To function your body requires small amounts of essential metals like iron and zinc. However, tiny amounts of toxic metals like mercury and arsenic can kill you. Chemically the differences between essential metals and toxicmetals are subtle, and scientists don't completely understand why toxic metals are so dangerous. In my work, I ask questions about how our body recognizes different metals and what chemistry toxic metals do that make them dangerous. Proteins are the chemicals our bodies produce to both use essential metals and capture toxic metals. As an example, I study one protein called GR-DBD that has to bind two zinc metals in order to function. Once GR-DBD has its two zincs it plays an essential role in the development of brain and blood cells. But what happens if GR-DBD comes into contact with the toxic metal arsenic? First we studied GR-DBD until we had a clear understanding of how GR-DBD joins with the zincs. Then we tried to replace the zinc with arsenic, and we found that GR-DBD won't let go of zinc to bind arsenic. This means that GR-DBD can probably still perform normally even if there's arsenic in the body. But, certain cells in the body process arsenic for excretion by methylating or tagging the arsenic. This tagged arsenic does replace zinc joined to GR-DBD causing GR-DBD to malfunction. From GR-DBD, we learned that toxic metals themselves may not cause trouble, but the cell's way of processing strange metals may actually lead to toxicity.
I research in a branch of mathematics based entirely on the study of patterns; mathematicians call it Enumerative Combinatorics. An enumerative combinatorist looks at a particular structure and will ask questions about it; most commonly, they try to count some characteristic of it. At the heart of this subject are the most basic of math questions: How many ways can you seat the guests at a party? If everyone in this room shook hands, how many handshakes would occur? I'm working with a pattern that behaves very similarly to the stock market; every day in the stock market prices go up or down. Imagine you live in a world where the probability of a stock increasing on a particular day is the same as the probability of a stock decreasing on that day. Now, look at a list of the stock prices for a certain number of consecutive days – let's say the first 5 days. This is just a list of numbers with some bigger than others. Now we can label each day as follows: the day with the smallest value will be 1, the next smallest will be 2, and so on until the biggest day which will be 5. It's these labels that I study. For one set of 5 days I could get the labels 1,3,4,2,5 and another for another I could get 4,3,5,2,1 and so on. Here is the big question I'm trying to answer: which patterns have the same probability of occurring? I don't want to only answer this question for lists with five numbers – I want to answer it for any possible length!
I remember my aunt staying in the hospital for 2 weeks when I was younger. As far as I could tell, all she had was a silver dollar-sized red spot on her elbow. I later learned that she had an infection caused by a superbug, a bacteria resistant to antibiotic treatment. I am not surprised that people always approach me with their own stories when they learn my research is focused on methicillin-resistant Staphylococcus aureus (MRSA), a superbug, because these infections are so prevalent. Before the discovery of antibiotics, Staphylococcus aureus claimed 80% of its victims. The current mortality rate is about 18%; sadly, that is about 18,000 people a year in the US, a NBA stadium filled to capacity- and the number of MRSA infections continue to increase. While we know a lot about how the bacteria become antibiotic resistant, there are still areas of the physiology, or the internal workings, of the bacteria that are shrouded in mystery. My research is focused on how the MRSA respond to certain compounds that can damage their DNA or that can pop the bacteria open. Specifically, I study a chemical produced by the bacteria which prevents the damage caused by those dangerous compounds and helps the bacteria evade attack by our immune system. My hope is that my research could lead to better treatments for MRSA infections.
I work on a new cancer treatment that involves heating up cancer to kill it. If you can raise the temperature of the tumor by just a few degrees, for about an hour, that is enough to damage it. The main problem with using heat to fight cancer is that the normal tissue around a tumor is typically just as sensitive to heat as the tumor itself. The thermal sensitivity is similar because the cancer cells are identical to normal cells with the exception of a few specific mutations, such as having an uncontrolled growth rate. Because of the similar sensitivity to heat, we have to target the heat to the tumor, and try not to heat the surrounding normal tissue. Scientists have tried using microwaves or passing a small amount of electricity through the tumor to heat it, both safe and clinically proven methods for other medical applications, but those approaches had limited success in treating cancer.
For our therapy, we inject trillions of tiny magnetic particles into the tumor. We wait a while to let the cancer cells absorb the magnets, and then we heat the magnets up using a special antenna. The radio waves from the antenna also tend to warm the skin of the patient, sort of like how a kitchen microwave warms food, but much slower, so we have to be careful to balance the tumor heating against the skin warming effect. I'm developing a computer program that can predict the increase in temperature that we will get for any particular patient on the skin, and in the tumor, so that doctors can choose the right antenna power settings and the right amount of tiny magnets to inject.
This heating technique works well in conjunction with mainstream treatments like chemotherapy and radiation therapy. Some of my coworkers are testing these combination therapies to find the best way to mix them. In my work, I specifically study how the magnets heat by running computer simulations and doing experiments.
My research focuses on understanding the processes that lead to the formation of new species of plants and animals. Species extinctions are widely discussed, but less is known about the evolutionary and environmental conditions that lead to the formation of new species. I approach the process of speciation in crickets because they are abundant and relatively easy to study. Understanding the process of speciation in crickets will provide insight into the process of speciation in larger and more complex animals that are more difficult to study. To understand speciation, I focus on species that vary regionally as well as on closely related species. By studying variation within species as well as differences among species, I am working to understand how one species diverges into multiple species.
The ice sheets of Greenland and Antarctica are of increasing interest to scientists and policy-makers studying Earth's changing climate. They also play host to permanent stations and field expeditions conducting research in astronomy, astrophysics, geology and life in extreme environments. Over-snow vehicle traverses have become common to study these ice sheets and resupply their stations. To ensure safe travel, however, routes must avoid or mitigate crevasses, which in turn requires methods to detect them.
Crevasses are vertical fractures in ice sheets caused by local stresses. Frequent snowstorms can bridge hard-packed snow across the top or mouth of the crevasse as it slowly opens, and the resulting snow bridge can disguise the presence of the underlying void. Ground penetrating radar (GPR) has been used successfully to detect hidden crevasses and other voids in ice sheets. The standard method mounts a GPR antenna in front of a lightweight tractor, and a human operator interprets the returning radar signals for evidence of underlying crevasses as the vehicle executes a survey pattern. Detection probability depends on operator skill, attentiveness, crevasse structure and GPR approach angle. Long survey hours, a short interval (3--4 s) to halt the vehicle safely, and the severe consequences of undetected crevasses place enormous demands on the GPR operator.
My research focuses on efforts to automate GPR-based crevasse detection using machine-learning algorithms operating on the incoming data stream. Recognizing the operational requirements, we select algorithms that could run in real time to complement or replace human interpretation of the same data. Our main goals are to provide reliable detection of crevasses in Polar ice sheets while improving safety and comfort of field scientists.
Last Updated: 7/31/13