Cancer Incidence among Former Love Canal Residents

Ba c k g r o u n d: The Love Canal was a rectangular 16-acre, 10-ft-deep chemical waste landfill situated in a residential neighborhood in Niagara Falls, New York. This seriously contaminated site came to public attention in 1978. Only one prior study examined cancer incidence in former residents of the Love Canal neighborhood (LC). Objective: In this study we aimed to describe cancer incidence in former LC residents from 1979 to 1996 and to investigate whether it differs from that of New York State (NYS) and Niagara County (NC). Me t h o d s: From 1978 to 1982, we interviewed 6,181 former residents, and 5,052 were eligible to be included in this study. In 1996, we identified 304 cancer diagnoses in this cohort using the NYS Cancer Registry. We compared LC cancer incidence with that of NYS and NC using standardized incidence ratios (SIRs), and we compared risks within the LC group by potential exposure to the landfill using survival analysis. Res u l t s: SIRs were elevated for cancers of the bladder [SIR NYS = 1.44; 95 % confidence interval (CI), 0.91–2.16] and kidney (SIR NYS = 1.48; 95 % CI, 0.76–2.58). Although CIs included 1.00, other studies have linked these cancers to chemicals similar to those found at Love Canal. We also found higher rates of bladder cancer among residents exposed as children, based on two cases. Co n c l u s i o n s: In explaining these excess risks, the role of exposure to the landfill is unclear given such limitations as a relatively small and incomplete study cohort, imprecise exposure measurements, and the exclusion of cancers diagnosed before 1979. Given the relatively young age of the cohort, further surveillance is warranted. Key w o r d s: cancer, community health, exposure assessment, hazardous waste sites, Love Canal. Environ Health Perspect 117:1265–1271 (2009). doi:10.1289/ehp.0800153 available vi

How to find simple and accurate rules for viral protease cleavage specificities

<p>Abstract</p> <p>Background</p> <p>Proteases of human pathogens are becoming increasingly important drug targets, hence it is necessary to understand their substrate specificity and to interpret this knowledge in practically useful ways. New methods are being developed that produce large amounts of cleavage information for individual proteases and some have been applied to extract cleavage rules from data. However, the hitherto proposed methods for extracting rules have been neither easy to understand nor very accurate. To be practically useful, cleavage rules should be accurate, compact, and expressed in an easily understandable way.</p> <p>Results</p> <p>A new method is presented for producing cleavage rules for viral proteases with seemingly complex cleavage profiles. The method is based on orthogonal search-based rule extraction (OSRE) combined with spectral clustering. It is demonstrated on substrate data sets for human immunodeficiency virus type 1 (HIV-1) protease and hepatitis C (HCV) NS3/4A protease, showing excellent prediction performance for both HIV-1 cleavage and HCV NS3/4A cleavage, agreeing with observed HCV genotype differences. New cleavage rules (consensus sequences) are suggested for HIV-1 and HCV NS3/4A cleavages. The practical usability of the method is also demonstrated by using it to predict the location of an internal cleavage site in the HCV NS3 protease and to correct the location of a previously reported internal cleavage site in the HCV NS3 protease. The method is fast to converge and yields accurate rules, on par with previous results for HIV-1 protease and better than previous state-of-the-art for HCV NS3/4A protease. Moreover, the rules are fewer and simpler than previously obtained with rule extraction methods.</p> <p>Conclusion</p> <p>A rule extraction methodology by searching for multivariate low-order predicates yields results that significantly outperform existing rule bases on out-of-sample data, but are more transparent to expert users. The approach yields rules that are easy to use and useful for interpreting experimental data.</p

Thermodynamics-Based Models of Transcriptional Regulation by Enhancers: The Roles of Synergistic Activation, Cooperative Binding and Short-Range Repression

Quantitative models of cis-regulatory activity have the potential to improve our mechanistic understanding of transcriptional regulation. However, the few models available today have been based on simplistic assumptions about the sequences being modeled, or heuristic approximations of the underlying regulatory mechanisms. We have developed a thermodynamics-based model to predict gene expression driven by any DNA sequence, as a function of transcription factor concentrations and their DNA-binding specificities. It uses statistical thermodynamics theory to model not only protein-DNA interaction, but also the effect of DNA-bound activators and repressors on gene expression. In addition, the model incorporates mechanistic features such as synergistic effect of multiple activators, short range repression, and cooperativity in transcription factor-DNA binding, allowing us to systematically evaluate the significance of these features in the context of available expression data. Using this model on segmentation-related enhancers in Drosophila, we find that transcriptional synergy due to simultaneous action of multiple activators helps explain the data beyond what can be explained by cooperative DNA-binding alone. We find clear support for the phenomenon of short-range repression, where repressors do not directly interact with the basal transcriptional machinery. We also find that the binding sites contributing to an enhancer's function may not be conserved during evolution, and a noticeable fraction of these undergo lineage-specific changes. Our implementation of the model, called GEMSTAT, is the first publicly available program for simultaneously modeling the regulatory activities of a given set of sequences