Water can have many contaminants that are harmful to humans and animals alike. Since water is so essential to life on this planet, it is important to know whether or not a water source is contaminated. However, testing water samples can be expensive. In order to make it easier to test water for contaminants, we are working to create a device to sense this contamination within a matter of hours by using yeast that produce a fluorescent protein in the presence of various water contaminants. In order to make the yeast easier to transport, we first need to dry the yeast before packaging it, similar to dry yeast that can be bought at the grocery store. To test a sample, the device utilizes a light to frequency sensor to convert the intensity of light from the glowing yeast into a signal that can be processed by an arduino board contained within the device. Our device can be used as a reusable, low cost diagnostics machine for testing water for contamination. This could provide access to advanced technology in parts of the world that don’t have access to expensive testing equipment, as well as  be used by an ecologist in the field.

Water pollution is a serious problem that not only threatens wildlife, but also the health and safety of human populations around the world. While the commonly held perception is that water pollution only affects vulnerable groups in developing countries, the 2014 water crisis in Flint, Michigan is a stark reminder that even developed nations are not impervious. Given the scope and weight of this problem, there has yet to be an accurate and affordable way to reliably test for heavy metals and other water-soluble contaminants. Current solutions such as handheld digital instruments can be expensive and require calibration and electricity, making them less practical in low-resource settings. Test strips, while deployable, are often inaccurate. Hence, our aim is to create an inexpensive, deployable yeast-based diagnostic tool that can detect various water pollutants with high sensitivity and report results with a fluorescent or color output. At the beginning stages of our project, we are using Next-Generation RNA Sequencing and experimenting with various analysis techniques to screen for genes in Saccharomyces cerevisiae that exhibit a unique and differential expression profile after exposure to a particular contaminant. For now, we are limiting the scope of our research to zinc, copper sulfate and caffeine because there are existing gene sets in literature for us to reference, but as more data are acquired, we hope to apply this pipeline to a wider range of chemical pollutants, including pesticides, toxins and hormones.

Our current project is one stage in a larger research goal: to build multiple yeast strains capable of detecting toxic water contaminants. We searched for the optimal contamination concentrations for yeast response to obtain robust, accurate RNA sequencing analysis results. This data will then be used to determine which contaminants elicit a strong enough genetic response for promoter attachment. The fluorescent yeast will emit a recognizable signal in this manner, and allow for reliable water assessment without the use of expensive equipment or testing services. Thus, we investigated the optimal times for RNA extraction, when the cells are expressing the most contaminant-induced genes but are still sustaining metabolic processes. To this end, we must consider factors such as contaminant concentration and time after exposure. At this stage, data collection involves the use of a plate reader, which periodically measures absorbance of the yeast solution. Absorbance, which is proportional to population density, is limited by the solution’s carrying capacity. We then generate population trend graphs and identify the greatest instantaneous growth time in each sample. Through repeated experimentation, we compile a spectrum of contaminant types, concentrations, and yeast carrying capacities. Analysis of these growth curves indicate the time of incubation at which RNA expression is ideal for sequencing. Future work will involve identifying additional contaminants of interest and additional modification of our algorithms to obtain more precise, accurate, and meaningful data.

The Aortic Dissection Calculator, focuses on applying demographics, various genetic risk factors, and Computational Fluid Dynamics(CFD) to each patient case to create a predictive model of when a patient’s dissection will become problematic, requiring intervention. Risk factors include: genetic diseases, lifestyle choices, and demographic information. The necessity of this research lies within the ability to change the course of an otherwise dismal outcome by identifying patients at risk of this condition, and provides features that will help reduce the risk of death related to type B aortic dissections (TBAD). This project will be able to provide a more detailed timeline and better treatment plan to allow preventative measures and decrease the likelihood of death. CT scans of patients with TBAD are used as a building block for the construction of the 3-D vessel analysis portion of the project. These cases are imported into VMTK (Vessel Modeling Tool Kit) where a rough draft of patients’ aorta is created by a semi-automatic vessel creation software. Once this rough vessel has been created and exported, the case is moved into Slicer to be cleaned up. SolidWorks, a fluid modeling software, is utilized to model blood flow from the heart. Each patient has a unique pulse rate and blood pressure, thus by applying these specific constraints to the modeling software it produces forces identical to those found in the patient’s body. The material collected from multiple trials is analyzed to discover where the vessel is the weakest and where the dissection is most likely to occur based upon growth over time. These simulations allow us to test the wear and tear on the aorta over time. Tests for failure will be used in the predictive modeling of the calculator.