Adaptive Capacity to Extreme Heat: Results from a Household Survey in Houston, Texas

Extreme heat is the leading cause of weather-related mortality in the United States, suggesting the necessity for better understanding population vulnerability to extreme heat. The work presented here is part of a larger study examining vulnerability to extreme heat in current and future climates [System for Integrated Modeling of Metropolitan Extreme Heat Risk (SIMMER)] and was undertaken to assess Houston, Texas, residents\u27 adaptive capacity to extreme heat. A comprehensive, semistructured survey was conducted by telephone at 901 households in Houston in 2011. Frequency and logistic regression analyses were conducted. Results show that 20% of the survey respondents reported heat-related symptoms in the summer of 2011 despite widespread air conditioning availability throughout Houston. Of those reporting heat-related symptoms experienced in the home ( n = 56), the majority could not afford to use air conditioning because of the high cost of electricity. This research highlights the efficacy of community-based surveys to better understand adaptive capacity at the household level; this survey contextualizes population vulnerability and identifies more targeted intervention strategies and adaptation actions

Rigid Interferon-α Subtype Responses of Human Plasmacytoid Dendritic Cells

The large family of human type I interferon (IFN) includes 13 distinct subtypes of IFN-α, all utilizing a single type I IFN receptor. Many viruses have created evasion strategies to disable this cytokine family, highlighting their importance in antiviral defense. It is unclear what advantage the presence of so many different IFN-α subtypes provides, but functional differences observed among individual IFN-α subtypes suggested that they might play distinct regulatory roles during an immune response. To determine whether IFN-α subtype responses differ depending on a particular type of insult and thus whether IFN-α subtype responses are flexible to adapt to distinct pathogen challenges, we developed a novel nested multiplex reverse transcriptase polymerase chain reaction assay with which we measured expression of all IFN-α subtypes by freshly isolated human plasmacytoid dendritic cells (pDCs), a main source of IFN-α following pathogen challenge. Collectively our data show a remarkable stability in the relative magnitude and the kinetics of induction for each IFN-α subtype produced by pDC. Although various stimuli used, A-, B- and C-class CpGs, live and heat-inactivated influenza viruses and the TLR7 agonist R837 affected the overall magnitude of the response, each IFN-α subtype was induced at statistically similar relative levels and with similar kinetics, thereby revealing a great degree of rigidity in the IFN-α response pattern of pDC. These data are most consistent with the induction of optimized ratios of IFN-α subtypes, each of which may have differing signaling properties or alternatively, a great degree of redundancy in the IFN-α response

Comparison of two-dimensional and three-dimensional iterative watershed segmentation methods in hepatic tumor volumetrics

In this work the authors compare the accuracy of two-dimensional (2D) and three-dimensional (3D) implementations of a computer-aided image segmentation method to that of physician observers (using manual outlining) for volume measurements of liver tumors visualized with diagnostic contrast-enhanced and PET∕CT-based non-contrast-enhanced (PET-CT) CT scans. The method assessed is a hybridization of the watershed method using observer-set markers with a gradient vector flow approach. This method is known as the iterative watershed segmentation (IWS) method. Initial assessments are performed using software phantoms that model a range of tumor shapes, noise levels, and noise qualities. IWS is then applied to CT image sets of patients with identified hepatic tumors and compared to the physicians’ manual outlines on the same tumors. The repeatability of the physicians’ measurements is also assessed. IWS utilizes multiple levels of segmentation performed with the use of “fuzzy regions” that could be considered part of a selected tumor. In phantom studies, the outermost volume outline for level 1 (called level 1_1 consisting of inner region plus fuzzy region) was generally the most accurate. For in vivo studies, the level 1_1 and the second outermost outline for level 2 (called level 2_2 consisting of inner region plus two fuzzy regions) typically had the smallest percent error values when compared to physician observer volume estimates. Our data indicate that allowing the operator to choose the “best result” level iteration outline from all generated outlines would likely give the more accurate volume for a given tumor rather than automatically choosing a particular level iteration outline. The preliminary in vivo results indicate that 2D-IWS is likely to be more accurate than 3D-IWS in relation to the observer volume estimates