What Protects Elephants from Cancer?

Elephants and other large animals have a lower incidence of cancer than would be expected statistically, suggesting that they have evolved ways to protect themselves against the disease. A new study reveals how elephants do it: An old gene that was no longer functional was recycled from the vast “genome junkyard” to increase the sensitivity of elephant cells to DNA damage, enabling them to cull potentially cancerous cells early.

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Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

In multicellular animals, cells go through many cycles of growth and division. At each division, cells copy their entire genome, and inevitably a few mistakes creep in. Some of those mutations can lead to cancer. One might think that animals with larger bodies and longer lives would therefore have a greater risk of developing cancer. But that’s not what researchers see when they compare species across a wide range of body sizes: The incidence of cancer does not appear to correlate with the number of cells in an organism or its lifespan. In fact, researchers find that larger, longer-lived mammals have fewer cases of cancer. In the 1970s, the cancer epidemiologist Richard Peto, now a professor of medical statistics and epidemiology at the University of Oxford, articulated this surprising phenomenon, which has come to be known as Peto’s paradox.

The fact that larger animals like elephants do not have high rates of cancer suggests that they have evolved special cancer suppression mechanisms. In 2015, Joshua Schiffman at the University of Utah School of Medicine and Carlo Maley at Arizona State University headed a team of researchers who showed that the elephant genome has about 20 extra duplicates of p53, a canonical tumor suppressor gene. They went on to suggest that these extra copies of p53 could account, at least in part, for the elephants’ enhanced cancer suppression capabilities. Currently, Lisa M. Abegglen, a cell biologist at the Utah School of Medicine who contributed to the study, is leading a project to find out whether the copies of p53 have different functions.

Vincent Lynch, a geneticist at the University of Chicago, has shown that part of what enabled elephants to grow so big was that one of their pseudogenes—a broken duplicate of an ancestral gene—suddenly acquired a new function.

Courtesy of Vincent J. Lynch

Yet extra copies of p53 are not the elephants’ only source of protection. New work led by Vincent Lynch, a geneticist at the University of Chicago, shows that elephants and their smaller-bodied relatives (such as hyraxes, armadillos and aardvarks) also have duplicate copies of the LIF gene, which encodes for leukemia inhibitory factor. This signaling protein is normally involved in fertility and reproduction and also stimulates the growth of embryonic stem cells. Lynch presented his work at the Pan-American Society for Evolutionary Developmental Biology meeting in Calgary in August 2017, and it is currently posted on biorxiv.org.

Lynch found that the 11 duplicates of LIF differ from one another but are all incomplete: At a minimum they all lack the initial block of protein-encoding information as well as a promoter sequence to regulate the activity of the gene. These deficiencies suggested to Lynch that none of the duplicates should be able to perform the normal functions of a LIF gene, or even be expressed by cells.

The eminent biologist Richard Peto, now at the University of Oxford, pointed out in the 1970s that elephants and other large-bodied animals ought to be at great statistical risk for cancer.

Cathy Harwood

But when Lynch looked in cells, he found RNA transcripts from at least one of the duplicates, LIF6, which indicated that it must have a promoter sequence somewhere to turn it on. Indeed, a few thousand bases upstream of LIF6 in the genome, Lynch and his collaborators discovered a sequence of DNA that looked like a binding site for p53 protein. It suggested to them that p53 (but not any of the p53 duplicates) might be regulating the expression of LIF6. Subsequent experiments on elephant cells confirmed this hunch.

To discover what LIF6 was doing, the researchers blocked the gene’s activity and subjected the cells to DNA-damaging conditions. The result was that the cells became less likely to destroy themselves through a process called apoptosis (programmed cell death), which organisms often use as a kind of quality control system for eliminating defective tissue. LIF6 therefore seems to help eradicate potentially malignant cells. Further experiments indicated that LIF6 triggers cell death by creating leaks in the membranes around mitochondria, the vital energy-producing organelles of cells.

To find out more about the evolutionary history of LIF and its duplicates, Lynch found their counterparts in the genomes of closely related species: manatees, hyraxes and extinct mammoths and mastodons. His analysis suggested that the LIF gene was duplicated 17 times and lost 14 times during the evolution of the elephant’s lineage. Hyraxes and manatees have LIF duplicates, but the p53 duplicates appear only in living and extinct elephants, which suggests that the LIF duplications happened earlier in evolution.

Elephants are closely related to large animals such as manatees (left), but also to smaller ones like hyraxes (right), aardvarks and armadillos. Elephants only began to develop their immense size about 30 million years ago.

Jim P. Reid, USFWS / Bjørn Christian Tørrissen

Lynch found that most duplicates of the LIF gene are pseudogenes—old, mutated, useless copies of genes that survive in the genome by chance. The exception, however, is the LIF6 gene sequence, which unlike the others has not accumulated random mutations, implying that natural selection is preserving it.

“We think that LIF6 is a refunctionalized pseudogene,” Lynch said. That is, the elephant LIF6 re-evolved into a functional gene from a pseudogene ancestor. Because it came back from the dead and plays a role in cell death, Lynch called it a “zombie gene.”

Although manatees and hyraxes also have extra copies of LIF, only modern and extinct elephants have LIF6, which suggests that it evolved only after the elephants branched away from those related species. And when Lynch’s group dated the origin of LIF6 by molecular clock methods, they found that the pseudogene regained a function about 30 million years ago, when the fossil record indicates that elephants were evolving large body sizes.

Lucy Reading-Ikkanda/Quanta Magazine

“Refunctionalizing a pseudogene is not something that happens every day,” explained Stephen Stearns, an evolutionary biologist at Yale University, in an email to Quanta. Being able to show that it happened at roughly the same time that elephants evolved a large body, he wrote, “supports, but does not prove, that the refunctionalizing of the gene was a precondition for the evolution of large body size.”

Evolving protections against cancer would seem to be in the interest of all animals, so why don’t they all have a refunctionalized LIF6 gene? According to the researchers, it’s because this protection comes with risks. LIF6 suppresses cancer, but extra copies of LIF6 would kill the cell if they accidentally turned on. “There’s a bunch of toxic pseudogenes sitting there” in the genome, Lynch explained in an email. “If they get inappropriately expressed, it’s basically game over.”

There also appears to be a trade-off between cancer suppression mechanisms and fertility. A study published in 2009 suggested that LIF is critical for implantation of the embryo in the uterus. Because LIF activity is controlled by p53, LIF and p53 jointly regulate the efficiency of reproduction. When the same set of genes has two functions (such as reproduction and cancer suppression), it is possible that those functions will be in direct conflict—a phenomenon that geneticists call antagonistic pleiotropy.

The elephants may have solved the problem of antagonistic pleiotropy by duplicating p53 and LIF and splitting up those functions, according to Maley. “Some copies of p53 and LIF are doing what’s necessary for fertility, while other pairs of LIF and p53 are doing what’s necessary for cancer suppression,” he said. Maley speculated that the gene duplicates “allowed the elephants to get better at cancer suppression and still maintain their fertility, which would allow them to grow a larger body.” That hypothesis, however, still needs to be tested, he said.

Bats are not large animals, but some species live for decades. Scientists are investigating whether they have their own protective adaptations against cancer.

Ann Froschauer, USFWS

Evolving extra copies of p53 and LIF may have helped elephants overcome Peto’s paradox, but that can’t be the only solution: Other large animals like whales have only one copy of p53 and one version of LIF. Lynch and his team are currently exploring how whales and bats solve Peto’s paradox. Although not large-bodied, some bat species live up to 30 years, and the longer-lived ones might have evolved cancer suppression mechanisms that the shorter-lived ones lack.

Maley is also working on how whales solve Peto’s paradox. Even though whales don’t have extra copies of p53, he said, “we do think there has been a lot of selection and evolution on genes in the p53 pathway.” Maley believes that understanding how diverse large-bodied animals solve Peto’s paradox may have applications in human health. “That is the end goal,” he said. “The hope is that by seeing how evolution has found a way to prevent cancer, we could translate that into better cancer prevention in humans.”

“Every organism that evolved large body size probably has a different solution to Peto’s paradox,” Maley said. “There’s a bunch of discoveries that are just waiting for us out there in nature, where nature is showing us the way to prevent cancer.”

Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

Just how colors Vision stumbled on the Animals

Animals live color. Wasps buzz with painted warnings. Birds shimmer their iridescent desires. Fish hide from predators with body colors that dapple like light across a rippling pond. And all this color on every one of these animals happened because other creatures could view it.

The normal world can be so showy, it’s no wonder boffins have been attracted to animal color for hundreds of years. Even now, the questions how pets see, create, and use color are among the most compelling in biology.

Before the last couple of years, they were additionally at the least partially unanswerable—because color scientists are only peoples, which means that they can’t begin to see the rich, vivid colors that other animals do. But now brand new technologies, like portable hyperspectral scanners and cameras tiny sufficient to match on a bird’s head, are assisting biologists see the unseen. And also as described in a new Science paper, it’s really a totally new world.

Visions of life

The basic principles: Photons strike a surface—a rock, a plant, another animal—and that area absorbs some photons, reflects others, refracts still others, all based on the molecular arrangement of pigments and structures. Some of these photons find their way into an animal’s eye, where specific cells transmit the signals of these photons toward animal’s brain, which decodes them as colors and forms.

Oahu is the mind that determines or perhaps a colorful thing is a distinct and interesting form, not the same as the photons through the trees, sand, sky, lake, an such like it received as well. If it is effective, it has to decide whether this colorful thing is food, a potential mate, or maybe a predator. “The biology of color is all about these complex cascades of activities,” says Richard Prum, an ornithologist at Yale University and co-author for the paper.

In the beginning, there was light and there is dark. Which, fundamental greyscale vision probably developed first, because pets that could anticipate the dawn or skitter far from a shadow are pets that live to breed. And very first eye-like structures—flat spots of photosensitive cells—probably did not resolve much more than that. It wasn’t sufficient. “The problem with making use of simply light and dark is that the information is quite noisy, and something problem which comes up is determining where one object stops and a different one begins. ” states Innes Cuthill, a behavioral ecologist within University of Bristol and coauthor regarding the new review.

Colors adds context. And context on a scene is an evolutionary benefit. So, just like with smart phones, better resolution and brighter colors became competitive enterprises. The quality bit, the area light-sensing cells developed over countless years right into a appropriate eye—first by recessing right into a cup, then a cavity, and eventually a fluid-filled spheroid capped having a lens. For color, look much deeper at those light-sensing cells. Wedged within their surfaces are proteins called opsins. Each time they get hit with a photon—a quantum little bit of light itself—they transduce that sign into an electrical zap toward rudimentary animal’s rudimentary brain. The first light/dark opsin mutated into spin-offs that may detect certain ranges of wavelengths. Colors vision was so essential it developed individually multiple times within the animal kingdom—in mollusks, arthropods, and vertebrates.

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In fact, primitive fish had four different opsins, to sense four spectra—red, green, blue, and ultraviolet light. That four-fold ability is known as tetrachromacy, and also the dinosaurs most likely had it. As they are the ancestors of today’s wild birds, many of them are tetrachromats, too.

But contemporary mammals don’t see things that way. That is most likely because early mammals had been little, nocturnal things that spent their first 100 million years running around at night, trying to save yourself from being eaten by tetrachromatic dinosaurs. “During that duration the complicated artistic system they inherited from their ancestors degraded,” states Prum. “We have clumsy, retrofitted form of color eyesight. Fishes, and wild birds, and many lizards visit a much richer globe than we do.”

In fact, many monkeys and apes are dichromats, and discover the world as greyish and somewhat red-hued. Boffins believe very early primates regained three-color vision because spotting fruit and immature leaves led to a far more healthy diet. But regardless of how much you love springtime of fall colors, the wildly varicolored world we humans are now living in now isn’t putting on a show for all of us. It’s mostly for pests and birds. “Flowering flowers of course have evolved to signal pollinators,” states Prum. “The proven fact that we find them gorgeous is incidental, and the undeniable fact that we can see them at all is because of an overlap in spectrums insects and wild birds can easily see and those we can see.”

Covered in color

And also as animals gained the ability to sense color, evolution kickstarted an hands competition in displays—hues and patterns that aided in survival became signifiers of ace baby-making skills. Almost every expression of color in the natural world came into being to signal, or obscure, a creature to something different.

For example, “aposematism” is color used as warning—the butterfly’s bright colors say “don’t consume me, you’ll receive ill.” “Crypsis” is color utilized as camouflage. Colors acts social purposes, too. Like, in mating. Did you know that feminine lions choose brunets? Or that paper wasps can recognize each other people’ faces? “Some wasps have small black colored spots that become karate belts, telling other wasps to not try to fight them,” claims Elizabeth Tibbetts, an entomologist at University of Michigan.

But pets display colors utilizing two completely different techniques. The very first is with pigments, colored substances produced by cells called chromatophores (in reptiles, seafood, and cephalopods), and melanocytes (in animals and wild birds). They absorb most wavelengths of light and mirror just a few, restricting both their range and brilliance. As an example, many animals cannot naturally produce red; they synthesize it from plant chemical substances called carotenoids.

Others means pets make color is by using nanoscale structures. Bugs, and, up to a smaller degree, birds, would be the masters of color-based structure. And compared to pigment, framework is fabulous. Structural coloration scatters light into vibrant, shimmering colors, like shimmering iridescent bib for a Broad-tailed hummingbird, and/or metallic carapace of a Golden scarab beetle. And experts aren’t quite yes why iridescence evolved. Most likely to signal mates, but still: Why?

Decoding the rainbow of life

Issue of iridescence is comparable to most questions boffins have actually about animal coloration. They understand what the colors do in broad strokes, but there’s till lots of nuance to tease away. This is certainly mostly because, until recently, these were restricted to seeing the normal world through peoples eyes. “If you ask issue, what’s this color for, you need to treat it the way in which animals see those colors,” claims Tim Caro, a wildlife biologist at UC Davis together with organizing force behind the new paper. (Speaking of mysteries, Caro recently figured out why zebras have stripes.)

Just take the peacock. “The male’s tail is breathtaking, plus it evolved to wow the feminine. But the feminine might be impressed in a different way than you or I,” Caro says. Humans have a tendency to gaze during the shimmering eyes during the tip of every tail feather; peahens typically consider the root of the feathers, where they put on the peacock’s rump. How does the peahen find the root of the feathers sexy? No one understands. But until scientists strapped towards the wild birds’ minds small cameras spun faraway from the cellular phone industry, they couldn’t also monitor the peahens’ gaze.

Another new technology: Advanced nanomaterials give researchers the ability to replicate the structures animals used to bend light into iridescent displays. By recreating those structures, researchers can figure out how genetically high priced they’re to make.

Likewise, new magnification practices have allowed researchers to check into an animal’s eye structure. You may have find out about how mantis shrimp never have three or four but a whopping 12 different color receptors, and how they see the globe in psychedelic hyperspectral saturation. This really isn’t quite real. Those color channels aren’t linked together—not like they truly are in other pets. The shrimp most likely aren’t seeing 12 various, overlapping color spectra. “We are usually planning perhaps those color receptors are increasingly being switched on or down by some other, non-color, signal,” claims Caro.

But perhaps the most important modern innovation in biological color scientific studies are getting all the various people from different procedures together. “There certainly are a lot of differing types of people working on color,” claims Caro. “Some behavioral biologists, some neurophysiologists, some anthropologists, some structural biologists, and so on.”

And these researchers are scattered around the world. He says the reason why he brought everybody else to Berlin is really so they might finally synthesize each one of these sub-disciplines together, and transfer to a wider understanding of color worldwide. The main technology in understanding animal color eyesight isn’t a camera or perhaps a nanotech surface. It’s an airplane. And/or internet.

Watch SpaceX Launch Its Second Rocket in 48 Hours

If Friday’s rocket livestream wasn’t enough for you, you’re in luck—this Sunday, SpaceX is set to launch its second Falcon 9 of the week. This time, the company is firing a shiny new rocket from California’s Vandenberg Air Force Base. It’s the fastest turnaround yet for two SpaceX launches, but if it’s going to launch as many satellites as it says, there are more rapid-fire liftoffs to come.

These two payloads weren’t originally planned as a double-whammy. A pneumatic valve pushed the BulgariaSat launch back from Monday, June 19. And after initially being delayed from October—then December, then April—today’s liftoff is actually a bit ahead of schedule. This launch delivers 10 more satellites to the fleet that telecommunications company Iridium is building in low Earth orbit. To get the new satellites situated just-so, the launch window is exact, scheduled for 1:25:14 pm Pacific time.

Roughly an hour after it lifts off from Space Launch Complex 4E, the Falcon 9 will dispense one satellite every 90 seconds. These newcomers will be tested for a few weeks before joining the rest of their brethren to beam voice and data information. After dispensing the satellites into orbit, the first stage of the Falcon 9, like a few before it), will land vertically on a drone barge in the Pacific Ocean, to be reused in later launches.

So far, Iridium has only contracted new, unused rockets from SpaceX to place its constellation of satellites. But they may soon get on board with Musk’s rocket reusability plan, if older rockets mean faster launches. Their 2010 agreement with SpaceX originally aimed to send around 70 satellites up by the end of 2017, and that the endpoint has now been delayed to 2018.

Despite the delays, the car-size satellites being launched today have come a long way since they were first trucked in pairs from Phoenix to Vandenberg. Today’s satellite delivery brings Iridium’s total up to 20, with six more SpaceX launches scheduled to deliver the remaining 55 satellites in the next year or so. If all goes well, the end of the day will mark two down, six to go, with precedent set for rapid launches to come.

Arctic Climate Change Study Canceled Due to Climate Change

This story originally appeared on Newsweek and is part of the Climate Desk collaboration.

The Canadian research icebreaker CCGS Amundsen, an Arctic expedition vessel, will not be venturing north for its planned trip this year. The highly anticipated voyage aimed to monitor and understand the effects of climate change on Arctic marine and coastal ecosystems. But due to warming temperatures, Arctic sea ice is unexpectedly in motion, making the trip far too dangerous for the Amundsen and the scientists it would be carrying. In other words, the climate change study has been rendered unsafe by climate change.

The project, known as the Hudson Bay System Study (BaySys), involves 40 scientists from five Canadian universities and was supported by $15 million over four years. A partnership between the scientists, led by the University of Manitoba, and the Canadian Coast Guard has been facilitating such climate change studies for nearly 15 years. The Amundsen is equipped with 65 scientific systems, 22 onboard and portable laboratories and a plethora of instruments that have been allowing researchers to study sediment, ocean ecosystems from just below the ice to just above the seafloor, the ice, the snow and the atmosphere.

The planned 2017 expedition was scheduled to depart six days early due to severe ice conditions in the Strait of Bell Isle, along the northeast coast of Newfoundland. The team was to carry out crucial operations in that area before starting their scientific program.

But the researchers, led by David Barber, expedition chief scientist and BaySys scientific lead, soon realized the trip was impossible. A southward motion of hazardous Arctic sea ice would prevent the Amundsen from reaching its destination in time to conduct the planned studies.

Barber said the severe ice conditions in the area are the result of climate change. Warming temperatures have reduced both the extent and thickness of the ice and increased its mobility. “Ice conditions are likely to become more variable, and severe conditions such as these will occur more often,” Barber said in a statement.

“Considering the severe ice conditions and the increasing demand for search-and-rescue operations and ice escort, we decided to cancel the BaySys mission,” said Barber.

Other portions of the 2017 Amundsen expedition will continue. Specifically, a planned oceanographic study and a Nunavik Inuit Health Survey are on schedule. The team also hopes to resume the BaySys program in 2018.

“The research of our scientists clearly indicate that climate change is not something that is going to happen in the future—it is already here,” a University of Manitoba statement announcing the cancelation stated.

For Modern Astronomers, It’s Learn to Code or Get Left Behind

Astronomer Meredith Rawls was in an astronomy master’s program at San Diego State University in 2008 when her professor threw a curveball. “We’re going to need to do some coding,” he said to her class. “Do you know how to do that?”

Not really, the students said.

And so he taught them—at lunch, working around their regular class schedule. But what he meant by “coding” was Fortran, a language IBM developed in the 1950s. Later, working on her PhD at New Mexico State, Rawls decided her official training wasn’t going to cut it. She set out to learn a more modern language called Python, which she saw other astronomers switching to. “It’s going to suck,” she remembers telling herself, “but I’m just going to do it.”

And so she started teaching herself, and signed up for a workshop called SciCoder. “I basically lost the better part of a year of standard research productivity time largely due to that choice, to switch my tools,” she says, “but I don’t think I could have succeeded without that, either.”

That’s probably true. Rawls’s educational experience is still typical: Fledgling astronomers take maybe one course in coding and then informally learn whatever language their leaders happen to use, because those are the ones the leaders know how to teach. They usually don’t take meaningful courses in modern coding, data science, or their best practices.

But today’s astronomers don’t just need to know how stars form and black holes burst. They also need knowledge of how to pry that information from the many terabytes of data that will stream from next-generation telescopes like the Large Synoptic Survey Telescope and the Square Kilometer Array. So they’re largely teaching themselves—using a suite of open-source training tools, focused workshops, and fellowship programs aims to help and actually prepare astronomers for the universe they’re entering.

Segmentation Fault

Back when telescopes produced less data, astronomers could get by on teaching themselves. “The old model was you go to your telescope—or you log in remotely because you’re fancy—you get your data, you download it on your computer, you make a plot, you write a paper, and you’re a scientist,” says Rawls, who is now a postdoc at the University of Washington. “Now, it’s not practical to download all the data.” And “a plot” is laughable. You just try using graph paper to nail down the correlation function that shows the distribution of millions of galaxies (go ahead; I’ll wait).

There are social costs to that inadequate education. First, it gives a booster to people who knew, early, both that they wanted to be astronomers and that astronomy meant typing into your computer all day. You know, the kinds of kids who sat in Algebra I “hacking” their TI-83s—ones with access to autodidactic materials and the free time to do that didacting. That kind of favoring is a good way to, on average, keep astronomy’s usual suspects—white guys!—on top.

Beyond the social costs, though, lie scientific ones. Let’s say a scientist writes a program that analyzes quakes inside the sun (that happens!). But there’s no documentation on how the program works, and its kludgy, coagulated subroutines are opaque. No second scientist, then, can run that code see if they get the same result, or if the program actually does what Scientist 1 claims. “Reproducibility is held up as the gold standard for what is real or not,” says Lucianne Walkowicz, an astrophysicist at the Adler Planetarium. “You need the materials upon which the experiment was performed, and you need the tools. Code is the equivalent of our beakers and Bunsen burners.”

Plus, the way astrophysics programming has historically worked is inefficient. Out on overheating desktops across Earth’s universities are dozens of programs that do the same thing—catch those quakes, comb for exoplanets—different research groups having made their own. Instead of applying increasingly refined algorithms to their research problems, ill-trained astronomer-coders sometimes spend their time reinventing the wheel.

Data Drama

Walkowicz wants to help fix these problems before they get worse—which they’re about to. She is the science collaboration coordinator for the Large Synoptic Survey Telescope, which will essentially make a 10-year-long HD movie of the sky, so astronomers can see—and, ideally, understand—what changes from diurn to diurn. “Part of the reason we could all get by on being self-taught is that datasets, even when they’re on the fairly big side, are pretty small,” says Walkowicz. “They’re not as large and complex as the data from LSST will be. Problems will be amplified.”

Knowing this, and knowing that astronomer apprentices are getting essentially the same training astronomers have gotten since always, she and LSST colleagues decided to help prepare those apprentices. The LSST Data Science Fellowship program was born, bringing cohorts of students to six weeklong workshops over two years. To select fellows, they use a program called Entrofy, which optimizes diversity among each class.

The idea doesn’t always go over well with professors. “Reactions that I’ve gotten run the gamut from ‘That’s a good point, but our students don’t have time’ to ‘Stop trying to turn our astronomers into computer scientists,’” says Walkowicz.

Reactions that I’ve gotten run the gamut from ‘That’s a good point, but our students don’t have time’ to ‘Stop trying to turn our astronomers into computer scientists.’ Lucianne Walkowicz

But for their part, the students—perhaps more aware of the future of their field than the more senior researchers—feel more like astronomers. “Before being in this program, I already knew my thesis and my thesis hasn’t changed,” says Charee Peters, a grad student at the University of Wisconsin, “but I feel more comfortable now being able to approach it. I feel more like a scientist.”

Grad student Bela Abolfathi of UC Irvine has similar feelings, and thinks it makes sense that education be driven by data. “I had been trying to learn a lot of these techniques on my own, and my progress was glacial,” she says. “It really helps to learn these skills in a formal way, where you can ask questions from experts in the field, just as you would any other subject.”

You can often spot a formally untrained astronomer’s code a light-year away—with its lack of documentation, its serpentine subroutines. But you can also spot a computer scientist’s astronomy code. It’s high and tight, but it doesn’t display the same depth of knowledge about what the program is doing, and what those actions mean for, say, supernovae. “The key thing is combining the two approaches,” says Joachim Moeyens, an LSST data fellow from the University of Washington. “My goal is to keep everyone guessing about whether I’m an astronomer or a software engineer.” (My guess: a new kind of hybrid.)

Put a GitHubcap on that Wheel

The LSST’s fellowship only admits 15 students at a time—hardly the whole field. But the curriculum is online, and it has company. The Banneker & Aztlán Institute preps undergrads from all over in Unix, Python, computational astronomy, and data visualization. There are general boot camps, astro-specific modules, and continent-centric workshops. NASA and the SETI Institute recently teamed up to start the Frontier Development Lab, which brings planetary researchers and data scientists into contact with the private sector. And the University of Washington has a whole organization—the E-Science Institute—dedicated to the cause.

Astronomers have also given each other actual tools. The open-source AstroPy is “a community effort to develop a single core package for Astronomy in Python and foster interoperability between Python astronomy packages.” AstroML has a similar goal for the machine learning and data mining side. Scientists, here, can use the same code to do the same things on different data, fixing both that whole redundant wheel thing and the reproducibility problem.

Still, there’s some resistance in The Academy, reluctance to integrate all of this into curricula instead of requiring students to (or just tolerating students who) boot themselves off to camp. Alexandra Amon, an LSST Data Science fellow and a grad student at the University of Edinburgh, feels this acutely, in thinking about how, in the view of some, her hours spent learning to deal with data detract from her science—essentially the same sentiment Rawls expressed, despite the difference in their years. “Traditionally, from a job application point of view, time spent doing data analysis is detracting from delivering science results and paper-producing,” Almon says, “and therefore a hindrance.”

But “doing science” means—and has meant, for a while now—doing the kind of analysis that demands data and computer science expertise. Without that, the gap between knowledge and scientists’ ability to get that knowledge will only grow, like, you know, the universe itself.

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