Boa constrictors don’t so much suffocate prey as break their hearts. It turns out that the snakes kill like demon blood pressure cuffs, squeezing down circulation to its final stop. The notion that constrictors slay by preventing breathing turns out to be wrong.
The snakes don’t need limbs, or even venom, to bring down an animal of their own size. “Imagine you’re killing and swallowing a 150-pound animal in one meal — with no hands or legs!” animal ecologist Scott Boback tells his students at Dickinson College in Carlisle, Pa., to convey what extraordinary hunters snakes are. Speed matters with prey flailing claws, hooves or other weaponry the snake lacks. Embracing prey into heart failure is faster than suffocating it and appeared in different forms multiple times in snake history. Ambushing birds, monkeys and a wide range of other animals from Mexico south to Argentina, the iconic Boa constrictor attacks in much the same way each time. The snake cinches a loop or two around the upper body of prey, pressing against its victim hard enough to starve organs of oxygenated blood.
“It’s not some unbelievable amount of pressure,” says Boback, whose arms get snaked now and then. “It stings a little — you can kind of feel the blood stop,” he says. Within six seconds of looping around an anesthetized lab rat, a boa constrictor squeezes enough to halve blood pressure in a rear-leg artery. Blood that should surge through the artery lies dammed behind snake coils in the rat’s upper body. And back pressure keeps the rat heart from pumping out new blood. Circulation falters and fails. Boas release their grip after about six minutes on average, Boback and his colleagues report in the July 15 Journal of Experimental Biology.
Then the boa swallows the catch whole. A rat about a quarter of the snake’s weight disappears down the gullet in a couple of minutes. Moveable bones in the head help the snake make the gulp, as does a dimple of stretchy cartilage that lets the chin open wide. But what people most often tell Boback — that snake jaws must separate at the back — is just another serpentine myth.
Icicles made from pure water give scientists brain freeze.
In nature, most icicles are made from water with a hint of salt. But lab-made icicles free from salt disobey a prominent theory of how icicles form, and it wasn’t clear why. Now, a study is helping to melt away the confusion.
Natural icicles tend to look like skinny cones with rippled surfaces — the result of a thin film of water that coats the ice, researchers think (SN: 11/24/13). As icicles grow, the film freezes. Any preexisting small bumps in the icicle get magnified into large ripples because the water layer is thinner above the bumps and can freeze more readily. But this theory fails to explain the salt-free variety, which have more irregular shapes reminiscent of drippy candles, says physicist Menno Demmenie of the University of Amsterdam. So Demmenie and colleagues grew icicles in the lab, adding a blue dye that was visible only when the water was liquid. Salted icicles not only had ripples, but they also were covered in a thin, blue film. Icicles made from pure water had no such film. Only small droplets of blue appeared on those icicles, the team reports in the February Physical Review Applied.
In icicles with salt, the temperature at which the water on the surface freezes is lowered, allowing a liquid layer to coat the entire icicle. Without the salt, icicles must build up drop by drop.
Tiny, pond-dwelling Halteria ciliates are virovores, able to survive on a virus-only diet, researchers report December 27 in Proceedings of the National Academy of Sciences. The single-celled creatures are the first known to thrive when viruses alone are on the menu.
Scientists already knew that some microscopic organisms snack on aquatic viruses such as chloroviruses, which infect and kill algae. But it was unclear whether viruses alone could provide enough nutrients for an organism to grow and reproduce, says ecologist John DeLong of the University of Nebraska–Lincoln. In laboratory experiments, Halteria that were living in water droplets and given only chloroviruses for sustenance reproduced, DeLong and colleagues found. As the number of viruses in the water dwindled, Halteria numbers went up. Ciliates without access to viral morsels, or any other food, didn’t multiply. But Paramecium, a larger microbe, didn’t thrive on a virus-only diet, hinting that viruses can’t satisfy the nutritional requirements for all ciliates to grow.
Viruses could be a good source of phosphorus, which is essential for making copies of genetic material, DeLong says. But it probably takes a lot of viruses to account for a full meal.
In the lab, each Halteria microbe ate about 10,000 to 1 million viruses daily, the team estimates. Halteria in small ponds with abundant viral snacks might chow down on about a quadrillion viruses per day.
These feasts could shunt previously unrecognized energy into the food web, and add a new layer to the way viruses move carbon through an ecosystem — if it happens in the wild, DeLong says (SN: 6/9/16). His team plans to start finding out once ponds in Nebraska thaw.
In full swing The swaying feeling in jazz music that compels feet to tap may arise from near-imperceptible delays in musicians’ timing, Nikk Ogasa reported in “Jazz gets its swing from small, subtle delays” (SN: 11/19/22, p. 5).
Reader Oda Lisa, a self-described intermediate saxophonist, has noticed these subtle delays while playing.“I recorded my ‘jazzy’ version of a beloved Christmas carol, which I sent to a friend of mine,” Lisa wrote. “She praised my effort overall, but she suggested that I get a metronome because the timing wasn’t consistent. My response was that I’m a slave to the rhythm that I hear in my head. I think now I know why.” On the same page Murky definitions and measurements impede social science research, Sujata Gupta reported in “Fuzzy definitions mar social science” (SN: 11/19/22, p. 10).
Reader Linda Ferrazzara found the story thought-provoking. “If there’s no consensus on the terms people use … then there can be no productive discussion or conversation. People end up talking and working at cross-purposes with no mutual understanding or progress,” Ferrazzara wrote.
Fly me to the moon Space agencies are preparing to send the next generation of astronauts to the moon and beyond. Those crews will be more diverse in background and expertise than the crews of the Apollo missions, Lisa Grossman reported in “Who gets to go to space?” (SN: 12/3/22, p. 20).
“It is great to see a broader recognition of the work being done to make spaceflight open to more people,” reader John Allen wrote. “Future space travel will and must accommodate a population that represents humanity. It won’t be easy, but it will be done.”
The story also reminded Allen of the Gallaudet Eleven, a group of deaf adults who participated in research done by NASA and the U.S. Navy in the 1950s and ’60s. Experiments tested how the volunteers responded (or didn’t) to a range of scenarios that would typically induce motion sickness, such as a ferry ride on choppy seas. Studying how the body’s sensory systems work without the usual gravitational cues from the inner ear allowed scientists to better understand motion sickness and the human body’s adaptation to spaceflight.
Sweet dreams are made of this A memory-enhancing method that uses sound cues may boost an established treatment for debilitating nightmares, Jackie Rocheleau reported in “Learning trick puts nightmares to bed” (SN: 12/3/22, p. 11).
Reader Helen Leaver shared her trick to a good night’s sleep: “I learned that I was having strong unpleasant adventures while sleeping, and I would awaken hot and sweaty. By eliminating the amount of heat from bedding and an electrically heated mattress pad, I now sleep well without those nightmares.” Pest perspectives In “Why do we hate pests?” (SN: 12/3/22, p. 26), Deborah Balthazar interviewed former Science News Explores staff writer Bethany Brookshire about her new book, Pests. The book argues that humans — influenced by culture, class, colonization and much more — create animal villains.
The article prompted reader Doug Clapp to reflect on what he considers pests or weeds. “A weed is a plant in the wrong place, and a pest is an animal in the wrong place,” Clapp wrote. But what’s considered “wrong” depends on the humans who have power over the place, he noted. “Grass in a lawn can be a fine thing. Grass in a garden choking the vegetables I’m trying to grow becomes a weed. Mice in the wild don’t bother me. Field mice migrating into my house when the weather cools become a pest, especially when they eat into my food and leave feces behind,” Clapp wrote.
The article encouraged Clapp to look at pests through a societal lens: “I had never thought of pests in terms of high-class or low-class. Likewise, the residual implications of [colonization]. Thanks for provoking me to consider some of these issues in a broader context.”
As far back as roughly 25,000 years ago, Ice Age hunter-gatherers may have jotted down markings to communicate information about the behavior of their prey, a new study finds.
These markings include dots, lines and the symbol “Y,” and often accompany images of animals. Over the last 150 years, the mysterious depictions, some dating back nearly 40,000 years, have been found in hundreds of caves across Europe.
Some archaeologists have speculated that the markings might relate to keeping track of time, but the specific purpose has remained elusive (SN: 7/9/19). Now, a statistical analysis, published January 5 in Cambridge Archeological Journal, presents evidence that past people may have been recording the mating and birthing schedule of local fauna. By comparing the marks to the animals’ life cycles, researchers showed that the number of dots or lines in a given image strongly correlates to the month of mating across all the analyzed examples, which included aurochs (an extinct species of wild cattle), bison, horses, mammoth and fish. What’s more, the position of the symbol “Y” in a sequence was predictive of birth month, suggesting that “Y” signifies “to give birth.”
The finding is one of the earliest records of a coherent notational system, the researchers say. It indicates that people at the time were able to interpret the meaning of an item’s position in a sequence and plan ahead for the distant future using a calendar of sorts — reinforcing the suggestion that they were capable of complex cognition. “This is a really big deal cognitively,” says Ben Bacon, an independent researcher based in London. “We’re dealing with a system that has intense organization, intense logic to it.”
A furniture conservator by day, Bacon spent years poring through scientific articles to compile over 800 instances of these cave markings. From his research and reading the literature, he reasoned that the dots corresponded to the 13 lunar cycles in a year. But he thought that the hunter-gatherers would’ve been more concerned with seasonal changes than the moon.
In the new paper, he and colleagues argue that rather than pinning a calendar to astronomical events like the equinox, the hunter-gatherers started their calendar year with the snowmelt in the spring. Not only would the snowmelt be a clear point of origin, but the meteorological calendar would also account for differences in timing across locations. For example, though snowmelt would start on different dates in different latitudes, bison would always mate approximately four lunar cycles — or months — after that region’s snowmelt, as indicated by four dots or lines.
“This is why it’s such a clever system, because it’s based on the universal,” Bacon says. “Which means if you migrate from the Pyrenees to Belgium, you can just use the same calendar.”
He needed data to prove his idea. After compiling the markings, he worked with academic researchers to identify the timing of migration, mating and birth for common Ice Age animals targeted by hunter-gatherers by using archaeological data or comparing with similar modern animals. Next, the researchers determined if the marks aligned significantly with important life events based on this calendar. When the team ran the statistical analysis, the results strongly supported Bacon’s theory.
When explaining the markings, “we’ve argued for notational systems before, but it’s always been fairly speculative as to what the people were counting and why they were counting,” says Brian Hayden, an archaeologist at Simon Fraser University in Burnaby, British Columbia, who peer-reviewed the paper. “This adds a lot more depth and specificity to why people were keeping calendars and how they were using them.”
Linguistic experts argue that, given the lack of conventional syntax and grammar, the marks wouldn’t be considered writing. But that doesn’t make the finding inherently less exciting, says paleoanthropologist Genevieve von Petzinger of the Polytechnic Institute of Tomar in Portugal, who wasn’t involved in the study. Writing systems are often mistakenly considered a pinnacle of achievement, when in fact writing would be developed only in cultural contexts where it’s useful, she says. Instead, it’s significant that the marks provide a way to keep records outside of the mind.
“In a way, that was the huge cognitive leap,” she says. “Suddenly, we have the ability to preserve [information] beyond the moment. We have the ability to transmit it across space and time. Everything starts to change.”
The debate over these marks’ meanings continues. Archaeologist April Nowell doesn’t buy many of the team’s assumptions. “It boggles my mind why one would need a calendar … to predict that animals were going to have offspring in the spring,” says Nowell, of the University of Victoria in British Columbia. “The amount of information that this calendar is providing, if it really is a calendar, is quite minimal.”
Hayden adds that, while the basic pattern would still hold, some of the cave marks had “wiggle room for interpretation.” The next step, he says, will be to review and verify the interpretations of the marks.
Humankind is seeing Neptune’s rings in a whole new light thanks to the James Webb Space Telescope.
In an infrared image released September 21, Neptune and its gossamer diadems of dust take on an ethereal glow against the inky backdrop of space. The stunning portrait is a huge improvement over the rings’ previous close-up, which was taken more than 30 years ago.
Unlike the dazzling belts encircling Saturn, Neptune’s rings appear dark and faint in visible light, making them difficult to see from Earth. The last time anyone saw Neptune’s rings was in 1989, when NASA’s Voyager 2 spacecraft, after tearing past the planet, snapped a couple grainy photos from roughly 1 million kilometers away (SN: 8/7/17). In those photos, taken in visible light, the rings appear as thin, concentric arcs.
As Voyager 2 continued to interplanetary space, Neptune’s rings once again went into hiding — until July. That’s when the James Webb Space Telescope, or JWST, turned its sharp, infrared gaze toward the planet from roughly 4.4 billion kilometers away (SN: 7/11/22). Neptune itself appears mostly dark in the new image. That’s because methane gas in the planet’s atmosphere absorbs much of its infrared light. A few bright patches mark where high-altitude methane ice clouds reflect sunlight.
And then there are the ever-elusive rings. “The rings have lots of ice and dust in them, which are extremely reflective in infrared light,” says Stefanie Milam, a planetary scientist at NASA’s Goddard Space Flight Center in Greenbelt, Md., and one of JWST’s project scientists. The enormity of the telescope’s mirror also makes its images extra sharp. “JWST was designed to look at the first stars and galaxies across the universe, so we can really see fine details that we haven’t been able to see before,” Milam says.
Upcoming JWST observations will look at Neptune with other scientific instruments. That should provide new intel on the rings’ composition and dynamics, as well as on how Neptune’s clouds and storms evolve, Milam says. “There’s more to come.”
As people around the world marveled in July at the most detailed pictures of the cosmos snapped by the James Webb Space Telescope, biologists got their first glimpses of a different set of images — ones that could help revolutionize life sciences research.
The images are the predicted 3-D shapes of more than 200 million proteins, rendered by an artificial intelligence system called AlphaFold. “You can think of it as covering the entire protein universe,” said Demis Hassabis at a July 26 news briefing. Hassabis is cofounder and CEO of DeepMind, the London-based company that created the system. Combining several deep-learning techniques, the computer program is trained to predict protein shapes by recognizing patterns in structures that have already been solved through decades of experimental work using electron microscopes and other methods. The AI’s first splash came in 2021, with predictions for 350,000 protein structures — including almost all known human proteins. DeepMind partnered with the European Bioinformatics Institute of the European Molecular Biology Laboratory to make the structures available in a public database.
July’s massive new release expanded the library to “almost every organism on the planet that has had its genome sequenced,” Hassabis said. “You can look up a 3-D structure of a protein almost as easily as doing a key word Google search.”
These are predictions, not actual structures. Yet researchers have used some of the 2021 predictions to develop potential new malaria vaccines, improve understanding of Parkinson’s disease, work out how to protect honeybee health, gain insight into human evolution and more. DeepMind has also focused AlphaFold on neglected tropical diseases, including Chagas disease and leishmaniasis, which can be debilitating or lethal if left untreated. The release of the vast dataset was greeted with excitement by many scientists. But others worry that researchers will take the predicted structures as the true shapes of proteins. There are still things AlphaFold can’t do — and wasn’t designed to do — that need to be tackled before the protein cosmos completely comes into focus.
Having the new catalog open to everyone is “a huge benefit,” says Julie Forman-Kay, a protein biophysicist at the Hospital for Sick Children and the University of Toronto. In many cases, AlphaFold and RoseTTAFold, another AI researchers are excited about, predict shapes that match up well with protein profiles from experiments. But, she cautions, “it’s not that way across the board.”
Predictions are more accurate for some proteins than for others. Erroneous predictions could leave some scientists thinking they understand how a protein works when really, they don’t. Painstaking experiments remain crucial to understanding how proteins fold, Forman-Kay says. “There’s this sense now that people don’t have to do experimental structure determination, which is not true.” Plodding progress Proteins start out as long chains of amino acids and fold into a host of curlicues and other 3-D shapes. Some resemble the tight corkscrew ringlets of a 1980s perm or the pleats of an accordion. Others could be mistaken for a child’s spiraling scribbles.
A protein’s architecture is more than just aesthetics; it can determine how that protein functions. For instance, proteins called enzymes need a pocket where they can capture small molecules and carry out chemical reactions. And proteins that work in a protein complex, two or more proteins interacting like parts of a machine, need the right shapes to snap into formation with their partners.
Knowing the folds, coils and loops of a protein’s shape may help scientists decipher how, for example, a mutation alters that shape to cause disease. That knowledge could also help researchers make better vaccines and drugs.
For years, scientists have bombarded protein crystals with X-rays, flash frozen cells and examined them under highpowered electron microscopes, and used other methods to discover the secrets of protein shapes. Such experimental methods take “a lot of personnel time, a lot of effort and a lot of money. So it’s been slow,” says Tamir Gonen, a membrane biophysicist and Howard Hughes Medical Institute investigator at the David Geffen School of Medicine at UCLA. Such meticulous and expensive experimental work has uncovered the 3-D structures of more than 194,000 proteins, their data files stored in the Protein Data Bank, supported by a consortium of research organizations. But the accelerating pace at which geneticists are deciphering the DNA instructions for making proteins has far outstripped structural biologists’ ability to keep up, says systems biologist Nazim Bouatta of Harvard Medical School. “The question for structural biologists was, how do we close the gap?” he says.
For many researchers, the dream has been to have computer programs that could examine the DNA of a gene and predict how the protein it encodes would fold into a 3-D shape.
Here comes AlphaFold Over many decades, scientists made progress toward that AI goal. But “until two years ago, we were really a long way from anything like a good solution,” says John Moult, a computational biologist at the University of Maryland’s Rockville campus.
Moult is one of the organizers of a competition: the Critical Assessment of protein Structure Prediction, or CASP. Organizers give competitors a set of proteins for their algorithms to fold and compare the machines’ predictions against experimentally determined structures. Most AIs failed to get close to the actual shapes of the proteins. Then in 2020, AlphaFold showed up in a big way, predicting the structures of 90 percent of test proteins with high accuracy, including two-thirds with accuracy rivaling experimental methods.
Deciphering the structure of single proteins had been the core of the CASP competition since its inception in 1994. With AlphaFold’s performance, “suddenly, that was essentially done,” Moult says.
Since AlphaFold’s 2021 release, more than half a million scientists have accessed its database, Hassabis said in the news briefing. Some researchers, for example, have used AlphaFold’s predictions to help them get closer to completing a massive biological puzzle: the nuclear pore complex. Nuclear pores are key portals that allow molecules in and out of cell nuclei. Without the pores, cells wouldn’t work properly. Each pore is huge, relatively speaking, composed of about 1,000 pieces of 30 or so different proteins. Researchers had previously managed to place about 30 percent of the pieces in the puzzle. That puzzle is now almost 60 percent complete, after combining AlphaFold predictions with experimental techniques to understand how the pieces fit together, researchers reported in the June 10 Science.
Now that AlphaFold has pretty much solved how to fold single proteins, this year CASP organizers are asking teams to work on the next challenges: Predict the structures of RNA molecules and model how proteins interact with each other and with other molecules.
For those sorts of tasks, Moult says, deep-learning AI methods “look promising but have not yet delivered the goods.”
Where AI falls short Being able to model protein interactions would be a big advantage because most proteins don’t operate in isolation. They work with other proteins or other molecules in cells. But AlphaFold’s accuracy at predicting how the shapes of two proteins might change when the proteins interact are “nowhere near” that of its spot-on projections for a slew of single proteins, says Forman-Kay, the University of Toronto protein biophysicist. That’s something AlphaFold’s creators acknowledge too.
The AI trained to fold proteins by examining the contours of known structures. And many fewer multiprotein complexes than single proteins have been solved experimentally. Forman-Kay studies proteins that refuse to be confined to any particular shape. These intrinsically disordered proteins are typically as floppy as wet noodles (SN: 2/9/13, p. 26). Some will fold into defined forms when they interact with other proteins or molecules. And they can fold into new shapes when paired with different proteins or molecules to do various jobs.
AlphaFold’s predicted shapes reach a high confidence level for about 60 percent of wiggly proteins that Forman-Kay and colleagues examined, the team reported in a preliminary study posted in February at bioRxiv.org. Often the program depicts the shapeshifters as long corkscrews called alpha helices.
Forman-Kay’s group compared AlphaFold’s predictions for three disordered proteins with experimental data. The structure that the AI assigned to a protein called alpha-synuclein resembles the shape that the protein takes when it interacts with lipids, the team found. But that’s not the way the protein looks all the time.
For another protein, called eukaryotic translation initiation factor 4E-binding protein 2, AlphaFold predicted a mishmash of the protein’s two shapes when working with two different partners. That Frankenstein structure, which doesn’t exist in actual organisms, could mislead researchers about how the protein works, Forman-Kay and colleagues say. AlphaFold may also be a little too rigid in its predictions. A static “structure doesn’t tell you everything about how a protein works,” says Jane Dyson, a structural biologist at the Scripps Research Institute in La Jolla, Calif. Even single proteins with generally well-defined structures aren’t frozen in space. Enzymes, for example, undergo small shape changes when shepherding chemical reactions.
If you ask AlphaFold to predict the structure of an enzyme, it will show a fixed image that may closely resemble what scientists have determined by X-ray crystallography, Dyson says. “But [it will] not show you any of the subtleties that are changing as the different partners” interact with the enzyme.
“The dynamics are what Mr. AlphaFold can’t give you,” Dyson says.
A revolution in the making The computer renderings do give biologists a head start on solving problems such as how a drug might interact with a protein. But scientists should remember one thing: “These are models,” not experimentally deciphered structures, says Gonen, at UCLA.
He uses AlphaFold’s protein predictions to help make sense of experimental data, but he worries that researchers will accept the AI’s predictions as gospel. If that happens, “the risk is that it will become harder and harder and harder to justify why you need to solve an experimental structure.” That could lead to reduced funding, talent and other resources for the types of experiments needed to check the computer’s work and forge new ground, he says. Harvard Medical School’s Bouatta is more optimistic. He thinks that researchers probably don’t need to invest experimental resources in the types of proteins that AlphaFold does a good job of predicting, which should help structural biologists triage where to put their time and money.
“There are proteins for which AlphaFold is still struggling,” Bouatta agrees. Researchers should spend their capital there, he says. “Maybe if we generate more [experimental] data for those challenging proteins, we could use them for retraining another AI system” that could make even better predictions.
He and colleagues have already reverse engineered AlphaFold to make a version called OpenFold that researchers can train to solve other problems, such as those gnarly but important protein complexes.
Massive amounts of DNA generated by the Human Genome Project have made a wide range of biological discoveries possible and opened up new fields of research (SN: 2/12/22, p. 22). Having structural information on 200 million proteins could be similarly revolutionary, Bouatta says.
In the future, thanks to AlphaFold and its AI kin, he says, “we don’t even know what sorts of questions we might be asking.”
The answer to one of the greatest mysteries of the universe may come down to one of the smallest, and spookiest, particles.
Matter is common in the cosmos. Everything around us — from planets to stars to puppies — is made up of matter. But matter has a flip side: antimatter. Protons, electrons and other particles all have antimatter counterparts: antiprotons, positrons, etc. Yet for some reason antimatter is much rarer than matter — and no one knows why. Physicists believe the universe was born with equal amounts of matter and antimatter. Since matter and antimatter counterparts annihilate on contact, that suggests the universe should have ended up with nothing but energy. Something must have tipped the balance.
Some physicists think lightweight subatomic particles called neutrinos could point to an answer. These particles are exceedingly tiny, with less than a millionth the mass of an electron (SN: 4/21/21). They’re produced in radioactive decays and in the sun and other cosmic environments. Known for their ethereal tendency to evade detection, neutrinos have earned the nickname “ghost particles.” These spooky particles, originally thought to have no mass at all, have a healthy track record of producing scientific surprises (SN: 10/6/15).
Now researchers are building enormous detectors to find out if neutrinos could help solve the mystery of the universe’s matter. The Hyper-Kamiokande experiment in Hida City, Japan, and the Deep Underground Neutrino Experiment in Lead, S.D., will study neutrinos and their antimatter counterparts, antineutrinos. A difference in neutrinos’ and antineutrinos’ behavior might hint at the origins of the matter-antimatter imbalance, scientists suspect.
Watch the video below to find out how neutrinos might reveal why the universe contains, well, anything at all.
Face masks — the unofficial symbol of the COVID-19 pandemic — are leveling up.
A mask outfitted with special electronics can detect SARS-CoV-2, the virus that causes COVID-19, and other airborne viruses within 10 minutes of exposure, materials researcher Yin Fang and colleagues report September 19 in Matter.
“The lightness and wearability of this face mask allows users to wear it anytime, anywhere,” says Fang, of Tongji University in Shanghai. “It’s expected to serve as an early warning system to prevent large outbreaks of respiratory infectious diseases.” Airborne viruses can hitch a ride between hosts in the air droplets that people breathe in and out. People infected with a respiratory illness can expel thousands of virus-containing droplets by talking, coughing and sneezing. Even those with no signs of being sick can sometimes pass on these viruses; people who are infected with SARS-CoV-2 can start infecting others at least two to three days before showing symptoms (SN: 3/13/20). So viruses often have a head start when it comes to infecting new people.
Fang and his colleagues designed a special sensor that reacts to the presence of certain viral proteins in the air and attached it to a face mask. The team then spritzed droplets containing proteins produced by the viruses that cause COVID-19, bird flu or swine flu into a chamber with the mask.
The sensor could detect just a fraction of a microliter of these proteins — a cough might contain 10 to 80 times as much. Once a pathogen was detected, the sensor-mask combo sent a signal to the researchers informing them of the virus’s presence. Ultimately, the researchers plan for such signals to be sent to a wearer’s phone or other devices. By combining this technology with more conventional testing, the team says, health care providers and public health officials might be able to better contain future pandemics.
Getting out into society after a long isolation gets awkward. Ask the Pahrump poolfish, loners in a desert for some 10,000 years.
This hold-in-your-hand-size fish (Empetrichthys latos) has a chubby, torpedo shape and a mouth that looks as if it’s almost smiling. Until the 1950s, this species had three forms, each evolving in its own spring. Now only one survives, which developed in a spring-fed oasis in the Mojave Desert’s Pahrump Valley, about an hour’s drive west of Las Vegas.
Fish in a desert are not that weird when you take the long view (SN: 1/26/16). In a former life, some desert valleys were ancient lakes. As the region’s lakes dried up, fish got stuck in the remaining puddles. Various stranded species over time adapted to quirks of their private microlakes, and a desert-fish version of the Galapagos Islands’ diverse finches arose. “We like to say that Darwin, if he had a different travel agent, could have come to the same conclusions just from the desert,” says evolutionary biologist Craig Stockwell of North Dakota State University in Fargo.
The desert “island” where E. latos evolved was Manse Spring on a private ranch. From a distance, the spring looked “just like a little clump of trees,” remembers ecologist Shawn Goodchild, who is now based in Lake Park, Minn. The spot of desert greenery surrounded the Pahrump poolfish’s entire native range, about the length of an Olympic swimming pool.
By the 1960s, biologists feared the fish were doomed. The spring’s flow rate had dropped some 70 percent as irrigation for farms in the desert sucked away water. And disastrous predators arrived: a kid’s discarded goldfish. Conservation managers fought back, but neither poison nor dynamite wiped out the newcomers. And then in August of 1975, Manse Spring dried up.
Conservation managers had moved some poolfish to other springs, but the long-isolated species just didn’t seem to get the dangers of living with other kinds of fishes. The poolfish were easily picked off by predators in their new home.
Lab tests of fake fish-murder scenes may help explain why. For instance, researchers tainted aquarium water with pureed fish bits. In an expected reaction, fathead minnows (Pimephales promelas) freaked at traces of dead minnow drifting through the water and huddled low in the tank. The Pahrump poolfish in water tainted with blender-whizzed skin of their kind just kept swimming around the upper waters as if corpse taint were no scarier than tap water. Literally. Stockwell and colleagues can say that because they ran a fear test with nonscary dechlorinated tap water. Poolfish didn’t huddle then either, the team reports in the Aug. 31 Proceedings of the Royal Society B.
Then, however, Stockwell and a colleague were musing about some rescued poolfish in cattle tanks when nearby dragonflies caught the researchers’ attention.
Before dragonflies mature into shimmering aerial marvels, the young prowl underwater as violent predators. In moves worthy of scary aliens in a sci-fi movie, many dragonfly nymphs can shoot their jaws out from their head to scoop up prey, including fish eggs and fish larvae. With young dragonflies prowling a pool’s bottom and plants, poolfish moving up the water column “would be a good way to reduce their risk,” Stockwell says. Testing of that idea has begun.
Fish that people thought were foolishly naïve may just be savvy in a different way. Especially after isolation in a desert with dragons.
