Over the last several decades, concern has increased over the effects of chronic low-level exposure of aquatic wildlife to endocrine disrupting compounds (EDCs). In aquatic systems, EDCs are potent at trace concentrations (e.g., <100 ng/L) and can enter an organism via multiple pathways. Following exposure, EDCs are typically nonlethal but can elicit a variety of adverse effects on the body (e.g., reduced immune function, reduced egg production, skewed sex ratios). Therefore, many EDC exposure can result in population-level declines resulting from decreased reproductive performance. As a result, EDCs pose serious threats to biodiversity and ecosystem health in aquatic systems, and observations of endocrine disruption are becoming increasingly common throughout the world.
In the past, traditional ecotoxicological studies coupled with targeted chemical analyses of aquatic samples have been employed in attempts to identify EDCs in surface water bodies. These analyses, however, have largely been unsuccessful, likely due to several factors. Environmental water samples are highly complex and contain 10’s of thousands of unknown compounds, making targeted analyses challenging. Furthermore, different chemical “cocktails” could enhance or suppress the effects of other compounds, thereby making chemical patterns difficult to interpret. Therefore, experimental approaches to identifying unknown EDCs are largely infeasible.
Instead, the aim of this project is to use “big data” approaches to relate nontarget chemical data to endocrine bioassays to find the causative agents driving endocrine disruption in surface bodies of water. The student will conduct mass spec. analyses and program machine learning tools (preferably in R or Python) to tease apart the complex relationships between the chemical composition and the estrogenicity of the water samples to isolate unknown compounds driving the estrogenicity bioassay responses. Students will have access to high resolution mass spectometry instruments, state of the art computing facilites, and will receive training from the global leaders in nontarget chemical analyses.
Qualifications: Students should have completed a MS degree in math, statistics, or an environmental related field (e.g., environmental engineering, ecotoxicology). Highly successful BS students with programming experience will be considered. Preferred qualifications include programming experience or mass spectormetry experience.
This line of research is challenging, and as a result, there is a lot of progress that can be made by creative individuals who are willing to work hard and can find unorthodox solutions to problems. Therefore, all highly motivated students, regardless of national origin, age, gender, sexual orientation, or creed are encouraged to apply. For more information, please contact Dr. Jones directly (Gerrad.Jones@oregonstate.edu, https://agsci.oregonstate.edu/users/gerrad-jones).