Lynch syndrome is the predisposition to visceral malignancies that are associated with deleterious germline mutations in DNA mismatch repair genes, including MLH1, MSH2, MSH6, and PMS2. Muir-Torre ...syndrome is a variant of Lynch syndrome that includes a predisposition to certain skin tumors. We determined the frequency of Muir-Torre syndrome among 50 Lynch syndrome families that were ascertained from a population-based series of cancer patients who were newly diagnosed with colorectal or endometrial carcinoma. Histories of Muir-Torre syndrome–associated skin tumors were documented during counseling of family members. Muir-Torre syndrome was observed in 14 (28%) of 50 families and in 14 (9.2%) of 152 individuals with Lynch syndrome. Four (44%) of nine families with MLH1 mutations had a member with Muir-Torre syndrome compared with 10 (42%) of 24 families with MSH2 mutations (P = .302). Families who carried the c.942+3A>T MSH2 gene mutation had a higher frequency of Muir-Torre syndrome than families who carried other mutations in the MSH2 gene (75% vs 25%; P = .026). Muir-Torre syndrome was not found in families with mutations in the MSH6 or PMS2 genes. Our results suggest that Muir-Torre syndrome is simply a variant of Lynch syndrome. Screening for Muir-Torre syndrome–associated skin lesions among patients with Lynch syndrome is recommended.
Comprehensive and spatially accurate poultry population demographic data do not currently exist in the United States; however, these data are critically needed to adequately prepare for, and ...efficiently respond to and manage disease outbreaks. In response to absence of these data, this study developed a national-level poultry population dataset by using a novel combination of remote sensing and probabilistic modelling methodologies. The Farm Location and Agricultural Production Simulator (FLAPS) (Burdett et al., 2015) was used to provide baseline national-scale data depicting the simulated locations and populations of individual poultry operations. Remote sensing methods (identification using aerial imagery) were used to identify actual locations of buildings having the characteristic size and shape of commercial poultry barns. This approach was applied to 594 U.S. counties with > 100,000 birds in 34 states based on the 2012 U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Census of Agriculture (CoA). The two methods were integrated in a hybrid approach to develop an automated machine learning process to locate commercial poultry operations and predict the number and type of poultry for each operation across the coterminous United States. Validation illustrated that the hybrid model had higher locational accuracy and more realistic distribution and density patterns when compared to purely simulated data. The resulting national poultry population dataset has significant potential for application in animal disease spread modelling, surveillance, emergency planning and response, economics, and other fields, providing a versatile asset for further agricultural research.
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