Building a Coherent Data Pipeline in Microarray Data Analyses: Optimization of Signal/Noise Ratios Using an Interactive Visualization Tool and a Novel Noise Filtering Method (2003)

Motivation: Sources of uncontrolled noise strongly influence data analysis in microarray studies, yet signal/noise ratios are rarely considered in microarray data analyses. We hypothesized that different research projects would have different sources and levels of confounding noise, and built an interactive visual analysis tool to test and define parameters in Affymetrix analyses that optimize the ratio of signal (desired biological variable) versus noise (confounding uncontrolled variables). Results: Five probe set algorithms were studied with and without statistical weighting of probe sets using Microarray Suite (MAS) 5.0 probe set detection p values. The signal/noise optimization method was tested in two large novel microarray datasets with different levels of confounding noise; a 105 sample U133A human muscle biopsy data set (11 groups) (extensive noise), and a 40 sample U74A inbred mouse lung data set (8 groups) (little noise). Success was measured using F-measure value of success of unsupervised clustering into appropriate biological groups (signal). We show that both probe set signal algorithm and probe set detection p-value weighting have a strong effect on signal/noise ratios, and that the different methods performed quite differently in the two data sets. Among the signal algorithms tested, dChip difference model with p-value weighting was the most consistent at maximizing the effect of the target biological variables on data interpretation of the two data sets. Availability: The Hierarchical Clustering Explorer 2.0 is [url=http://www.cs.umd.edu/hcil/hce/]available[/url] online and the improved version of the Hierarchical Clustering Explorer 2.0 with p-value weighting and Fmeasure is available upon request to the first author. Murine arrays (40 samples) are publicly available at the [url=http://microarray.cnmcresearch.org/pgadatatable.asp]PEPR resource.[/url] (Chen et al., 2004)

Gender-Specific Cardiovascular Reactions to +Gz Interval Training on a Short Arm Human Centrifuge

Cardiovascular deconditioning occurs in astronauts during microgravity exposure, and may lead to post-flight orthostatic intolerance, which is more prevalent in women than men. Intermittent artificial gravity is a potential countermeasure, which can effectively train the cardiovascular mechanisms responsible for maintaining orthostatic integrity. Since cardiovascular responses may differ between women and men during gravitational challenges, information regarding gender specific responses during intermittent artificial gravity exposure plays a crucial role in countermeasure strategies. This study implemented a +Gz interval training protocol using a ground based short arm human centrifuge, in order to assess its effectiveness in stimulating the components of orthostatic integrity, such as diastolic blood pressure, heart rate and vascular resistance amongst both genders. Twenty-eight participants (12 men/16 women) underwent a two-round graded +1/2/1 Gz profile, with each +Gz phase lasting 4 min. Cardiovascular parameters from each phase (averaged last 60 sec) were analyzed for significant changes with respect to baseline values. Twelve men and eleven women completed the session without interruption, while five women experienced an orthostatic event. These women had a significantly greater height and baseline mean arterial pressure than their counterparts. Throughout the +Gz interval session, women who completed the session exhibited significant increases in heart rate and systemic vascular resistance index throughout all +Gz phases, while exhibiting increases in diastolic blood pressure during several +Gz phases. Men expressed significant increases from baseline in diastolic blood pressure throughout the session with heart rate increases during the +2Gz phases, while no significant changes in vascular resistance were recorded. Furthermore, women exhibited non-significantly higher heart rates over men during all phases of +Gz. Based on these findings, this protocol proved to consistently stimulate the cardiovascular systems involved in orthostatic integrity to a larger extent amongst women than men. Thus the +Gz gradients used for this interval protocol may be beneficial for women as a countermeasure against microgravity induced cardiovascular deconditioning, whereas men may require higher +Gz gradients. Lastly, this study indicates that gender specific cardiovascular reactions are apparent during graded +Gz exposure while no significant differences regarding cardiovascular responses were found between women and men during intermittent artificial gravity training

IL-1α Signaling Is Critical for Leukocyte Recruitment after Pulmonary Aspergillus fumigatus Challenge

Aspergillus fumigatus is a mold that causes severe pulmonary infections. Our knowledge of how A. fumigatus growth is controlled in the respiratory tract is developing, but still limited. Alveolar macrophages, lung resident macrophages, and airway epithelial cells constitute the first lines of defense against inhaled A. fumigatus conidia. Subsequently, neutrophils and inflammatory CCR2+ monocytes are recruited to the respiratory tract to prevent fungal growth. However, the mechanism of neutrophil and macrophage recruitment to the respiratory tract after A. fumigatus exposure remains an area of ongoing investigation. Here we show that A. fumigatus pulmonary challenge induces expression of the inflammasome-dependent cytokines IL-1β and IL-18 within the first 12 hours, while IL-1α expression continually increases over at least the first 48 hours. Strikingly, Il1r1-deficient mice are highly susceptible to pulmonary A. fumigatus challenge exemplified by robust fungal proliferation in the lung parenchyma. Enhanced susceptibility of Il1r1-deficient mice correlated with defects in leukocyte recruitment and anti-fungal activity. Importantly, IL-1α rather than IL-1β was crucial for optimal leukocyte recruitment. IL-1α signaling enhanced the production of CXCL1. Moreover, CCR2+ monocytes are required for optimal early IL-1α and CXCL1 expression in the lungs, as selective depletion of these cells resulted in their diminished expression, which in turn regulated the early accumulation of neutrophils in the lung after A. fumigatus challenge. Enhancement of pulmonary neutrophil recruitment and anti-fungal activity by CXCL1 treatment could limit fungal growth in the absence of IL-1α signaling. In contrast to the role of IL-1α in neutrophil recruitment, the inflammasome and IL-1β were only essential for optimal activation of anti-fungal activity of macrophages. As such, Pycard-deficient mice are mildly susceptible to A. fumigatus infection. Taken together, our data reveal central, non-redundant roles for IL-1α and IL-1β in controlling A. fumigatus infection in the murine lung

Building a Coherent Data Pipeline in Microarray Data Analyses: Optimization of Signal/Noise Ratios Using an Interactive Visualization Tool and a Novel Noise Filtering Method

Motivation: Sources of uncontrolled noise strongly influence data analysis in microarray studies, yet signal/noise ratios are rarely considered in microarray data analyses. We hypothesized that different research projects would have different sources and levels of confounding noise, and built an interactive visual analysis tool to test and define parameters in Affymetrix analyses that optimize the ratio of signal (desired biological variable) versus noise (confounding uncontrolled variables). Results: Five probe set algorithms were studied with and without statistical weighting of probe sets using Microarray Suite (MAS) 5.0 probe set detection p values. The signal/noise optimization method was tested in two large novel microarray datasets with different levels of confounding noise; a 105 sample U133A human muscle biopsy data set (11 groups) (extensive noise), and a 40 sample U74A inbred mouse lung data set (8 groups) (little noise). Success was measured using F-measure value of success of unsupervised clustering into appropriate biological groups (signal). We show that both probe set signal algorithm and probe set detection p-value weighting have a strong effect on signal/noise ratios, and that the different methods performed quite differently in the two data sets. Among the signal algorithms tested, dChip difference model with p-value weighting was the most consistent at maximizing the effect of the target biological variables on data interpretation of the two data sets. Availability: The Hierarchical Clustering Explorer 2.0 is available a