OmicsVis: an interactive tool for visually analyzing metabolomics data

When analyzing metabolomics data, cancer care researchers are searching for differences between known healthy samples and unhealthy samples. By analyzing and understanding these differences, researchers hope to identify cancer biomarkers. Due to the size and complexity of the data produced, however, analysis can still be very slow and time consuming. This is further complicated by the fact that datasets obtained will exhibit incidental differences in intensity and retention time, not related to actual chemical differences in the samples being evaluated. Additionally, automated tools to correct these errors do not always produce reliable results. This work presents a new analytics system that enables interactive comparative visualization and analytics of metabolomics data obtained by two-dimensional gas chromatography-mass spectrometry (GC × GC-MS). The key features of this system are the ability to produce visualizations of multiple GC × GC-MS data sets, and to explore those data sets interactively, allowing a user to discover differences and features in real time. The system provides statistical support in the form of difference, standard deviation, and kernel density estimation calculations to aid users in identifying meaningful differences between samples. These are combined with novel transfer functions and multiform, linked visualizations in order to provide researchers with a powerful new tool for GC × GC-MS exploration and bio-marker discovery

The Efficacy of Vaginal Clindamycin for the Treatment of Abnormal Genital Tract Flora in Pregnancy

Objective: To assess the efficacy of 2% clindamycin vaginal cream (CVC) to treat bacterial vaginosis (BV) in pregnancy. Methods: A prospective, randomized, double-blind, placebo-controlled, tricenter study. Four hundred and four women with BV on Gram stain at their first antenatal clinic visit were randomized to receive a 3-day course of 2% CVC or placebo. The outcome was assessed using an intention to treat analysis at 3 weeks and 6 weeks post-treatment according to three different diagnostic methods based on five criteria (Gram stain and all four elements of clinical composite criteria: vaginal discharge, abnormal vaginal pH, clue cells, amine odor), three criteria (vaginal pH, clue cells, amine odor) or two criteria (clue cells and amine odor) to reflect stringency of diagnosis, historical precedence and government agency recommendations respectively. Results: Using five diagnostic criteria, 18% of CVC patients were cured and 70.8% either cured and/or improved compared to 1.6% and 12% of placebo patients respectively (p < 0.0001). Using three diagnostic criteria, 44.8% of CVC patients were cured and 77.3% were either cured and/or improved compared to 9.3% and 28.8% of placebo patients respectively (p < 0.0001). Using two diagnostic criteria, 75.0% of CVC patients were cured compared to 18.0% of placebo patients (p < 0.0001). Recurrence rates in those CVC patients successfully treated were approximately 6% at 6 weeks post baseline and 10% at 28 to 34 weeks gestation. Conclusions: A 3-day course of CVC appears to be well tolerated by the mother and statistically significantly more efficacious than placebo in the treatment of BV during the second trimester of pregnancy

Comparative visual analytic techniques for mass spectrometry data

When analyzing metabolomics data, cancer care researchers are searching for differences between known healthy samples and unhealthy samples. By analyzing and understanding these differences, researchers hope to identify cancer biomarkers. Due to the size and complexity of the data produced, however, analysis can still be very slow and time consuming. This is further complicated by the fact that datasets obtained will exhibit incidental differences in intensity and retention time, not related to actual chemical differences in the samples being evaluated. Additionally, automated tools to correct these errors do not always produce reliable results. This work presents a new analytics system that enables interactive comparative visualization and analytics of metabolomics data obtained by two-dimensional gas chromatography-mass spectrometry (GCxGC-MS). The key features of this system are the ability to produce visualizations of multiple GCxGC-MS data sets, and to explore those data sets interactively, allowing a user to discover differences and features in real time. The system provides statistical support in the form of difference, standard deviation, and kernel density estimation calculations to aid users in identifying meaningful differences between samples. These are combined with novel transfer functions and multiform, linked visualizations in order to provide researchers with a powerful new tool for GCxGC-MS exploration and bio-marker discovery

The Ultraviolet Spectrograph on NASA’s Juno Mission

The ultraviolet spectrograph instrument on the Juno mission (Juno-UVS) is a long-slit imaging spectrograph designed to observe and characterize Jupiter’s far-ultraviolet (FUV) auroral emissions. These observations will be coordinated and correlated with those from Juno’s other remote sensing instruments and used to place in situ measurements made by Juno’s particles and fields instruments into a global context, relating the local data with events occurring in more distant regions of Jupiter’s magnetosphere. Juno-UVS is based on a series of imaging FUV spectrographs currently in flight—the two Alice instruments on the Rosetta and New Horizons missions, and the Lyman Alpha Mapping Project on the Lunar Reconnaissance Orbiter mission. However, Juno-UVS has several important modifications, including (1) a scan mirror (for targeting specific auroral features), (2) extensive shielding (for mitigation of electronics and data quality degradation by energetic particles), and (3) a cross delay line microchannel plate detector (for both faster photon counting and improved spatial resolution). This paper describes the science objectives, design, and initial performance of the Juno-UVS