Prevalence of Antimicrobial Resistant from Sinking Creek in Northeast Tennessee.

Antibiotic resistance (AR) is a critical global health threat exacerbated by complex human-animal-environment interactions. Aquatic environments, particularly surface water systems, can serve as reservoirs and transmission routes for AR bacteria. This study investigated the prevalence of AR in Sinking Creek, a pathogen-impacted creek in Northeast Tennessee.

Canonical Variable Selection for Ecological Modeling of Fecal Indicators.

More than 270,000 km of rivers and streams are impaired due to fecal pathogens, creating an economic and public health burden. Fecal indicator organisms such as are used to determine if surface waters are pathogen impaired, but they fail to identify human health risks, provide source information, or have unique fate and transport processes. Statistical and machine learning models can be used to overcome some of these weaknesses, including identifying ecological mechanisms influencing fecal pollution.

Maxent estimation of aquatic stream impairment.

Background: The leading cause of surface water impairment in United States' rivers and streams is pathogen contamination. Although use of fecal indicators has reduced human health risk, current approaches to identify and reduce exposure can be improved. One important knowledge gap within exposure assessment is characterization of complex fate and transport processes of fecal pollution.

Atmospheric influences on water quality: a simulation of nutrient loading for the Pearl River Basin, USA.

Knowledge of water quality conditions is essential in assessing the health of riverine ecosystems. The goal of this study is to determine the degree to which water quality variables are related to precipitation and air temperature conditions for a segment of the Pearl River Basin near Bogalusa, LA, USA. The AQUATOX ecological fate simulation model is used to estimate daily total nitrogen, total phosphorus, and dissolved oxygen concentrations over a 2-year period.

Modeling the potential distribution of Bacillus anthracis under multiple climate change scenarios for Kazakhstan.

Anthrax, caused by the bacterium Bacillus anthracis, is a zoonotic disease that persists throughout much of the world in livestock, wildlife, and secondarily infects humans. This is true across much of Central Asia, and particularly the Steppe region, including Kazakhstan. This study employed the Genetic Algorithm for Rule-set Prediction (GARP) to model the current and future geographic distribution of Bacillus anthracis in Kazakhstan based on the A2 and B2 IPCC SRES climate change scenarios using a 5-variable data set at 55 km(2) and 8 km(2) and a 6-variable BioClim data set at 8 km(2).