Give Homo naledi credit for originality. The fossils of this humanlike species previously revealed an unexpectedly peculiar body plan. Now its pockmarked teeth speak to an unusually hard-edged diet.

H. naledi displays a much higher rate of chipped teeth than other members of the human evolutionary family that once occupied the same region of South Africa, say biological anthropologist Ian Towle and colleagues. Dental damage of this kind results from frequent biting and chewing on hard or gritty objects, such as raw tubers dug out of the ground, the scientists report in the September American Journal of Physical Anthropology.
“A diet containing hard and resistant foods like nuts and seeds, or contaminants such as grit, is most likely for H. naledi,” says Towle, of Liverpool John Moores University in England.

Extensive tooth chipping shows that “something unusual is going on” with H. naledi’s diet, says paleoanthropologist Peter Ungar of the University of Arkansas in Fayetteville. He directs ongoing microscopic studies of H. naledi’s teeth that may provide clues to what this novel species ate.
Grit from surrounding soil can coat nutrient-rich, underground plant parts, including tubers and roots. Regularly eating those things can cause the type of chipping found on H. naledi teeth, says paleobiologist Paul Constantino of Saint Michael’s College in Colchester, Vt. “Many animals cannot access these underground plants, but primates can, especially if they use digging sticks.”
H. naledi fossils, first found in South Africa’s subterranean Dinaledi Chamber and later a second nearby cave (SN: 6/10/17, p. 6), came from a species that lived between 236,000 and 335,000 years ago. It had a largely humanlike lower body, a relatively small brain and curved fingers suited for climbing trees.

Towle’s group studied 126 of 156 permanent H. naledi teeth found in Dinaledi Chamber. Those finds come from a minimum of 12 individuals, nine of whom had at least one chipped chopper. Two of the remaining three individuals were represented by only one tooth. Teeth excluded from the study were damaged, had not erupted above the gum surface or showed signs of having rarely been used for chewing food.

Chips appear on 56, or about 44 percent, of H. naledi teeth from Dinaledi Chamber, Towle’s team says. Half of those specimens sustained two or more chips. About 54 percent of molars and 44 percent of premolars, both found toward the back of the mouth, display at least one chip. For teeth at the front of the mouth, those figures fell to 25 percent for canines and 33 percent for incisors.

Chewing on small, hard objects must have caused all those chips, Towle says. Using teeth as tools, say to grasp animal hides, mainly damages front teeth, not cheek teeth as in H. naledi. Homemade toothpicks produce marks between teeth unlike those on the H. naledi finds.

Two South African hominids from between roughly 1 million and 3 million years ago, Australopithecus africanus and Paranthropus robustus, show lower rates of tooth chipping than H. naledi, at about 21 percent and 13 percent, respectively, the investigators find. Researchers have suspected for decades that those species ate hard or gritty foods, although ancient menus are difficult to reconstruct (SN: 6/4/11, p. 8). Little evidence exists on the extent of tooth chipping in ancient Homo species. But if H. naledi consumed underground plants, Stone Age Homo sapiens in Africa likely did as well, Constantino says.

In further tooth comparisons with living primates, baboons — consumers of underground plants and hard-shelled fruits — showed the greatest similarity to H. naledi, with fractures on 25 percent of their teeth. That figure reached only about 11 percent in gorillas and 5 percent in chimpanzees.

Human teeth found at sites in Italy, Morocco and the United States show rates and patterns of tooth fractures similar to H. naledi, he adds. Two of those sites date to between 1,000 and 1,700 years ago. The third site, in Morocco, dates to between 11,000 and 12,000 years ago. People at all three sites are suspected to have had diets unusually heavy on gritty or hard-shelled foods, the scientists say.

Chips mar 50 percent of H. naledi’s right teeth, versus 38 percent of its left teeth. That right-side tilt might signify that the Dinaledi crowd were mostly right-handers who typically placed food on the right side of their mouths. But more fossil teeth are needed to evaluate that possibility, Towle cautions.

Some stars erupt like clockwork. Astronomers have tracked down a star that Korean astronomers saw explode nearly 600 years ago and confirmed that it has had more outbursts since. The finding suggests that what were thought to be three different stellar objects actually came from the same object at different times, offering new clues to the life cycles of stars.

On March 11, 1437, Korean royal astronomers saw a new “guest star” in the tail of the constellation Scorpius. The star glowed for 14 days, then faded. The event was what’s known as a classical nova explosion, which occurs when a dense stellar corpse called a white dwarf steals enough material from an ordinary companion star for its gas to spontaneously ignite. The resulting explosion can be up to a million times as bright as the sun, but unlike supernovas, classical novas don’t destroy the star.
Astronomer Michael Shara of the American Museum of Natural History in New York City and colleagues used digitized photographic plates dating from as early as 1923 to trace a modern star back to the nova. The team tracked a single star as it moved away from the center of a shell of hot gas, the remnants of an old explosion, thus showing that the star was responsible for the nova. The researchers also saw the star, which they named Nova Scorpii AD 1437, give smaller outbursts called dwarf novas in the 1930s and 1940s. The findings were reported in the Aug. 31 Nature.

The discovery fits with a proposal Shara and colleagues made in the 1980s. They suggested that three different stellar observations — bright classical nova explosions, dwarf nova outbursts and an intermediate stage where a white dwarf is not stealing enough material to erupt — are all different views of the same system.

“In biology, we might say that an egg, a larva, a pupa and a butterfly are all the same system seen at different stages of development,” Shara says.

Peer inside the brain of someone learning. You might be lucky enough to spy a synapse pop into existence. That physical bridge between two nerve cells seals new knowledge into the brain. As new information arrives, synapses form and strengthen, while others weaken, making way for new connections.

You might see more subtle changes, too, like fluctuations in the levels of signaling molecules, or even slight boosts in nerve cell activity. Over the last few decades, scientists have zoomed in on these microscopic changes that happen as the brain learns. And while that detailed scrutiny has revealed a lot about the synapses that wire our brains, it isn’t enough. Neuroscientists still lack a complete picture of how the brain learns.

They may have been looking too closely. When it comes to the neuroscience of learning, zeroing in on synapse action misses the forest for the trees.

A new, zoomed-out approach attempts to make sense of the large-scale changes that enable learning. By studying the shifting interactions between many different brain regions over time, scientists are beginning to grasp how the brain takes in new information and holds onto it.
These kinds of studies rely on powerful math. Brain scientists are co-opting approaches developed in other network-based sciences, borrowing tools that reveal in precise, numerical terms the shape and function of the neural pathways that shift as human brains learn.

“When you’re learning, it doesn’t just require a change in activity in a single region,” says Danielle Bassett, a network neuroscientist at the University of Pennsylvania. “It really requires many different regions to be involved.” Her holistic approach asks, “what’s actually happening in your brain while you’re learning?” Bassett is charging ahead to both define this new field of “network neuroscience” and push its boundaries.

“This line of work is very promising,” says neuroscientist Olaf Sporns of Indiana University Bloomington. Bassett’s research, he says, has great potential to bridge gaps between brain-imaging studies and scientists’ understanding of how learning happens. “I think she’s very much on the right track.”
Already, Bassett and others have found tantalizing hints that the brains that learn best have networks that are flexible, able to rejigger connections on the fly to allow new knowledge in. Some brain regions always communicate with the same neural partners, rarely switching to others. But brain regions that exhibit the most flexibility quickly swap who they’re talking with, like a parent who sends a birthday party invite to the preschool e-mail list, then moments later, shoots off a work memo to colleagues.

In a few studies, researchers have witnessed this flexibility in action, watching networks reconfigure as people learn something while inside a brain scanner. Network flexibility may help several types of learning, though too much flexibility may be linked to disorders such as schizophrenia, studies suggest.

Not surprisingly, some researchers are rushing to apply this new information, testing ways to boost brain flexibility for those of us who may be too rigid in our neural connections.

“These are pretty new ideas,” says cognitive neuroscientist Raphael Gerraty of Columbia University. The mathematical and computational tools required for this type of research didn’t exist until recently, he says. So people just weren’t thinking about learning from a large-scale network perspective. “In some ways, it was a pretty boring mathematical, computational roadblock,” Gerraty says. But now the road is clear, opening “this conceptual avenue … that people can now explore.”
It takes a neural village
That conceptual avenue is more of a map, made of countless neural roads. Even when a person learns something very simple, large swaths of the brain jump in to help. Learning an easy sequence of movements, like tapping out a brief tune on a keyboard, prompts activity in the part of the brain that directs finger movements. The action also calls in brain areas involved in vision, decision making, memory and planning. And finger taps are a pretty basic type of learning. In many situations, learning calls up even more brain areas, integrating information from multiple sources, Gerraty says.

He and colleagues caught glimpses of some of these interactions by scanning the brains of people who had learned associations between two faces. Only one of the faces was then paired with a reward. In later experiments, the researchers tested whether people could figure out that the halo of good fortune associated with the one face also extended to the face it had been partnered with earlier. This process, called “transfer of learning,” is something that people do all the time in daily life, such as when you’re wary of the salad at a restaurant that recently served tainted cheese.

Study participants who were good at applying knowledge about one thing — in this case, a face — to a separate thing showed particular brain signatures, Gerraty and colleagues reported in 2014 in the Journal of Neuroscience. Connections between the hippocampus, a brain structure important for memory, and the ventromedial prefrontal cortex, involved in self-control and decision making, were weaker in good learners than in people who struggled to learn. The scans, performed several days after the learning task, revealed inherent differences between brains, the researchers say. The experiment also turned up other neural network differences among these regions and larger-scale networks that span the brain.

Children who have difficulty learning math, when scanned, also show unexpected brain connectivity, according to research by neuroscientist Vinod Menon of Stanford University and colleagues. Compared with kids without disabilities, children with developmental dyscalculia who were scanned while doing math problems had more connections, particularly among regions involved in solving math problems. That overconnectivity, described in 2015 in Developmental Science, was a surprise, Menon says, since earlier work had suggested that these math-related networks were too weak. But it may be that too many links create a system that can’t accommodate new information. “The idea is that if you have a hyperconnected system, it’s not going to be as responsive,” he says.
There’s a balance to be struck, Menon says. Neural pathways that are too weak can’t carry necessary information, and pathways that are too connected won’t allow new information to move in. But the problem isn’t as simple as that. “It’s not that everything is changing everywhere,” he says. “There is a specificity to it.” Some connections are more important than others, depending on the task.

Neural networks need to shuttle information around quickly and fluidly. To really get a sense of this movement as opposed to snapshots frozen in time, scientists need to watch the brain as it learns. “The next stage is to figure out how the networks actually shift,” Menon says. “That’s where the studies from Dani Bassett and others will be very useful.”

Flexing in real time
Bassett and colleagues have captured these changing networks as people learn. Volunteers were given simple sequences to tap out on a keyboard while undergoing a functional MRI scan. During six weeks of scanning as people learned the task, neural networks in their brains shifted around. Some connections grew stronger and some grew weaker, Bassett and her team reported in Nature Neuroscience in 2015.

People who quickly learned to tap the correct sequence of keys showed an interesting neural trait: As they learned, they shed certain connections between their frontal cortex, the outermost layer of the brain toward the front of the head, and the cingulate, which sits toward the middle of the brain. This connection has been implicated in directing attention, setting goals and making plans, skills that may be important for the early stages of learning but not for later stages, Bassett and colleagues suspect. Compared with slow learners, fast learners were more likely to have shunted these connections, a process that may have made their brains more efficient.

Flexibility seems to be important for other kinds of learning too. Reinforcement learning, in which right answers get a thumbs up and wrong answers are called out, also taps into brain flexibility, Gerraty, Bassett and others reported online May 30 at bioRxiv.org. This network comprises many points on the cortex, the brain’s outer layer, and a deeper structure known as the striatum. Other work on language comprehension, published by Bassett and colleagues last year in Cerebral Cortex, found some brain regions that were able to quickly form and break connections.

These studies captured brains in the process of learning, revealing “a much more interesting network structure than what we previously thought when we were only looking at static snapshots,” Gerraty says. The learning brain is incredibly dynamic, he says, with modules breaking off from partners and finding new ones.

While the details of those dynamics differ from study to study, there is an underlying commonality: “It seems that part of learning about the world is having parts of your brain become more flexible, and more able to communicate with different areas,” Gerraty says. In other words, the act of learning takes flexibility.

But too much of a good thing may be bad. While performing a recall task in a scanner, people with schizophrenia had higher flexibility among neural networks across the brain than did healthy people, Bassett and colleagues reported last year in the Proceedings of the National Academy of Sciences. “That suggests to me that while flexibility is good for healthy people, there is perhaps such a thing as too much flexibility,” Bassett says.
Just how this flexibility arises, and what controls it, is unknown. Andrea Stocco, a cognitive neuroscientist at the University of Washington in Seattle, suspects that a group of brain structures called the basal ganglia, deep within the brain, has an important role in controlling flexibility. He compares this region, which includes the striatum, to an air traffic controller who shunts information to where it’s most needed. One of the basal ganglia’s jobs seems to be shutting things down. “Most of the time, the basal ganglia is blocking something,” he says. Other researchers have found evidence that crucial “hubs” in the cortex help control flexibility.

Push for more
Researchers don’t yet know how measures of flexibility in brain regions relate to the microscopic changes that accompany learning. For now, the macro and the micro views of learning are separate worlds. Despite that missing middle ground, researchers are charging ahead, looking for signs that neural flexibility might offer a way to boost learning aptitude.

It’s possible that external brain stimulation may enhance flexibility. After receiving brain stimulation carefully aimed at a known memory circuit, people were better able to recall lists of words, scientists reported May 8 in Current Biology. If stimulation can boost memory, some argue, the technique could enhance flexibility and perhaps learning too.
Certain drugs show promise. DXM, found in some cough medicines, blocks proteins that help regulate nerve cell chatter. Compared with a placebo, the compound made some brain regions more flexible and able to rapidly switch partners in healthy people, Bassett and colleagues reported last year in the Proceedings of the National Academy of Sciences. She is also studying whether neurofeedback — a process in which people try to change their brain patterns to become more flexible with real-time monitoring — can help.

Something even simpler might work for boosting flexibility. On March 31 in Scientific Reports, Bassett and colleagues described their network analyses of an unusual subject. For a project called MyConnectome, neuroscientist Russ Poldrack, then at the University of Texas at Austin, had three brain scans a week for a year while assiduously tracking measures that included mood. Bassett and her team applied their mathematical tools to Poldrack’s data to get measurements of his neural flexibility on any given scan day. The team then looked for associations with mood. The standout result: When Poldrack was happiest, his brain was most flexible, for reasons that aren’t yet clear. (Flexibility was lowest when he was surprised.)

Those results are from a single person, so it’s unknown how well they would generalize to others. What’s more, the study identifies only a link, not that happiness causes more flexibility or vice versa. But the idea is intriguing, if not obvious, Bassett says. “Of course, no teacher is really going to say we’re doing rocket science if we tell them we should make the kids happier and then they’ll learn better.” But finding out exactly how happiness relates to learning is important, she says.

The research is just getting started. But already, insights on learning are coming quickly from the small group of researchers viewing the brain as a matrix of nodes and links that deftly shift, swap and rearrange themselves. Zoomed out, network science brings to the brain “a whole new set of hypotheses and new ways of testing them,” Bassett says.

Some bacteria may shield tumor cells against a common chemotherapy drug.

Certain types of bacteria make an enzyme that inactivates the drug gemcitabine, researchers report in the Sept. 15 Science. Gemcitabine is used to treat patients with pancreatic, lung, breast and bladder cancers.

Bacteria that produce the enzyme cytidine deaminase converted the drug to an inactive form. That allowed tumor cells to survive gemcitabine treatment in lab dishes and mouse studies, Leore Geller of the Weizmann Institute of Science in Rehovot, Israel, and colleagues discovered. More than 98 percent of the enzyme-producing microbes belong to the Gammaproteobacteria class, which includes E. coli and about 250 bacterial genera.
Pancreatic tumors taken from human patients also carried the enzyme-producing bacteria. Of 113 pancreatic ductal adenocarcinoma samples studied, 86 contained gemcitabine-inactivating bacteria.

Antibiotics may correct the problem. In the study, Geller and colleagues infected mice that had colon cancer with the enzyme-producing bacteria. Tumors grew rapidly in infected mice treated with gemcitabine alone. Giving the mice antibiotics helped gemcitabine kill tumor cells, increasing the number of tumor cells going through a type of cell death called apoptosis from about 15 percent to 60 percent or more. That result may indicate that combinations of gemcitabine and antibiotics could make chemotherapy more effective for some cancer patients.

No one knows whether chronic marijuana smoking causes emotional troubles or is a symptom of them…. This dearth of evidence has a number of explanations: serious lingering reactions, if they exist, occur after prolonged use, rarely after a single dose; marijuana has no known medical use, unlike LSD, so scientists have had little reason to study the drug…. Also, marijuana has been under strict legal sanctions … for more than 30 years. – Science News, October 7, 1967

In 29 states and in Washington, D.C., marijuana is now commonly prescribed for post-traumatic stress disorder and chronic pain. But the drug’s pros and cons remain hazy. Regular pot use has been linked to psychotic disorders and to alcohol and drug addiction (SN Online: 1/12/17). And two recent research reviews conclude that very little high-quality data exist on whether marijuana effectively treats PTSD or pain. Several large-scale trials are under way to assess how well cannabis treats these conditions.

Discoveries about the clocklike ups and downs of daily life have won Jeffery C. Hall, Michael Rosbash and Michael W. Young the Nobel Prize in physiology or medicine.

Circadian rhythms are daily cycles of hormones, gene activity and other biological processes that govern sleep, body temperature and metabolism. When thrown out of whack, there can be serious health consequences, including increased risk of diabetes, heart and Alzheimer’s diseases.

Hall and Rosbash discovered the first molecular gear of the circadian clockworks: A protein called Period increases and decreases in abundance on a regular cycle during the day. Young discovered that another protein called Timeless works with Period to drive the clock. Young also discovered other circadian clockworks.

A bit of imperfection could be perfect for flowers creating a “blue halo” effect that bees can see.

At least a dozen families of flowering plants, from hibiscuses to daisy relatives, have a species or more that can create a bluish-ultraviolet tinge using arrays of nanoscale ridges on petals, an international research team reports online October 18 in Nature. These arrays could be the first shown to benefit from the sloppiness of natural fabrication, says coauthor Silvia Vignolini, a physicist specializing in nanoscale optics at the University of Cambridge.
Flowers, of course, can’t reach industrial standards for uniform nanoscale fabrication. Yet the halo may be a case where natural imperfections may be important to a flower’s display. Tests with artificial flowers showed that the nanoglitches made it easier for bees to learn that a showy petal meant a sugary reward, Vignolini and colleagues found.
Blues are rare in actual pigments in living things( SN: 12/10/16, p. 4 ). Color in the wings of Morpho butterflies or blue jay feathers, for instance, comes from nanoscale structures that contain no pigments but create colorful illusions by muting some wavelengths of light while intensely reflecting others ( SN: 6/11/16, p. 32 ).
Flower petals make their blue halo illusion with somewhat irregular versions of what are called diffraction gratings, rows of ridges like the recording surface on a CD. A perfectly regular array of ridges would create true iridescence, changing color depending on the angle a viewer takes. The flowers’ imperfections, variations in ridge height and spacing, weaken or destroy the iridescence. A viewer swooping by would see less color shifting and more of a bluish-ultraviolet tinge reflected at a wider range of angles.

To see whether bees respond more to iridescence or a blue halo, researchers created sets of artificial flowers, pieces of epoxy resin with some kind of nanoscale-ridged array. A petal-scale project was huge compared with the usual nanoscale experiments, requiring marathon fabrication sessions. “We were a pain to everybody,” Vignolini says.

In two tests, researchers offered bumblebees a pair of “flowers,” one that held sugar water and one with a nasty-tasting solution, to see how quickly bees would learn to distinguish sweet from foul. When the flower’s nanoridges had imperfections creating a blue halo, bees learned the task faster than when the flower had perfect iridescence. Imperfect arrays were actually an advantage for the flowers in creating displays pollinating bees find memorable, the researchers conclude.
Such disorder in nature’s structural color (versus pigments) has shown up before, as in obviously jumbled color-trick structures in bird feathers. Before the tests, though, it was unclear whether flowers would benefit from perfect iridescence and were just falling short in growing perfect arrays. The blue halo might have been merely a side effect of challenging botanical fabrication. The bee experiments, however, showed the opposite, the researchers say. These are the first tests to show that some disorder is not just a downside of natural fabrication but in itself “has a function,” Vignolini says.

That result makes sense to visual ecologist Nathan Morehouse of the University of Cincinnati. Nanostructures that iridesce may often just be a way birds or butterflies can create an unusual color rather than a way to produce iridescence for its own sake. The shifting colors might even have a downside. By definition, true iridescence changes color as an insect or bird changes its angle of approach, and so may not be the best form for an easy-to-remember signal. “Iridescence itself is something they just have to manage,” he suggests.

Alligators don’t just stick to freshwater and the prey they find there. These crafty reptiles can live quite easily, at least for a bit, in salty waters and find plenty to eat — including crabs, sea turtles and even sharks.

“They should change the textbooks,” says James Nifong, an ecologist with the Kansas Cooperative Fish and Wildlife Research Unit at Kansas State University in Manhattan, who has spent years documenting the estuarine gator diet.

Nifong’s most recent discovery, splashed all over the news last month, is that the American alligator (Alligator mississippiensis) eats at least three species of shark and two species of rays, he and wildlife biologist Russell Lowers report in the September Southeastern Naturalist.

Lowers captured a female gator with a young Atlantic stingray in her jaws near where he works at Kennedy Space Center in Cape Canaveral, Florida. And he and Nifong gathered several other eyewitness accounts: A U.S. Fish and Wildlife employee spotted a gator consuming a nurse shark in a Florida mangrove swamp in 2003. A birder photographed an alligator eating a bonnethead shark in a Florida salt marsh in 2006. One of Nifong’s collaborators, a marine turtle researcher, saw gators consuming both bonnethead and lemon sharks in the late 1990s. And Nifong found yet another report of a gator eating a bonnethead shark in Hilton Head, S.C., after their paper was published. All of these snacks required gators to venture into salty waters.
But shark may not be the most surprising item on the alligator estuarine menu. Nifong spent years catching hundreds of wild gators and pumping their stomachs to figure out what they eat, work that relies “on electrical tape, duct tape and zip ties,” Nifong says. And he found that the menu is pretty long.

To snag an alligator, he uses a big blunted hook or, with smaller animals, just grabs the animal and hauls it into the boat. He gets a noose around its neck. Then the researchers tape the mouth shut, take body measurements (everything from weight to toe length) and get blood or urine samples.

Once that’s out of the way, the team will strap the gator to a board with Velcro ties or rope. Then, it’s time to untape the mouth, quickly insert a piece of pipe to hold it open, and tape the alligator’s mouth around the pipe. The pipe, Nifong says, is there “so they can’t bite down.” And that’s important, because next someone has to stick a tube down the gator’s throat and hold it there to keep the animal’s throat open.
Finally, “we fill [the stomach] up with water very slowly so we don’t injure the animal,” Nifong says. “Then we do basically the Heimlich maneuver.” Pressing down on the abdomen forces the gator to give up its stomach contents. Usually.
“Sometimes it goes better than other times,” he says. “They can just decide to not let it out.” Then the researchers carefully undo all their work to let the gator loose.

Back in the lab, Nifong and his colleagues teased out what they could find in those stomach contents, and looked for more clues about the animals’ diet from in the blood samples. Nifong and his colleagues found that the gators were eating a rich marine diet, including small fish, mammals, birds, insects and crustaceans. They’ll even eat fruit and seeds. The sharks and rays didn’t show up in these studies (nor did sea turtles, which gators have also been spotted munching on). But Nifong and Lowers speculate that’s because the tissue of those animals gets digested very quickly. So if a gator had eaten a shark more than a few days before being caught, there was no way to know.

Because alligators don’t have any salt glands, “they’re subject to the same pressures as me or you when being out in saltwater,” Nifong says. “You’re losing water, and you’re increasing salt in your blood system.” That can lead to stress and even death, he notes. So the gators tend to just go back and forth between saltwater and freshwater. They can also close off their throat with a cartilaginous shield and shut their nostrils to keep salty water out. And when they eat, they’ll tip their head up to let the saltwater drain out before gulping down their catch.
What alligators eat isn’t as important a finding as the discovery that they regularly travel between saltwater and freshwater environments, Nifong says. And, he notes, “it occurs across a wide variety of habitats across the U.S. southeast.” That’s important because the gators are moving nutrients from rich marine waters into poorer, fresh waters. And they may be having a larger effect on estuarine food webs that anyone had imagined.

For instance, one of the prey items on the alligator menu is blue crab. Gators “scare the bejesus out of them,” Nifong says. And when gators are around, blue crabs decrease their predation of snails, which might then eat more of the cordgrass that forms the base of the local ecosystem. “Understanding that an alligator has a role in that kind of interaction,” Nifong points out, is important when planning conservation efforts.

Artificial cells made from scratch in the lab could one day offer a more effective, patient-friendly diabetes treatment.

Diabetes, which affects more than 400 million people around the world, is characterized by the loss or dysfunction of insulin-making beta cells in the pancreas. For the first time researchers have created synthetic cells that mimic how natural beta cells sense blood sugar concentration and secrete just the right amount of insulin. Experiments with mice show that these cells can regulate blood sugar for up to five days, researchers report online October 30 in Nature Chemical Biology.
If the mouse results translate to humans, diabetics could inject these artificial beta cells to automatically regulate their blood sugar levels for days at a time.

That would be a “a huge leap forward” for diabetic patients who currently have to check their blood sugar and inject insulin several times a day, says Omid Veiseh, a bioengineer at Rice University in Houston who wasn’t involved in the research. “Even if it were just a one-day thing, it would still be impressive,” he says.
Fashioned from human-made materials and biological ingredients like proteins, these faux cells contain insulin-filled pouches much like the insulin-carrying compartments inside real beta cells. And, similar to a natural beta cell, when one of these artificial beta cells is surrounded by excess blood sugar, its insulin sacs fuse with its outer membrane and eject insulin into the bloodstream. As blood sugar levels drop, insulin packets stop fusing with the membrane, which stems the cell’s insulin secretion.
Fabricating artificial insulin delivery systems that actually imitate the inner workings of real beta cells for ultrafine blood sugar regulation is “an engineering feat,” says Patrik Rorsman, a diabetes researcher at the University of Oxford who wasn’t involved in the work. The cellular imitations are “not as perfect as the beta cells we’re equipped with when we’re healthy,” he adds. For one thing, the faux cells eventually run out of insulin to release. But Rorsman believes that such artificial cells present a viable diabetes treatment.
Unlike transplanted beta cells — or other types of real cells genetically engineered to release insulin for diabetes treatment (SN: 1/15/11, p. 9) — these artificial cells could be mass-produced and have a much longer shelf life than live cells, says study coauthor Zhen Gu, a biomedical engineer at the University of North Carolina at Chapel Hill.

When Gu and colleagues injected their synthetic cells into diabetic mice, the animals’ blood sugar levels normalized within an hour and stayed that way up to five days, when the cells ran out of insulin. The researchers plan to perform further tests on lab animals to assess the fake cells’ long-term health effects before running clinical trials.

Even for patients who manage their insulin with automated mechanical pumps (SN Online: 5/8/10), synthetic cells offer the advantage of more precise, real time blood sugar regulation, says Michael Strano, a bioengineer at MIT. The creation of the new faux cells not only poses a potential diabetes treatment, “but it’s also a bellwether. It’s slightly ahead of its time,” says Strano. “I think therapeutics of the future are going to look like this.”

It would be a memorable sight. But it would also be so wrong to tip over Galápagos giant tortoises to see how shell shape affects their efforts to leg-pump, neck-stretch and rock right-side up again.

Shell shape matters, says evolutionary biologist Ylenia Chiari, though not the way she expected. It’s taken years, plus special insights from a coauthor who more typically studies scorpions, for Chiari and her team to measure and calculate their way to that conclusion. But no endangered species have been upended in the making of the study.
“They’re amazing,” says Chiari of the dozen or so species of Chelonoidis grazing over the Galápagos Islands. Hatchlings start not quite the size of a tennis ball and after decades, depending on species and sex, “could be like — a desk,” says Chiari, of the University of South Alabama in Mobile.

Two extremes among the species’ shell shapes intrigue Chiari: high-domed mountains versus mere hillocks called saddlebacks because of an upward flare saddling the neck. Researchers have dreamed up possible benefits for the shell differences, such as the saddleback flare letting tortoises stretch their necks higher upward in grazing on sparse plants.
At the dryer, lower altitudes where saddleback species tend to live, fields of lava chunks and cacti make walking treacherous. “I fell on a cactus once,” Chiari says. Tortoises tumble over, too, and she wondered whether saddleback shells might be easier to set right again.
She went paparazzi on 89 tortoise shells, taking images from multiple angles to create a 3-D computerized version of each shell. Many shells were century-old museum specimens from the California Academy of Sciences in San Francisco, but she stalked some in the wild, too. The domed tortoises tended to pull into their shells with a huffing noise during their time in front of the lens and just wait till the weirdness ended. A saddleback species plodded toward the interruption, though, butting and biting (toothless but emphatic) at her legs.

To calculate energy needed to rock and roll the two shell types, Chiari needed to know the animals’ centers of mass. No one, however, had measured it for any tortoise. Enter coauthor Arie van der Meijden of CIBIO, Research Center in Biodiversity and Genetic Resources at the University of Porto in Portugal. With expertise in biomechanics, he scaled up from the arthropods he often studies. For a novel test of tortoises, he arranged for a manufacturer to provide equipment measuring force exerted at three points under a tiltable platform. As the first giant tortoise, weighing in at about 100 kilograms, started to lumber aboard the platform at Rotterdam’s zoo, Chiari thought, “Oh my gosh, it’s going to crush everything.” For a gentler and more even landing, four men heaved the tortoise into position.

Calculating the centers of mass for Rotterdam tortoises, the researchers extrapolated to the 89 shells. The low, flattened saddleback shape actually made shells tougher to right, taking more energy, the team reports November 30 in Scientific Reports. Now Chiari muses over whether the saddle at the shell front might let freer neck movements compensate after a trip and a flip.