass spectral identification of lipids

Motivation: Liquid chromatography–mass spectrometry-based metabolomics has gained importance in the life sciences, yet it is not supported by software tools for high throughput identification of metabolites based on their fragmentation spectra. An algorithm (ISIS: in silico identification software) and its implementation are presented and show great promise in generating in silico spectra of lipids for the purpose of structural identification. Instead of using chemical reaction rate equations or rules-based fragmentation libraries, the algorithm uses machine learning to find accurate bond cleavage rates in a mass spectrometer employing collision-induced dissociation tandem mass spectrometry. Results: A preliminary test of the algorithm with 45 lipids from a subset of lipid classes shows both high sensitivity and specificity. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Structured Inquiry-Based Learning: <i>Drosophila</i> GAL4 Enhancer Trap Characterization in an Undergraduate Laboratory Course

<div><p>We have developed and tested two linked but separable structured inquiry exercises using a set of <i>Drosophila melanogaster</i> GAL4 enhancer trap strains for an upper-level undergraduate laboratory methods course at Bucknell University. In the first, students learn to perform inverse PCR to identify the genomic location of the GAL4 insertion, using FlyBase to identify flanking sequences and the primary literature to synthesize current knowledge regarding the nearest gene. In the second, we cross each GAL4 strain to a UAS-CD8-GFP reporter strain, and students perform whole mount CNS dissection, immunohistochemistry, confocal imaging, and analysis of developmental expression patterns. We have found these exercises to be very effective in teaching the uses and limitations of PCR and antibody-based techniques as well as critical reading of the primary literature and scientific writing. Students appreciate the opportunity to apply what they learn by generating novel data of use to the wider research community.</p></div