Although there is a growing body of research examining public perceptions of global climate change, little work has focused on the role of place and proximity in shaping these perceptions. This study extends previous conceptual models explaining risk perception associated with global climate change by adding a spatial dimension. Specifically, Geographic Information Systems and spatial analytical techniques are used to map and measure survey respondents' physical risk associated with expected climate change. Using existing spatial data, multiple measures of climate change vulnerability are analyzed along with demographic, attitudinal, and social contextual variables derived from a representative national survey to predict variation in risk perception. Bivariate correlation and multivariate regression analyses are used to identify and explain the most important indicators shaping individual risk perception. Analysis of the data suggests that the relationship between actual and perceived risk is driven by specific types of physical conditions and experiences.

Introduction Climate change is an environmental problem with considerable social, economic, and ecological risks (Scheraga and Grambsch, 1998). The thermometric instrumental record indicates that the global average surface temperature is increasing, and is up by about 0.6 8C in the last one hundred years. Proxy data from ice cores and tree rings suggest that the`abrupt' warming trend of the 20th century is`unique' by historical standards (North, 2003). The impacts of temperature change are many, and include coastal flooding and beach erosion, extreme weather events, continental drying and drought, loss of habitat and species, decreased revenue for commercial fisheries, fluctuations in crop yields, and increased spread of vector-borne diseases

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Studies on the impacts of hurricanes, tropical storms, and tornados indicate that poor communities of colour suffer disproportionately in human death and injury.(2) Few quantitative studies have been conducted on the degree to which flood events affect socially vulnerable populations. We address this research void by analysing 832 countywide flood events in Texas from 1997-2001. Specifically, we examine whether geographic localities characterised by high percentages of socially vulnerable populations experience significantly more casualties due to flood events, adjusting for characteristics of the natural and built environment. Zero-inflated negative binomial regression models indicate that the odds of a flood casualty increase with the level of precipitation on the day of a flood event, flood duration, property damage caused by the flood, population density, and the presence of socially vulnerable populations. Odds decrease with the number of dams, the level of precipitation on the day before a recorded flood event, and the extent to which localities have enacted flood mitigation strategies. The study concludes with comments on hazard-resilient communities and protection of casualty-prone populations.

Improvements in mass spectrometry (MS)-based peptide sequencing provide a new opportunity to determine whether polymorphisms, mutations, and splice variants identified in cancer cells are translated. Herein, we apply a proteogenomic data integration tool (QUILTS) to illustrate protein variant discovery using whole genome, whole transcriptome, and global proteome datasets generated from a pair of luminal and basal-like breast-cancer-patient-derived xenografts (PDX). The sensitivity of proteogenomic analysis for singe nucleotide variant (SNV) expression and novel splice junction (NSJ) detection was probed using multiple MS/MS sample process replicates defined here as an independent tandem MS experiment using identical sample material. Despite analysis of over 30 sample process replicates, only about 10% of SNVs (somatic and germline) detected by both DNA and RNA sequencing were observed as peptides. An even smaller proportion of peptides corresponding to NSJ observed by RNA sequencing were detected (<0.1%). Peptides mapping to DNA-detected SNVs without a detectable mRNA transcript were also observed, suggesting that transcriptome coverage was incomplete (ϳ80%). In contrast to germline variants, somatic variants were less likely to be detected at the peptide level in the basal-like tumor than in the luminal tumor, raising the possibility of differential translation or protein degradation effects. In conclusion, this large-scale proteogenomic integration allowed us to determine the degree to which mutations are translated and identify gaps in sequence coverage, thereby benchmarking current technology and progress toward whole cancer proteome and transcriptome analysis. Massively parallel sequencing (MPS)1 of cancer genomes has demonstrated enormous complexity, and it is often unclear which somatic mutations drive tumor biology and which are nonfunctional passenger mutations that passively accumulate. RNA sequencing is frequently used to determine which nucleotide variants are transcribed and therefore have the potential for biological function. However, many mutations detected at the DNA level are not observed at the mRNA level, and their observation is dependent upon expression of the stability of the mRNA (1). Mutation detection at the peptide level clearly increases the confidence that any given variant is