Robin Smith, Author at Research Blog

How Changes in Lemur Brains Made Some Mean Girls Nice

On April 21, 2025

If there was a contest for biggest female bullies of the animal world, lemurs would be near the top of the list. In these distant primate cousins, it’s the ladies who call the shots, relying on physical aggression to get their way and keep males in line.

*Male and female blue-eyed black lemurs at the Duke Lemur Center. In these distant primate cousins, females get their way most of the time. Photo: David Haring.*

Not all lemur societies are built about female rule, however. In one branch of the lemur family tree, some species have evolved, within the last million years, to have a more harmonious relationship between the sexes.

Now, new findings suggest that this amiable shift in lemurs was at least partly driven by changes in the action of the “love hormone” oxytocin inside their brains. The research could shed light on how hormones influence behavior in humans and other animals.

In a study published in the journal Biology Letters, Duke University researchers studied seven closely related lemur species in the genus Eulemur, noting which ones had domineering females and which were more egalitarian.

Take blue-eyed black lemurs, for example. Females get first dibs on food and prime resting spots; smacking, biting and chasing the males to get their way.

Their behavior isn’t the fierce protectiveness of a mother defending her babies, said senior author Christine Drea, a professor of evolutionary anthropology at Duke. Aggression in these females can be entirely unprovoked, simply to remind others who’s in charge.

“Males let females have priority access to whatever they want,” Drea said.

Others species, like the collared lemurs, are more peaceful and egalitarian, with males and females sharing equal status. “It’s more of an even playing field,” said first author Allie Schrock, who earned her Ph.D. in the Drea lab.

*Collared lemurs huddle at the Duke Lemur Center. Photo: David Haring*

The lemurs in the study died of natural causes some time ago, but their tissues live on, thanks to a bank of tissues from these endangered primates kept frozen at the Duke Lemur Center. Using an imaging technique called autoradiography, the researchers mapped brain binding sites for oxytocin, a hormone involved in social behaviors like trust and bonding.

The results revealed a striking pattern.

The researchers found that the more recently evolved egalitarian species had more oxytocin receptors than the others, essentially giving them more targets for oxytocin to act on.

The key difference was in the amygdala, a region of the brain typically associated with emotions such as fear, anxiety and anger.

The pattern held up for both sexes, suggesting that egalitarian species achieved gender parity by becoming less aggressive towards others overall, rather than males ramping up their aggression to match their female counterparts, Drea said.

*In these cross-sectional images of two lemur brains, arrows show oxytocin binding in the amygdala in domineering and egalitarian species. Courtesy: Allie Schrock, Duke University*

The potential implications go beyond lemurs, the researchers said. Problems with oxytocin signaling in the brain have been linked to aggression, personality disorders and autism in humans, rodents and other animals.

Next, the researchers plan to examine links between hormone receptors and additional aspects of social behavior in lemurs, such as whether they are solitary or social.

“There’s a lot more that we can learn from lemurs about how the brain regulates behavior,” Schrock said.

CITATION: “Neuropeptide Receptor Distributions in Male and Female Eulemur Vary Between Female-Dominant and Egalitarian Species,” Allie E. Schrock, Mia R. Grossman, Nicholas M. Grebe, Annika Sharma, Sara M. Freeman, Michelle C. Palumbo, Karen L. Bales, Heather B. Patisaul and Christine M. Drea. Biology Letters, March 19, 2025. DOI:10.1098/rsbl.2024.0647

College basketball can be hard to predict. That didn’t stop these student data whizzes from trying

By Robin Smith

On April 8, 2025

In Computers/Technology, Data, Statistics

Blue Devils fans are still making sense of Duke’s stunning late-game loss to Houston in what was a wild Final Four game.

But for some fans of men’s college hoops, predicting this season’s nail-biters, closest contests — and even the biggest blowouts — was a worthwhile competition in itself.

The Blue Devils during the 2025 ACC men’s basketball regular season matchup against UNC on March 8. Credit: Duke Athletics

In the first-ever Triangle Sports Analytics competition, 15 teams of undergraduates and master’s students from Duke, UNC and NC State competed to make predictions about the 2024/25 ACC basketball season, and in a way most armchair fans don’t — using data science.

For these contestants, it wasn’t just a question of which teams would survive and advance.

The idea was to predict not only who would win, but by how much. That number is called the point spread. Anyone who wanted to compete had to submit, back in January, their point spread predictions for each of the remaining regular season games involving any one of the three Triangle teams. Everything from the razor-thin margins to the landslides. The winners were the ones whose predictions most closely mirrored the results.

They also had to come up with a confidence interval for their spreads, based on probability and the data at their disposal. In a nutshell, it’s a way to show how reliable their estimates were by saying, “we’re pretty sure the real margin of victory is somewhere between X and Y.” Teams that predicted the point spreads more accurately were given an edge.

“Predicting results weeks or months in advance is a really difficult task,” said Duke statistical science professor Alexander Fisher, who co-led the competition together with professors Mario Giacomazzo of UNC and Elijah Meyer of NCSU.

You don’t need to be an expert coder to enter. “We have some tutorials and resources to get you started, even if you have little to no experience programming,” Fisher said.

“The two skills that are really emphasized in a competition like this are data wrangling ability and model building,” he added. And while data skills are important, it’s just as critical to “love the game and be creative.”

One student who used his passion for the game and penchant for data crunching to test his mettle was Chris Johnson ’25, a senior majoring in economics.

“I’ve been watching college basketball since I was a kid,” Johnson said. “March Madness is my favorite time of year.”

To make his predictions, Johnson used Barttorvik.com, a website that contains a wealth of college basketball data. He built a model in Python that took into account statistics on each team’s offensive prowess and defensive strength, along with the pace at which they tend to play. Using these, he was able to estimate the final points for each team and calculate the point spread from there.

What started out as a fun side project will soon become a day job for this Duke student. Chris Johnson was the first place winner in the first-ever Triangle Sports Analytics competition, and will be starting a job as a data analyst with DraftKings this summer.

Of course, data and statistics can only capture so much of what makes basketball exciting — the unquantifiable human element also plays a role. Factors such as injuries and roster shifts can also have a significant impact on the outcome of a game, but they’re hard to anticipate in advance, Johnson said.

This year, the top three teams in the Triangle Sports Analytics competition were all from Duke. “I think we had a pretty sweeping victory this year,” Fisher said.

There’s no prize money; “this was just a ‘do it for the glory’ competition,” Fisher said.

“Bragging rights are really important,” said Dillan Sant, co-president of the Duke Sports Analytics Club, in a workshop he and co-president Anmol Sapru hosted to help students prepare for the competition.

Johnson finished first in this year’s contest, which didn’t include the NCAA tournament. So what was his reaction to Duke’s loss in the Final Four?

“The ending was definitely surprising,” said Johnson, who will be going on to a career in sports analytics after graduation, working as a data analyst for DraftKings.

“Any statistical model would tell you that Duke had more than a 90% chance of winning going into the final two minutes,” Johnson said.

“Pretty sad being a Duke fan, but it’s also part of the craziness and unpredictability that makes college basketball fun in the first place,” he added.

Duke experts discuss the potential of AI to help prevent, detect and treat disease

By Robin Smith

On October 15, 2024

In Artificial Intelligence, Biomedical Engineering, Engineering, Entrepreneurship, Medicine

Sure, A.I. chatbots can write emails, summarize an article, or come up with a grocery list. But ChatGPT-style artificial intelligence and other machine learning techniques have been making their way into another realm: healthcare.

Imagine using AI to detect early changes in our health before we get sick, or understand what happens in our brains when we feel anxious or depressed — even design new ways to fight hard-to-treat diseases.

These were just a few of the research themes discussed at the Duke Summit on AI for Health Innovation, held October 9 – 11.

Duke assistant professor Pranam Chatterjee is the co-founder of Gameto, Inc. and UbiquiTx, Inc. Credit: Brian Strickland

For assistant professor of biomedical engineering Pranam Chatterjee, the real opportunity for the large language models behind tools like ChatGPT lies not in the language of words, but in the language of biology.

Just like ChatGPT predicts the order of words in a sentence, the language models his lab works on can generate strings of molecules that make up proteins.

His team has trained language models to design new proteins that could one day fight diseases such as Huntington’s or cancer, even grow human eggs from stem cells to help people struggling with infertility.

“We don’t just make any proteins,” Chatterjee said. “We make proteins that can edit any DNA sequence, or proteins that can modify other disease-causing proteins, as well as proteins that can make new cells and tissues from scratch.”

Duke assistant professor Monica Agrawal is the co-founder of Layer Health. Credit: Brian Strickland

New faculty member Monica Agrawal said algorithms that leverage the power of large language models could help with another healthcare challenge: mining the ever-expanding trove of data in a patient’s medical chart.

To choose the best medication for a certain patient, for example, a doctor might first need to know things like: How has their disease progressed over time? What interventions have already been tried? What symptoms and side effects did they have? Do they have other conditions that need to be considered?

“The challenge is, most of these variables are not found cleanly in the electronic health record,” said Agrawal, who joined the departments of computer science and biostatistics and bioinformatics this fall.

Instead, most of the data that could answer these questions is trapped in doctors’ notes. The observations doctors type into a patient’s electronic medical record during a visit, they’re often chock-full of jargon and abbreviations.

The shorthand saves time during patient visits, but it can also lead to confusion among patients and other providers. What’s more, reviewing these records to understand a patient’s healthcare history is time-intensive and costly.

Agrawal is building algorithms that could make these records easier to maintain and analyze, with help from AI.

“Language is really embedded across medicine, from notes to literature to patient communications to trials,” Agrawal said. “And it affects many stakeholders, from clinicians to researchers to patients. The goal of my new lab is to make clinical language work for everyone.”

Duke assistant professor Jessilyn Dunn leads Duke’s BIG IDEAs Lab. Credit: Brian Strickland

Jessilyn Dunn, an assistant professor of biomedical engineering and biostatistics and bioinformatics at Duke, is looking at whether data from smartwatches and other wearable devices could help detect early signs of illness or infection before people start to have symptoms and realize they’re sick.

Using AI and machine learning to analyze data from these devices, she and her team at Duke’s Big Ideas Lab say their research could help people who are at risk of developing diabetes take action to reverse it, or even detect when someone is likely to have RSV, COVID-19 or the flu before they have a chance to spread the infection.

“The benefit of wearables is that we can gather information about a person’s health over time, continuously and at a very low cost,” Dunn said. “Ultimately, the goal is to provide patient empowerment, precision therapies, just-in-time intervention and improve access to care.”

Duke associate professor David Carlson. Credit: Brian Strickland

David Carlson, an associate professor of civil and environmental engineering and biostatistics and bioinformatics, is developing AI techniques that can make sense of brain wave data to better understand different emotions and behaviors.

Using machine learning to analyze the electrical activity of different brain regions in mice, he and his colleagues have been able to track how aggressive a mouse is feeling, and even block the aggression signals to make them more friendly to other mice.

“This might sound like science fiction,” Carlson said. But Carlson said the work will help researchers better understand what happens in the brains of people who struggle with social situations, such as those with autism or social anxiety disorder, and could even lead to new ways to manage and treat psychiatric disorders such as anxiety and depression.

Can You Spot the Species in These Lemur Lookalikes?

By Robin Smith

On October 7, 2024

In Animals, Genetics/Genomics, Lemurs

In some parts of the world, animals are going extinct before scientists can even name them.

Such may be the case for mouse lemurs, the saucer-eyed, teacup-sized primates native to the African island of Madagascar.

Various species of mouse lemurs found in Madagascar. Photos by Sam Hyde Roberts

There, deforestation has prompted the International Union for the Conservation of Nature (IUCN) to classify some of these tree-dwelling cousins as “endangered” even before they are formally described.

Duke professor Anne Yoder has been trying to take stock of how many mouse lemur species are alive today before they blink out of existence.

It’s not an easy task. Mouse lemurs are shy, they only come out at night, and they live in hard-to-reach places in remote forests. To add to the difficulty, many species of mouse lemurs are essentially lookalikes. It’s impossible to tell them apart just by peering at them through binoculars.

When Yoder first started studying mouse lemurs some 25 years ago, there were only three distinct species recognized by scientists. Over time and with advances in DNA sequencing, researchers began to wonder if what looked like three species might actually be upwards of two dozen.

In a new study, Yoder and dozens of colleagues from Europe, Madagascar and North America compiled and analyzed 50 years of hard-won data on the physical, behavioral and genetic differences among mouse lemurs to try to pin down the true number.

While many mouse lemur species look alike, they have different diets, and males use different calls to find and woo their mates, the researchers explain.

By pinning down their number and location, researchers hope to make more informed decisions about how best to help keep these species from the brink.

The study was published Sept. 27 in the journal Nature Ecology & Evolution.

Braxton Craven Distinguished Professor of Evolutionary Biology, Anne Yoder was director of the Duke Lemur Center from 2006 to 2018.

Student Wealth and Poverty Across Durham Public Schools, Mapped

By Robin Smith

On August 20, 2024

In Data, Students, Visualization

New maps of Durham released by students in Duke’s Data+ research program show the Bull City as a patchwork of red, white and pink. But what looks like a haphazardly assembled quilt is actually a picture of the socioeconomic realities facing Durham’s 32,000-plus public school students.

The color patches represent the home values across Durham, showing roughly where more and less affluent students live. The darker the red, the higher-priced their housing.

Like cities and neighborhoods, schools face economic disparities too. Research shows that school segregation by race and class in North Carolina has gotten steadily worse over the last three decades.

A 2024 study by North Carolina State University revealed that the typical low-income student attends schools where more than 70% of their classmates are low-income too — a trend that worsens the achievement gap between the richest and poorest children.

A new student assignment plan that Durham Public Schools is rolling out this year aims to combat that trend by redrawing district boundary lines — the thick black lines on the map — to make schools more diverse and equitable.

But if schools are to tackle economic segregation, they’ll need accurate ways to measure it as Durham continues to grow and change.

As kids across Durham head back to class, some 1500 elementary school students are changing schools this year under the Durham Public Schools Growing Together plan, which aims to increase equity and access across schools.

That was the challenge facing a team in Duke’s Data+ program this summer. For 10 weeks, Duke students Alex Barroso and Dhaval Potdar collaborated with school planners at Durham Public Schools to look at how family wealth and poverty are distributed across the school system.

“Socioeconomic status is a complicated thing,” said Barroso, a Duke junior majoring in statistical science.

For years, the standard way to identify children in need was using free and reduced-price lunch statistics from the National School Lunch Program, along with published income data from the U.S. Census Bureau.

But those numbers can be unreliable, Barroso said.

Changing state and federal policies mean that more districts — including Durham Public Schools — are providing free meals to all students, regardless of their family income. But as a result, schools no longer have an exact count of how many students qualify.

And Census estimates are based on geographic boundaries that can mask important variation in the data when we look more closely.

At a symposium in Gross Hall in July, Barroso pointed to several dark red patches (i.e., more expensive housing) bordering white ones (i.e., more affordable) on one of the team’s maps.

In some parts of the city, homes worth upwards of a million dollars abut modest apartments worth a fraction of that, “which can skew the data,” he said.

The problem with Census estimates “is that everyone who lives in that area is reported as having the same average income,” said team lead Vitaly Radsky, a PhD student at UNC’s School of Education and school planner with Durham Public Schools.

So they took a different approach: using homes as a proxy for socioeconomic status.

Research has confirmed that students from higher-value homes perform better in school as measured by standardized math tests.

The team created a custom script that fetches publicly available data on every home in Durham from sources such as Durham Open Data and the Census, and then automatically exports it to a dashboard that shows the data on a map.

“Every single house is accounted for within this project,” Barroso said.

They ran into challenges. For example, Census data are tied to tracts that don’t necessarily align with the district boundaries used by schools, said Dhaval Potdar, a graduate student in Duke’s Master in Interdisciplinary Data Science.

One takeaway from their analysis, Potdar said, is no one yardstick sums up the economic well-being of every student.

In Durham, the typical public school student lives in a home valued at about $300,000.

But the picture varies widely when you zoom in on different geographic scales and footprints.

It’s also a different story if you account for the significant fraction of Durham families who live among neighbors in a larger building such as an apartment, townhouse or condominium, instead of a single-family home.

Considering a home’s age can change the picture too.

Generally speaking, students who live in more expensive homes come from more affluent families. But in many parts of the U.S., home prices have far outpaced paychecks. That means a home that has soared in value in the years since it was purchased may not reflect a family’s true economic situation today, particularly if their income remained flat.

The team’s data visualizations aim to let school planners look at all those factors.

There are still issues to be ironed out. For example, there’s some work to be done before planners can make apples-to-apples comparisons between a student whose family owns their home versus renting a similar property, Barroso said.

“No data source is perfect,” but the research offers another way of anticipating the shifting needs of Durham students, Radsky said.

“The traditional metrics really aren’t getting at the granular fabric of the Durham community,” said Mathew Palmer, the district’s senior executive director of school planning and operational services.

Research like this helps address questions like, “are we putting our resources where the kids need them the most? And are schools equitable?”

“This analysis gives schools more tools moving forward,” Palmer said.

A Camera Trap for the Invisible

By Robin Smith

On July 29, 2024

In Artificial Intelligence, Physics

It sounds fantastical, but it’s a reality for the scientists who work at the world’s largest particle collider:

In an underground tunnel some 350 feet beneath the France–Switzerland border, a huge device called the Large Hadron Collider sends beams of protons smashing into each other at nearly the speed of light, creating tiny eruptions that mimic the conditions that existed immediately after the Big Bang.

Scientists like Duke physicist Ashutosh Kotwal think the subatomic debris of these collisions could contain hints of the universe’s “missing matter.” And with some help from artificial intelligence, Kotwal hopes to catch these fleeting clues on camera.

A view inside the ATLAS detector at the Large Hadron Collider. Akin to a giant digital camera, physicists hope to use the detector in the quest to find dark matter, the mysterious stuff that fills the universe but no one has ever seen it. Credit: CERN.

Ordinary matter — the stuff of people and planets — is only part of what’s out there. Kotwal and others are hunting for dark matter, an invisible matter that’s five times more abundant than the stuff we can see but whose nature remains a mystery.

Scientists know it exists from its gravitational influence on stars and galaxies, but other than that we don’t know much about it.

The Large Hadron Collider could change that. There, researchers are looking for dark matter and other mysteries using detectors that act like giant 3D digital cameras, taking continuous snapshots of the spray of particles produced by each proton-proton collision.

Only ordinary particles trigger a detector’s sensors. If researchers can make dark matter at the LHC, scientists think one way it could be noticeable is as a sort of disappearing act: heavy charged particles that travel a certain distance — 10 inches or so — from the point of collision and then decay invisibly into dark matter particles without leaving a trace.

If you retraced the paths of these particles, they would leave a telltale “disappearing track” that vanishes partway through the detector’s inner layers.

When beams collide at the Large Hadron Collider, they split into thousands of smaller particles that fly out in all directions before vanishing. Scientists think some of those particles could make up dark matter, and Duke physicist Ashutosh Kotwal is using AI and image recognition to help in the hunt. Credit: Pcharito.

But to spot these elusive tracks they’ll need to act fast, Kotwal says.

That’s because the LHC’s detectors take some 40 million snapshots of flying particles every second.

That’s too much raw data to hang on to everything and most of it isn’t very interesting. Kotwal is looking for a needle in a haystack.

“Most of these images don’t have the special signatures we’re looking for,” Kotwal said. “Maybe one in a million is one that we want to save.”

Researchers have just a few millionths of a second to determine if a particular collision is of interest and store it for later analysis.

“To do that in real time, and for months on end, would require an image recognition technique that can run at least 100 times faster than anything particle physicists have ever been able to do,” Kotwal said.

Kotwal thinks he may have a solution. He has been developing something called a “track trigger,” a fast algorithm that is able to spot and flag these fleeting tracks before the next collision occurs, and from among a cloud of tens of thousands of other data points measured at the same time.

Ashutosh Kotwal is the Fritz London Distinguished Professor of Physics at Duke University.

His design works by divvying up the task of analyzing each image among a large number of AI engines running simultaneously, built directly onto a silicon chip. The method processes an image in less than 250 nanoseconds, automatically weeding out the uninteresting ones.

Kotwal first described the approach in a sequence of two papers published in 2020 and 2021. In a more recent paper published this May in Scientific Reports, he and a team of undergraduate student co-authors show that his algorithm can run on a silicon chip.

Kotwal and his students plan to build a prototype of their device by next summer, though it will be another three or four years before the full device — which will consist of about 2000 chips — can be installed at detectors at the LHC.

As the performance of the accelerator continues to crank up, it will produce even more particles. And Kotwal’s device could help make sure that, if dark matter is hiding among them, scientists won’t miss it.

“Our job is to ensure that if dark matter production is happening, then our technology is up to snuff to catch it in the act,” Kotwal said.

Duke Mathletes Stand Out in a Crowd

By Robin Smith

On July 12, 2024

In Mathematics, Students

Standing out in a crowd of competitors is no easy task. But one Duke team has done just that — in math.

The Blue Devils were the only U.S.-based team to claim a top 25 finish at the 40th annual Mathematical Contest in Modeling (MCM), beating out more than 18,500 other teams from 20 countries.

*Blue Devils Brandon Lu, Benny Sun and Chris Kan (L to R) finished in the top 25 out of 18,525 teams in an international math contest called Mathematical Contest in Modeling.*

The team consisted of undergraduates Christopher Kan, Benny Sun, and Brandon Lu. Their task: to solve a real-world problem using mathematical modeling within 96 hours.

This year’s contestants tackled problems ranging from analyzing what gives tennis players an edge at Wimbledon, to optimizing search and rescue operations for missing submersibles.

The Duke team tackled a challenge that has vexed the fishing industry in the Great Lakes: predicting the impact of an invasive parasitic fish called the sea lamprey that can wreak havoc on native fish.

By adapting existing models from biology and biochemistry to model the sea lamprey population, the students were able to determine how to best apply treatments to rid streams of these parasites.

*Two sea lampreys chewing their way into the flesh of a native lake trout of the Great Lakes. (Great Lakes Fisheries Commission)*

The contest “is much more open-ended and creatively-focused than most STEM classes,” said Sun, a mathematics and computer science double major at Duke.

The participants try out different approaches to modeling the problems, and there is no one correct answer.

Sun, Kan and Lu also received the Mathematical Association of America Award for their paper. “They did a great job,” said team advisor Veronica Ciocanel, an assistant professor of math and biology who also co-organizes a local version of the contest each fall, called the Triangle Competition in Math Modeling.

In these contests, creativity, time management and writing skills are just as important as cramming on concepts.

“We realized that communication was as important as the findings themselves,” Sun said. “We spent the last two days primarily focused on writing a good paper.”

Having fun as a team is important too, Sun said. “Team chemistry can be an especially important factor in success when you are all locked in the same room for the weekend.”

Wiring the Brain

By Robin Smith

On June 26, 2024

In Animals, Neuroscience, Students

From tiny flies, Duke researchers are finding new clues to how the brain sets up its circuitry.

In her time at Duke, Khanh Vien figures she’s dissected close to 10,000 fly brains. For her PhD she spent up to eight hours each day peering at baby flies under the microscope, teasing out tiny brains a fraction the size of a poppy seed.

“I find it very meditative,” she said.

Vien acknowledges that, to most people, fruit flies are little more than a kitchen nuisance; something to swat away. But to researchers like her, they hold clues to how animal brains — including our own — are built.

While the human brain has some 86 billion neurons, a baby fruit fly’s brain has a mere 3016 — making it millions of times simpler. Those neurons talk to each other via long wire-like extensions, called axons, that relay electrical and chemical signals from one cell to the next.

Vien and other researchers in Professor Pelin Volkan’s lab at Duke are interested in how that wiring gets established during the fly’s development.

By analyzing a subset of neurons responsible for the fly’s sense of smell, the researchers have identified a protein that helps ensure that new neurons extend their axons to the correct spots in the olfactory area of the young fly’s brain and not elsewhere.

Because the same protein is found across the animal kingdom, including humans, the researchers say the work could ultimately shed light on what goes awry within the brains of people living with schizophrenia and other mental illnesses.

Their findings are published in the journal iScience.

Khanh Vien earned her PhD in developmental and stem cell biology in Professor Pelin Volkan’s lab at Duke.

To get a fuller picture of a forest, sometimes research requires a team effort

By Robin Smith

On April 1, 2024

In Animals, Biology, Climate/Global Change, Environment/Sustainability, Field Research, Lemurs

Film by Riccardo Morrelas, Zahava Production

For some people, the word “rainforest” conjures up vague notions of teeming jungles. But Camille DeSisto sees something more specific: a complex interdependent web.

For the past few years, the Duke graduate student has been part of a community-driven study exploring the relationships between people, plants and lemurs in a rainforest in northern Madagascar, where the health of one species depends on the health of others.

Many lemurs, for example, eat the fruits of forest trees and deposit their seeds far and wide in their droppings, thus helping the plants spread. People, in turn, depend on the plants for things like food, shelter and medicines.

But increasingly, deforestation and other disturbances are throwing these interactions out of whack.

DeSisto and her colleagues have been working in a 750,000-acre forest corridor in northeast Madagascar known as the COMATSA that connects two national parks.

The area supports over 200 tree species and nine species of lemurs, and is home to numerous communities of people.

A red-bellied lemur (*Eulemur rubriventer*) in a rainforest in northeast Madagascar. Photo by Martin Braun.

“People live together with nature in this landscape,” said DeSisto, who is working toward her Ph.D. in ecology at the Nicholas School of the Environment.

But logging, hunting and other stressors such as poverty and food insecurity have taken their toll.

Over the last quarter century, the area has lost 14% of its forests, mostly to make way for vanilla and rice.

This loss of wild habitats risks setting off a series of changes. Fewer trees also means fewer fruit-eating lemurs, which could create a feedback loop in which the trees that remain have fewer opportunities to replace themselves and sprout up elsewhere — a critical ability if trees are going to track climate change.

DeSisto and her colleagues are trying to better understand this web of connections as part of a larger effort to maximize forest resilience into an uncertain future.

To do this work, she relies on a network of a different sort.

The research requires dozens of students and researchers from universities in Madagascar and the U.S., not to mention local botanists and lemur experts, the local forest management association, and consultants and guides from nearby national parks, all working together across time zones, cultures and languages.

Forest field team members at camp (not everyone present). Photo credit: Jane Slentz-Kesler.

Together, they’ve found that scientific approaches such as fecal sampling or transect surveys can only identify so much of nature’s interconnected web.

Many lemurs are small, and only active at night or during certain times of year, which can make them hard to spot — especially for researchers who may only be on the ground for a limited time.

To fill the gaps, they’re also conducting interviews with local community members who have accumulated knowledge from a lifetime of living on the land, such as which lemurs like to munch on certain plants, what parts they prefer, and whether people rely on them for food or other uses.

By integrating different kinds of skills and expertise, the team has been able to map hidden connections between species that more traditional scientific methods miss.

For example, learning from the expertise of local community members helped them understand that forest patches that are regenerating after clear-cutting attract nocturnal lemurs that may — depending on which fruits they like to eat — promote the forest’s regrowth.

Camille DeSisto after a successful morning collecting lemur fecal samples.

Research collaborations aren’t unusual in science. But DeSisto says that building collaborations with colleagues more than 9,000 miles away from where she lives poses unique challenges.

Just getting to her field site involves four flights, several bumpy car rides, climbing steep trails and crossing slippery logs.

“Language barriers are definitely a challenge too,” DeSisto said.

She’s been studying Malagasy for seven years, but the language’s 18 dialects can make it hard to follow every joke her colleagues tell around the campfire.

To keep her language skills sharp she goes to weekly tutoring sessions when she’s back in the U.S., and she even helped start the first formal class on the language for Duke students.

“I like to think of it as language opportunities, not just language barriers,” DeSisto said.”

“Certain topics I can talk about with much more ease than others,” she added. “But I think making efforts to learn the language is really important.”

When they can’t have face-to-face meetings the team checks in remotely, using videoconferencing and instant messaging to agree on each step of the research pipeline, from coming up with goals and questions and collecting data to publishing their findings.

“That’s hard to navigate when we’re so far away,” DeSisto said. But, she adds, the teamwork and knowledge sharing make it worth it. “It’s the best part of research.”

This research was supported by Duke Bass Connections (“Biocultural Sustainability in Madagascar,” co-led by James Herrera), Duke Global, The Explorers Club, Primate Conservation, Inc., Phipps Conservatory and Botanical Gardens, and the Garden Club of America.

A Grueling Math Test So Hard, Almost No One Gets a Perfect Score

By Robin Smith

On March 1, 2024

In Engineering, Mathematics, Students

Yet hundreds of schools compete each year, and this time the Blue Devils made it into the top three

Every year, thousands of college students from across the U.S. and Canada give up a full Saturday before finals begin to take a notoriously difficult, 6-hour math test — and not for a grade, but for fun.

In “the Putnam,” as it’s known, contestants spend two 3-hour sessions trying to solve 12 proof-based math problems worth 10 points apiece.

More than 150,000 people have taken the exam in the contest’s 85-year history, but only five times has someone earned a perfect score. Total scores of 1 or 0 are not uncommon.

Despite the odds, the Blue Devils had a strong showing this year.

A total of 3,857 students from 471 schools competed in the December contest. In results announced Feb. 16, a Duke team consisting of Erick Jiang ’26, James “Fletch” Rydell ’26 and Kaixin “Kai” Wang ’27 ranked third in North America behind MIT and Harvard, winning a $15,000 prize for Duke and each taking home $600 for themselves.

According to mathematics professor Lenny Ng, it’s Duke’s best performance in almost 20 years.

“This is the first time a Duke team has placed this high since 2005,” said Ng, who was a three-time Putnam Fellow himself, finishing in the top five each year he was an undergraduate at Harvard.

There’s no official syllabus for prepping for the Putnam. To get ready, the students practice working through problems and discussing their solutions in a weekly problem-solving seminar held each fall.

Students serve as the instructors, focusing on a different topic each week ranging from calculus to number theory.

“They get a sense of what the problems are like, so it’s not quite as intimidating as it might be if they went into the contest cold,” said math department chair Robert Bryant.

“Not only do they learn how to do the problems, but they also get to know each other,” said professor emeritus David Kraines, who has coached Duke Putnam participants for more than 30 years.

Kraines said 8-10 students take his problem-solving seminar for credit each fall. “We always get another 10 or so who come for the pizza,” Kraines said.

The biggest difference between a Putnam problem and a homework problem, said engineering student Rydell, is that usually with a homework problem you’ve already been shown what to do; you just have to apply it.

Whereas most of the time in math competitions like the Putnam, “there’s no clear path forward when you first see the problem,” Rydell said. “They’re more about finding some insight or way of looking at the problem in a different perspective.”

Putnam problems are meant to be solvable using only paper and pencil — no computing power required. The contestants work through each problem by hand, trying different paths towards a solution and spelling out their reasoning step-by-step.

This year, one problem involved determining how many configurations of coins are possible given a grid with coins sitting in some of the squares, if those coins are only allowed to move in certain ways.

Another question required knowing something about the geometry of a 20-sided shape known as a icosahedron.

“That was the one I struggled with the most,” said Wang, whose individual score nevertheless tied him for sixth place overall out of 3,857 contestants.

The most common question he gets asked about the Putnam, Rydell said, is not so much what’s on the test, but why people take it in the first place.

This year’s test was so challenging that a score of 78 out of 120 or better — just 65% — was enough to earn a spot in the top 10.

Most of the people who took it scored less than 10%, which means many problems went unsolved.

“For days after I took the Putnam, I would think about the problems and wonder: could I have done it better this way? You can become obsessed,” said Bryant, who took the Putnam in the 1970s as a college student at NC State.

Sophomores Jiang and Rydell, who both ranked in the top 5%, see it as an opportunity to “meet people who also enjoy problem solving,” Jiang said.

“I’m not a math major so I probably wouldn’t do much of this kind of problem solving otherwise,” Rydell said.

For Rydell it’s also the aha moment: “Just the reward of when you solve a problem, the feeling of making that breakthrough,” Rydell said.

Professor Kraines’ weekly problem-solving seminar, MATH 283S, takes place on Tuesday evenings at 6:15 p.m. during the fall semester. Registration for Fall 2024 begins April 3.

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