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.
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.
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.”
