Large-bodied mammals play crucial roles in ecosystems. They create habitats, serve as prey, help plants thrive, and even influence how wildfires burn. But now, fewer than half of the large mammal species that were alive 50,000 years ago exist today, and those that remain are threatened with extinction from intensifying climate change and human activities.

While mammal extinctions are well-documented, very little research has explored the impact those losses had on the nuanced ways in which mammal communities interact with their environments. Researchers at the Georgia Institute of Technology are using a novel methodology to investigate how mammals’ ability to function in their environments has been threatened in the past, and what challenges they can expect to face in the future.

Jenny McGuire, associate professor in the School of Biological Sciences and leader of the Spatial Ecology and Paleontology Lab, and Daniel Lauer, a graduate student, looked millions of years into the past, observing how and why eastern African herbivores’ relationships with their environments changed across space and time in the face of biodiversity loss. They used a novel approach to build models that show how specific mammal traits — like body mass and tooth shape — evolved with their changing environments over time, revealing the factors that caused the biodiversity losses and how the losses affected the functioning of mammal communities. Their method offers a new strategy for investigating the implications of changing ecologies and prioritizing conservation efforts toward helping mammal communities flourish in the future.

Their research paper was published in the journal Nature Communications.

Combing the Data

The researchers began by diving into a collection of data from 186 sites across eastern Africa. The data contained records of over 200 extinct and 48 modern herbivore species (including the African elephant, giraffe, and hippopotamus), showing where and when each species lived at a given point in time over the past 7.4 million years. The data showed that mammal biodiversity in eastern Africa began to decline around 5 million years ago. It also revealed that aspects of biodiversity decline happened at multiple points, and that extinctions coincided with environmental changes and the emergence of early humans. But McGuire and Lauer wanted to know more.

“We wondered what we would find if we investigated how the mammals’ physical traits changed as their environments changed over time, rather than just looking at patterns in their biodiversity,” Lauer said. “This is important because if a mammal species possesses traits that are well-suited to its environment, it’s better able to contribute to the functioning of that environment. But if that is not the case, environments may not function as well as they could.”

To paint a fuller picture, they needed to examine biodiversity from a different perspective. This required a fresh approach, which led them to adapting a methodology known as ecometrics.

Ecometrics is an approach that looks at the relationships between the environmental conditions where animal communities are found — such as weather and vegetation — and the animal’s functional traits, which are traits that affect its biological performance. The team chose to focus on three traits: body mass, tooth height, and loph count (the number of ridges on molars).

Each of these traits exhibits a relationship based on the degree to which an environment is dominated by grasses versus woody plants. For example, if a species has a taller tooth, it can more durably consume the abrasive grassy vegetation of grasslands. With a shorter tooth, a species is instead suited to consume softer, woody vegetation, like shrubs.

For each of the three traits, they built a model of trait-environment relationships. They used trait data to estimate what the surrounding vegetation was like in each mammal community over time, specifically the percentage of trees and shrubs versus grassland.

“Using our models, we were able to use information about the traits occurring within mammal communities to estimate how the surrounding vegetation looked,” Lauer said. “Because these communities existed at different points in time, this enabled us to observe how consistent the mammals’ relationships with their environments remained through time.”

Analyzing Disruptions

Using their ecometric framework, the researchers uncovered a key difference between the mammal biodiversity declines that occurred before approximately 1.7 million years ago and those that occurred after. While biodiversity began declining around 5 million years ago, trait-environment relationships remained consistent despite that loss.

Their analysis demonstrated that earlier biodiversity losses were a result of species adapting to grassland environments or tracking their preferred environments across geographies. In short, those biodiversity losses didn't necessarily have any sort of negative impact on the ability of mammal communities to function properly in their environments.

But later, around 1.7 million years ago, when climates became more arid and variable and tree cover declined to below 35%, a major shift occurred. Rapid losses in the number and variety of species occurred, along with a significant disruption in trait-environment relationships. The researchers’ findings suggest that, unlike prior biodiversity losses, those occurring over the past 1.7 million years likely threatened the ability for many mammal species to function well in local environmental conditions.

“Our findings fascinated us, because we were able to differentiate between the different biodiversity losses that were happening and their implications,” Lauer said. “This work reinforces the idea that not all biodiversity losses are the same.”

Protecting the Vulnerable

Their findings have important implications for the types of environmental and climatic changes that could affect mammals going forward. In the past, when changes were gradual and wildlife were able to move freely on the landscape, they could readily adapt to these environmental conditions.

Now, fragmentation of wildlife habitats by fences, roadways, and cities has the potential to limit the ability of wildlife to adapt to the rapid environmental changes occurring today. That is exacerbated by both the fast pace and increasing variability of today’s climate, which puts animals at risk of losing their ability to function properly in their local environments.

Moving forward, the team’s analysis can shed light on which mammal communities should be prioritized for future conservation efforts. The study demonstrates that among all the communities that are experiencing biodiversity losses, priority should be given to those most at-risk — the communities for whom future biodiversity losses will profoundly affect their ability to function properly.

“By examining the past, we can get a remarkably clear understanding of how animals have responded to prior environmental changes,” McGuire said. “We plan to work with conservation practitioners to use our findings to develop well-informed strategies for conserving the most at-risk mammal communities.”

***

Co-authors include A. Michelle Lawing (Texas A&M University), Rachel A. Short (South Dakota State University), Fredrick K. Manthi (National Museums of Kenya), Johannes Müller (Leibniz Institute for Evolution and Biodiversity Science), and Jason J. Head (University of Cambridge).

Citation: Lauer, D.A., Lawing, A.M., Short, R.A. et al. Disruption of trait-environment relationships in African megafauna occurred in the middle Pleistocene. Nat Commun 14, 4016 (2023).

DOI: https://doi.org/10.1038/s41467-023-39480-8

Funding: This work was completed as part of a collaborative initiative from NSFDEB-NERC, with funding from NSF 2124836 to A.M.L., F.K.M., and J.M.; NSF 2124770 to J.L.M.; and NERC NE/W007576/1 to J.J.H. R.A.S. was supported by the NSF Postdoctoral Research Fellowships in Biology Program under grant DBI 2010680 and the USDA NIFA Hatch project SD00H787-23 (7004129 and 7004187). J.L.M. was also funded through NSF-CAREER and NSF 1945013.

The sixth mass extinction is currently happening on Earth. Rapid biodiversity loss is affecting every corner of the globe, as species of plants, mammals, fish, and reptiles disappear due to the changing climate. While much of the climate crisis and biodiversity loss looks grim, a group of researchers has recently highlighted some of the newest tools being used to address it.

Scientists at the Georgia Institute of Technology and Max Planck Institute for Intelligent Systems in Stuttgart have published a perspectives piece on the different tools used throughout the world that are aiding in the conservation of wildlife and biodiversity.

They highlight advances in technology, including both hardware and software, as well as frugal resources that are changing the way animals are protected. The research was published in the Journal of The Royal Society Interface in August.  

“We are experiencing technological advancements of low-cost hardware, open-source software, machine learning, and more that can help with global conservation efforts,” said Andrew Schulz, postdoctoral researcher in the haptic intelligence department at Max Planck Institute and recent Ph.D. graduate from the George W. Woodruff School of Mechanical Engineering. “For researchers and people interested in learning about the ways conservation technology and tools are created, this piece serves as a starter guide to the field.”

In the article, the researchers presented five case studies of conservation tools, including open-source innovation, environmental DNA, computer vision, game theory and optimization, and frugal technology. Researchers also highlighted the importance of indigenous design in these conservation tool interventions and warned not to employ toxic practices, such as colonization of conservation or parasitic conservation. These practices take advantage of native lands, where conservationists refuse to work with local or indigenous populations and often do not cite or credit their help or expertise.    

One case study looked at AudioMoth, a device that allows low-cost access to bioacoustics research. Recently, an AudioMoth was paired with an animal observation tower to track bird migrations over Georgia Tech’s campus. AudioMoth can also monitor aquatic environments, like coral colonies, to assist with species identification and habitat restoration. It’s used in a wide range of fields to monitor the biodiversity of a habitat or even help with the early detection of poachers to prevent wildlife decline.

“One of the best parts about this project was working with so many excellent researchers,” Schulz said. They included Suzanne Stathatos from Caltech and the project’s co-leaders, Cassie Shriver and Benjamin Seleb, from Georgia Tech’s quantitative biosciences Ph.D. program. “As early-career researchers working together, it is great to see that the conversations about conservation tool construction are growing and being led by outstanding Ph.D. students.”

At Georgia Tech, conservation tools are constantly being built and implemented. The Tech4Wildlife student organization is working to implement conservation tech solutions, including a rabies dispenser for our campus foxes, bird monitors in the EcoCommons, and forage feeders for Zoo Atlanta’s gorillas.

"I'm proud to see Cassie, Ben, and Andrew collaborating across fields and institutions to move conservation technology forward, and it inspires me about the future of conservation science,” said William Ratcliff, associate professor in the School of Biological Sciences and director of the quantitative biosciences program.

CITATION: Conservation tools: the next generation of engineering–biology collaborations Andrew K. Schulz., Cassie Shriver, Suzanne Stathatos, and Benjamin Seleb et. Al, Journal of The Royal Society InterfaceVolume 20, Issue 205. Published:16 August 2023. https://doi.org/10.1098/rsif.2023.0232

It is valuable and rare to have someone care to invest their time and invest in you early in your career. Breanna Shi, a Ph.D. student in bioinformatics, was lucky to have had many inspiring mentors throughout her college career. Shi’s experience with mentors inspired her to pursue mentorship.  

“Being a mentor is my favorite part of my work,” said Shi. “I have learned so much about student psychology and my own psychology. As scientists, we can neglect the human experience it takes for us all to collaborate. I love thinking of new ways to improve the effectiveness of our communication so we all feel welcomed and valued in our scientific communities.”  

In 2022, Shi started a mentorship group, FishStalkers, which grew from five to 20 members in just one semester. Shi’s mentees have been offered competitive co-ops and internships, awarded prestigious fellowships, presented at research symposiums, and more.  

Shi provides her techniques for cultivating a positive and productive mentor-mentee connection. 

Instill confidence in your mentees. “Student researchers have a lot of helpful ideas,” said Shi. “They attend courses where they learn about the newest software and theories while you are held up in the lab. You need to try and access this information, but it’s not going to happen if you do not instill the confidence in them that their idea is worth your time, and that it’s okay if the idea doesn’t work out because the contribution is valuable.”  

• Shi’s tips for instilling confidence include:  
  • Refer to mentees as “researcher” or “student researcher” to dissipate internal separations between undergraduates, master’s, and Ph.D. students working in the lab. 
  • Tell your mentees when they have taught you something new and when their work has gone above and beyond. 
  • Support mentees in pursuing their own goals to recognize their personhood. 

Lower the standards you set for yourself. “Most Ph.D. students are perfectionists, and they will put a lot of pressure on themselves in terms of responsibility to a mentee,” said Shi. “You don’t need to be perfect. In fact, if you are perfect around your mentees, you will probably just intimidate them.” 

According to Shi, this pressure can deter Ph.D. students from pursuing mentorship.  

“A lot of people will place barriers on themselves that they do not know enough, or they don’t have enough ‘good work’ for a mentee,” said Shi. “You will make mistakes as a mentor. You and your mentees as people will solve these miscommunications or issues. This is normal and healthy.”  

Humanize yourself. “Mentees often have an idealized perception of what a Ph.D. student is,” said Shi. “I will point out mistakes I have made in my work to students and encourage them to correct me if they have better information. I do not want to feel smart. I want to do good work and that requires criticism from other parties, including my mentees. Our goal is to increase the comfortability of the mentee while maintaining the professional boundary required of your role.”  

Facilitate situations where the mentee is empowered. “The important thing I focus on with my students is cross-training,” said Shi. “If one mentee has studied a software, they now become responsible for training other mentees and me. It helps to be intentional in teaching your mentees that knowledge can come from anyone. I think putting knowledge into a hierarchy is overblown and only serves to preserve the status of people at the top rather than allowing for new ideas.”  

Align mentor and mentee goals. “Goals should not conflict with one another, but this can happen if the mentor does not plan strategically,” said Shi. “The mentor needs to be transparent with what work the mentee needs to complete and the timeline. The mentor should inform the mentee of the amount of time the mentor has to assist the mentee and the appropriate method for contacting you when you need help. It is always best practice to be as specific with what you want rather than assume some ‘should know’ something.” 

Shi has created a mentorship document that outlines her expectations for all new student researchers.  

Communicate expectations. “We should communicate with each other the experience that we want from the relationship and work towards that goal,” said Shi. “You should align your students’ projects such that they are working towards something that advances your work. Sometimes, you will have motivated students who want to go off and do their own idea. That shows initiative in the student, but you should be direct with them that straying off into projects unrelated to your current research goals will mean that they will receive less oversight/feedback from you.” 

Provide positive feedback. “A lot of us analytical types may forget that we should point out tasks that are proceeding well along with the things that are going up in flames,” said Shi. “Recognizing quality mentee work is vital to them reproducing that quality of work again. They need to know when they have met your standards.” 

Provide critical feedback. “You will need to provide critical feedback to the mentee both on work and logistical miscommunications,” said Shi. “Do not shy away from this. If you are uncomfortable with discussing concerns on performance, this is normal, but by ignoring the issue you will deny the mentee from improving in this respect.” 

Shi’s procedure for handling performance issues involves gathering the facts, detangling your emotions, defining the solution, and sending them a message.  

For logistical, non-research issues, Shi recommends keeping records.  

“There is a lot of front-loaded work in creating documentation of expectations, but it really pays off in terms of not dealing with day-to-day logistical questions.” 

Understand the student researcher’s mindset. “Student researchers often feel insecure in navigating the lab equipment,” said Shi. “Sometimes, their perfectionism will cause them to ask you a lot of questions because they really want to impress you and do things correctly.”  

In these situations, Shi advises mentors to protect their own time while reassuring the mentee in their work. Let them know that you appreciate their effort to do things correctly, but part of research is independence, or let them know that you are unavailable to answer their question and provide a timeline for when they can expect to hear from you.  

Take the Tech to Teaching program and try your best! “I highly recommend this [Tech to Teaching] program to any Ph.D. student who has long-term goals of becoming a professor,” said Shi. “I want to emphasize something: you do not need formal training to be a mentor. If you are on the fence, try your best. You will learn the most about being a mentor by being a mentor. Listen to your mentee, balance your commitments, prioritize your time and goals, and you will be fine. There is the perception some people have that you need to mentor in a specific way. I do not agree with this mentality. I believe the scope of mentorship should be negotiated by the mentor and the mentee based on an alignment of goals.”  

It is valuable and rare to have someone care to invest their time and invest in you early in your career. Breanna Shi, a Ph.D. student in bioinformatics, was lucky to have had many inspiring mentors throughout her college career. Shi’s experience with mentors inspired her to pursue mentorship.  

“Being a mentor is my favorite part of my work,” said Shi. “I have learned so much about student psychology and my own psychology. As scientists, we can neglect the human experience it takes for us all to collaborate. I love thinking of new ways to improve the effectiveness of our communication so we all feel welcomed and valued in our scientific communities.”  

In 2022, Shi started a mentorship group, FishStalkers, which grew from five to 20 members in just one semester. Shi’s mentees have been offered competitive co-ops and internships, awarded prestigious fellowships, presented at research symposiums, and more.  

Shi provides her techniques for cultivating a positive and productive mentor-mentee connection. 

Instill confidence in your mentees. “Student researchers have a lot of helpful ideas,” said Shi. “They attend courses where they learn about the newest software and theories while you are held up in the lab. You need to try and access this information, but it’s not going to happen if you do not instill the confidence in them that their idea is worth your time, and that it’s okay if the idea doesn’t work out because the contribution is valuable.”  

• Shi’s tips for instilling confidence include:  
  • Refer to mentees as “researcher” or “student researcher” to dissipate internal separations between undergraduates, master’s, and Ph.D. students working in the lab. 
  • Tell your mentees when they have taught you something new and when their work has gone above and beyond. 
  • Support mentees in pursuing their own goals to recognize their personhood. 

Lower the standards you set for yourself. “Most Ph.D. students are perfectionists, and they will put a lot of pressure on themselves in terms of responsibility to a mentee,” said Shi. “You don’t need to be perfect. In fact, if you are perfect around your mentees, you will probably just intimidate them.” 

According to Shi, this pressure can deter Ph.D. students from pursuing mentorship.  

“A lot of people will place barriers on themselves that they do not know enough, or they don’t have enough ‘good work’ for a mentee,” said Shi. “You will make mistakes as a mentor. You and your mentees as people will solve these miscommunications or issues. This is normal and healthy.”  

Humanize yourself. “Mentees often have an idealized perception of what a Ph.D. student is,” said Shi. “I will point out mistakes I have made in my work to students and encourage them to correct me if they have better information. I do not want to feel smart. I want to do good work and that requires criticism from other parties, including my mentees. Our goal is to increase the comfortability of the mentee while maintaining the professional boundary required of your role.”  

Facilitate situations where the mentee is empowered. “The important thing I focus on with my students is cross-training,” said Shi. “If one mentee has studied a software, they now become responsible for training other mentees and me. It helps to be intentional in teaching your mentees that knowledge can come from anyone. I think putting knowledge into a hierarchy is overblown and only serves to preserve the status of people at the top rather than allowing for new ideas.”  

Align mentor and mentee goals. “Goals should not conflict with one another, but this can happen if the mentor does not plan strategically,” said Shi. “The mentor needs to be transparent with what work the mentee needs to complete and the timeline. The mentor should inform the mentee of the amount of time the mentor has to assist the mentee and the appropriate method for contacting you when you need help. It is always best practice to be as specific with what you want rather than assume some ‘should know’ something.” 

Shi has created a mentorship document that outlines her expectations for all new student researchers.  

Communicate expectations. “We should communicate with each other the experience that we want from the relationship and work towards that goal,” said Shi. “You should align your students’ projects such that they are working towards something that advances your work. Sometimes, you will have motivated students who want to go off and do their own idea. That shows initiative in the student, but you should be direct with them that straying off into projects unrelated to your current research goals will mean that they will receive less oversight/feedback from you.” 

Provide positive feedback. “A lot of us analytical types may forget that we should point out tasks that are proceeding well along with the things that are going up in flames,” said Shi. “Recognizing quality mentee work is vital to them reproducing that quality of work again. They need to know when they have met your standards.” 

Provide critical feedback. “You will need to provide critical feedback to the mentee both on work and logistical miscommunications,” said Shi. “Do not shy away from this. If you are uncomfortable with discussing concerns on performance, this is normal, but by ignoring the issue you will deny the mentee from improving in this respect.” 

Shi’s procedure for handling performance issues involves gathering the facts, detangling your emotions, defining the solution, and sending them a message.  

For logistical, non-research issues, Shi recommends keeping records.  

“There is a lot of front-loaded work in creating documentation of expectations, but it really pays off in terms of not dealing with day-to-day logistical questions.” 

Understand the student researcher’s mindset. “Student researchers often feel insecure in navigating the lab equipment,” said Shi. “Sometimes, their perfectionism will cause them to ask you a lot of questions because they really want to impress you and do things correctly.”  

In these situations, Shi advises mentors to protect their own time while reassuring the mentee in their work. Let them know that you appreciate their effort to do things correctly, but part of research is independence, or let them know that you are unavailable to answer their question and provide a timeline for when they can expect to hear from you.  

Take the Tech to Teaching program and try your best! “I highly recommend this [Tech to Teaching] program to any Ph.D. student who has long-term goals of becoming a professor,” said Shi. “I want to emphasize something: you do not need formal training to be a mentor. If you are on the fence, try your best. You will learn the most about being a mentor by being a mentor. Listen to your mentee, balance your commitments, prioritize your time and goals, and you will be fine. There is the perception some people have that you need to mentor in a specific way. I do not agree with this mentality. I believe the scope of mentorship should be negotiated by the mentor and the mentee based on an alignment of goals.”  

Today, machine learning, artificial intelligence, and algorithmic advancements made by research scientists and engineers are driving more targeted medical therapies through the power of prediction. The ability to rapidly analyze large amounts of complex data has clinicians closer to providing individualized treatments for patients, with an aim to create better outcomes through more proactive, personalized medicine and care. 

“In medicine, we need to be able to make predictions,” said John F. McDonald, professor in the School of Biological Sciences and director of the Integrated Cancer Research Center in the Petit Institute for Bioengineering and Bioscience at the Georgia Institute of Technology. One way is through understanding cause and reflect relationships, like a cancer patient’s response to drugs, he explained. The other way is through correlation. 

“In analyzing complex datasets in cancer biology, we can use machine learning, which is simply a sophisticated way to look for correlations. The advantage is that computers can look for these correlations in extremely large and complex data sets.”

Now, McDonald’s team and the Ovarian Cancer Institute are using ensemble-based machine learning algorithms to predict how patients will respond to cancer-fighting drugs with high accuracy rates. The results of their most recent work have been published in the Journal of Oncology Research .  

For the study, McDonald and his colleagues developed predictive machine learning-based models for 15 distinct cancer types, using data from 499 independent cell lines provided by the National Cancer Institute. Those models were then validated against a clinical dataset containing seven chemotherapeutic drugs, administered either singularly or in combination, to 23 ovarian cancer patients. The researchers found an overall predictive accuracy of 91%.

“While additional validation will need to be carried out using larger numbers of patients with multiple types of cancer,” McDonald noted, “our preliminary finding of 90% accuracy in the prediction of drug responses in ovarian cancer patients is extremely promising and gives me hope that the days of being able to accurately predict optimal cancer drug therapies for individual patients is in sight."

The study was conducted in collaboration with the Ovarian Cancer Institute (OCI) in Atlanta, where McDonald serves as chief research officer. Other authors are Benedict Benigno, MD (OCI founder and chief executive officer, as well as an obstetrician-gynecologist, surgeon, and oncologist); Nick Housley, a postdoctoral researcher in McDonald’s Georgia Tech lab; and the paper’s lead author, Jai Lanka, an intern with OCI. 

The challenges in predicting cancer treatments

The complex nature of cancer makes it a challenging problem when it comes to predicting drug responses, McDonald said. Patients with the same type of cancer will often respond differently to the same therapeutic treatment. 

“Part of the problem is that the cancer cell is a highly integrated network of pathways and patient tumors that display the same characteristics clinically may be quite different on the molecular level,” he explained. 

A major goal of personalized cancer medicine is to accurately predict likely responses to drug treatments based upon genomic profiles of individual patient tumors. 

“In our approach, we utilize an ensemble of machine learning methods to build predictive algorithms — based on correlations between gene expression profiles of cancer cell lines or patient tumors with previously observed responses — to a variety of cancer drugs. The future goal is that gene expression profiles of tumor biopsies can be fed into the algorithms, and likely patient responses to different drug therapies can be predicted with high accuracy,” said McDonald.   

Machine learning is already being applied to the data coming from the genomic profiles of tumor biopsies, but prior to the researchers’ work, these methods have typically involved a single algorithmic approach. 

McDonald and his team decided to combine several algorithm approaches that use multiple ways to analyze complex data; one even uses a three-dimensional approach. They found using this ensemble-based approach significantly boosted predictive accuracy.

The algorithms the team used have names like Support Vector Machines (SVM), Random Forest classifier (RF), K-Nearest Neighbor classifier (KNN), and Logistic Regression classifier (LR). 

“They’re all fairly technical, and they’re all different computational mathematical approaches, and all of them are looking for correlations,” said McDonald. “It’s just a question of which one to use, and for different data sets, we find that one model might work better than another.”

However, more patient datasets that combine genomic profiles with responses to cancer drugs are needed to advance the research.  

“If we want to have a clinical impact, we must validate our models using data from a large number of patients,” said McDonald, who added that many datasets are held by pharmaceutical companies who use them in drug development. That data is typically considered proprietary, private information. And although a significant amount of genomic data of cancer patients is generally available, it’s not typically correlated with patient responses to drugs.

McDonald is currently talking with medical insurance companies about access to relevant datasets, as well. “It costs insurance companies a significant amount of money to pay for drug treatments that don’t work,” he noted. Time, medical fees, and ultimately, many lives could be saved by providing researchers with these types of information. 

“Right now, a percentage of patients will not respond to a drug, but we don’t know that until after six weeks of chemotherapy,” said McDonald. “What we hope is that we will soon have tools that can accurately predict the probability of a patient responding to first line therapies — and if they don’t respond, to be able to make accurate predictions as to the next drug to be tried.”

Citation: Lanka J, Housley SN, Benigno BB, McDonald JF. “ELAFT: An Ensemble-based Machine-learning Algorithm that Predicts Anti-cancer Drug Responses with High Accuracy.” Journal of Oncology Research. ISSN: 2637-6148.

Funding for this research provided by the Ovarian Cancer Institute, Atlanta, Georgia; Northside Hospital (Atlanta); and The Deborah Nash Endowment Fund. John F. McDonald serves as chief research officer of the Ovarian Cancer Institute (OCI) in Atlanta.

About Georgia Tech

The Georgia Institute of Technology, or Georgia Tech, is a top 10 public research university developing leaders who advance technology and improve the human condition. The Institute offers business, computing, design, engineering, liberal arts, and sciences degrees. Its nearly 40,000 students, representing 50 states and 149 countries, study at the main campus in Atlanta, at campuses in France and China, and through distance and online learning. As a leading technological university, Georgia Tech is an engine of economic development for Georgia, the Southeast, and the nation, conducting more than $1 billion in research annually for government, industry, and society.