Arthur J Lembo | PubMed Health

PubMed2006

Molecular characterization of Listeria monocytogenes from natural and urban environments.

Characterization of 80 Listeria monocytogenes isolates from urban and natural environments differentiated 7 and 26 EcoRI ribotypes, respectively. Whereas the majority of isolates from the natural environment represented L. monocytogenes lineage II (12 of 13 isolates), urban isolates grouped evenly into lineages I and II (32 and 33 isolates, respectively) and included two lineage III isolates. Multilocus sequence typing of all natural isolates and a randomly selected subset of 30 urban isolates showed a higher overall diversity (Simpson index of discrimination [D] of 0.987 and 0.920, respectively) than did EcoRI ribotyping (D = 0.872 and 0.911, respectively). Combined analysis with ribotype and lineage data for 414 isolates from farm sources, 165 isolates from foods and food-processing environments, and 342 human clinical isolates revealed that lineage I was significantly more common among human (P < 0.0001) isolates, whereas lineage II was more common among isolates from the natural environment, farms, and foods (P < or = 0.05). Among a total of 92 ribotypes, 31 showed significant associations with specific isolate sources. One ribotype (DUP-1039C) was significantly associated with both natural environments and farms. A spatial analysis showed a marginal association between locations in the natural environment positive for L. monocytogenes and a proximity to farms. Our data indicate that (i) L. monocytogenes strains from different sources show a high level of diversity; (ii) L. monocytogenes subtypes differ significantly in their associations with different environments, even though populations overlap; and (iii) a higher proportion of isolates from environmental sources than from human clinical cases can be classified into L. monocytogenes lineage II, which supports the classification of this lineage as an environmentally adapted subgroup.

Colony Count, Microbial↗

PubMed2005

Identifying hydrologically sensitive areas: bridging the gap between science and application.

Researchers have noted that current water quality protection strategies, like nutrient management plans, lack a sound hydrological underpinning for pollutant transport processes. This is especially true for areas like the northeastern U.S. where copious research has shown that variable source area hydrology largely governs runoff generation. The goal of this study was to develop a scientifically justified method to identify the locations that generate overland flow. Furthermore, this methodology must be computationally simple enough that it can be utilized or incorporated into nutrient management plans and other established water quality tools. We specifically tested the reliability of the 'distance from a stream,'D(s), and the 'topographic index,'lambda, to predict areas with a high propensity for generating overland flow, i.e. hydrologically sensitive areas (HSA). HSAs were defined by their probability of generating runoff, P(sat), based on 30 year simulations using a physically based hydrological model. Using GIS, each location's P(sat) was correlated with D(s) and lambda. We used three Delaware Co., NY watersheds in the New York City watershed system with areas varying in size from 1.6 to 37 km2 and with forested and agricultural land uses. The topographic index gave stronger, more regionally consistent correlations with P(sat) than did D(s). Equations correlating lambda and P(sat) for each month are presented and can be used to estimate hydrological sensitivity in the region surrounding our study watersheds, i.e. in Delaware Co. This work is currently being incorporated into an Internet Mapping System to facilitate user-friendly, on-line identification of HSAs.

Conservation of Natural Resources↗