There are approximately 86 billion neurons in the human brain. Over the past decades, we have made enormous progress in understanding their molecular, genetic, and structural makeup as well as their function. However, the real power of the central nervous system lies in the smooth coordination of large numbers of neurons. Neurons are thus organized on many different scales, from small microcircuits and assemblies all the way to regional brain networks. To interact effectively on all these levels, neurons, nuclei, cortical columns, and larger areas need to be connected. The study of neuronal connectivity has expanded rapidly in past years. Large research groups have recently joined forces and formed consortia to tackle the difficult problems of how to experimentally investigate connections in the brain and how to analyze and make sense of the enormous amount of data that arises in the process.
This year's neuroscience special issue is devoted to general and also several more specific aspects of research on connectivity in the brain. We invited researchers to review the most recent progress in their fields and to provide us with an outlook on what the future may hold in store.
To make sense of larger structures, we first have to understand the composition of their basic building blocks. Markov et al. (p. 578) describe how interareal connectivity at the single-cell level, revealed by quantitative anatomical tract tracing, is relevant to our understanding of large-scale cortical networks and their hierarchical organization.
A different but also rapidly growing research direction deals with the use of connectivity measures to link brain structure and cognition. From the perspective of network theory, Park and Friston (p. 579) review our current understanding of structure-function relationships in large-scale brain networks and their underlying mechanisms.
One of the biggest breakthroughs in understanding the heavily connected brain has been the development of noninvasive brain-scanning methods, especially functional magnetic resonance imaging (fMRI). Turk-Browne (p. 580) provides an overview of recent exciting developments in large-scale fMRI data analysis, with a focus on unbiased approaches for examining whole-brain functional connectivity during cognitive tasks. Increased computational power now allows investigation of the whole-brain correlation matrix, the temporal correlation of every voxel with every other voxel throughout the brain, and the application of multivariate pattern analysis to these correlational data.
A sophisticated system that depends on the astonishingly precise interaction of a large number of cortical areas is the human ability to produce and understand language and music. Zatorre (p. 585) discusses how brain plasticity in the music and speech domains can be affected by predisposing factors that relate to brain structure and function.
Much of what medical researchers conclude in their studies is misleading, exaggerated, or flat-out wrong. So why are doctors—to a striking extent—still drawing upon misinformation in their everyday practice? Dr. John Ioannidis has spent his career challenging his peers by exposing their bad science.
In 2001, rumors were circulating in Greek hospitals that surgery residents, eager to rack up scalpel time, were falsely diagnosing hapless Albanian immigrants with appendicitis. At the University of Ioannina medical school’s teaching hospital, a newly minted doctor named Athina Tatsioni was discussing the rumors with colleagues when a professor who had overheard asked her if she’d like to try to prove whether they were true—he seemed to be almost daring her. She accepted the challenge and, with the professor’s and other colleagues’ help, eventually produced a formal study showing that, for whatever reason, the appendices removed from patients with Albanian names in six Greek hospitals were more than three times as likely to be perfectly healthy as those removed from patients with Greek names. “It was hard to find a journal willing to publish it, but we did,” recalls Tatsioni. “I also discovered that I really liked research.” Good thing, because the study had actually been a sort of audition. The professor, it turned out, had been putting together a team of exceptionally brash and curious young clinicians and Ph.D.s to join him in tackling an unusual and controversial agenda.
Last spring, I sat in on one of the team’s weekly meetings on the medical school’s campus, which is plunked crazily across a series of sharp hills. The building in which we met, like most at the school, had the look of a barracks and was festooned with political graffiti. But the group convened in a spacious conference room that would have been at home at a Silicon Valley start-up. Sprawled around a large table were Tatsioni and eight other youngish Greek researchers and physicians who, in contrast to the pasty younger staff frequently seen in U.S. hospitals, looked like the casually glamorous cast of a television medical drama. The professor, a dapper and soft-spoken man named John Ioannidis, loosely presided.
One of the researchers, a biostatistician named Georgia Salanti, fired up a laptop and projector and started to take the group through a study she and a few colleagues were completing that asked this question: were drug companies manipulating published research to make their drugs look good? Salanti ticked off data that seemed to indicate they were, but the other team members almost immediately started interrupting. One noted that Salanti’s study didn’t address the fact that drug-company research wasn’t measuring critically important “hard” outcomes for patients, such as survival versus death, and instead tended to measure “softer” outcomes, such as self-reported symptoms (“my chest doesn’t hurt as much today”). Another pointed out that Salanti’s study ignored the fact that when drug-company data seemed to show patients’ health improving, the data often failed to show that the drug was responsible, or that the improvement was more than marginal.
Salanti remained poised, as if the grilling were par for the course, and gamely acknowledged that the suggestions were all good—but a single study can’t prove everything, she said. Just as I was getting the sense that the data in drug studies were endlessly malleable, Ioannidis, who had mostly been listening, delivered what felt like a coup de grâce: wasn’t it possible, he asked, that drug companies were carefully selecting the topics of their studies—for example, comparing their new drugs against those already known to be inferior to others on the market—so that they were ahead of the game even before the data juggling began? “Maybe sometimes it’s the questions that are biased, not the answers,” he said, flashing a friendly smile. Everyone nodded. Though the results of drug studies often make newspaper headlines, you have to wonder whether they prove anything at all. Indeed, given the breadth of the potential problems raised at the meeting, can any medical-research studies be trusted?
That question has been central to Ioannidis’s career. He’s what’s known as a meta-researcher, and he’s become one of the world’s foremost experts on the credibility of medical research. He and his team have shown, again and again, and in many different ways, that much of what biomedical researchers conclude in published studies—conclusions that doctors keep in mind when they prescribe antibiotics or blood-pressure medication, or when they advise us to consume more fiber or less meat, or when they recommend surgery for heart disease or back pain—is misleading, exaggerated, and often flat-out wrong. He charges that as much as 90 percent of the published medical information that doctors rely on is flawed. His work has been widely accepted by the medical community; it has been published in the field’s top journals, where it is heavily cited; and he is a big draw at conferences. Given this exposure, and the fact that his work broadly targets everyone else’s work in medicine, as well as everything that physicians do and all the health advice we get, Ioannidis may be one of the most influential scientists alive. Yet for all his influence, he worries that the field of medical research is so pervasively flawed, and so riddled with conflicts of interest, that it might be chronically resistant to change—or even to publicly admitting that there’s a problem.
The city of Ioannina is a big college town a short drive from the ruins of a 20,000-seat amphitheater and a Zeusian sanctuary built at the site of the Dodona oracle. The oracle was said to have issued pronouncements to priests through the rustling of a sacred oak tree. Today, a different oak tree at the site provides visitors with a chance to try their own hands at extracting a prophecy. “I take all the researchers who visit me here, and almost every single one of them asks the tree the same question,” Ioannidis tells me, as we contemplate the tree the day after the team’s meeting. “‘Will my research grant be approved?’” He chuckles, but Ioannidis (pronounced yo-NEE-dees) tends to laugh not so much in mirth as to soften the sting of his attack. And sure enough, he goes on to suggest that an obsession with winning funding has gone a long way toward weakening the reliability of medical research.
He first stumbled on the sorts of problems plaguing the field, he explains, as a young physician-researcher in the early 1990s at Harvard. At the time, he was interested in diagnosing rare diseases, for which a lack of case data can leave doctors with little to go on other than intuition and rules of thumb. But he noticed that doctors seemed to proceed in much the same manner even when it came to cancer, heart disease, and other common ailments. Where were the hard data that would back up their treatment decisions? There was plenty of published research, but much of it was remarkably unscientific, based largely on observations of a small number of cases. A new “evidence-based medicine” movement was just starting to gather force, and Ioannidis decided to throw himself into it, working first with prominent researchers at Tufts University and then taking positions at Johns Hopkins University and the National Institutes of Health. He was unusually well armed: he had been a math prodigy of near-celebrity status in high school in Greece, and had followed his parents, who were both physician-researchers, into medicine. Now he’d have a chance to combine math and medicine by applying rigorous statistical analysis to what seemed a surprisingly sloppy field. “I assumed that everything we physicians did was basically right, but now I was going to help verify it,” he says. “All we’d have to do was systematically review the evidence, trust what it told us, and then everything would be perfect.”
It didn’t turn out that way. In poring over medical journals, he was struck by how many findings of all types were refuted by later findings. Of course, medical-science “never minds” are hardly secret.
And they sometimes make headlines, as when in recent years large studies or growing consensuses of researchers concluded that mammograms, colonoscopies, and PSA tests are far less useful cancer-detection tools than we had been told; or when widely prescribed antidepressants such as Prozac, Zoloft, and Paxil were revealed to be no more effective than a placebo for most cases of depression; or when we learned that staying out of the sun entirely can actually increase cancer risks; or when we were told that the advice to drink lots of water during intense exercise was potentially fatal; or when, last April, we were informed that taking fish oil, exercising, and doing puzzles doesn’t really help fend off Alzheimer’s disease, as long claimed. Peer-reviewed studies have come to opposite conclusions on whether using cell phones can cause brain cancer, whether sleeping more than eight hours a night is healthful or dangerous, whether taking aspirin every day is more likely to save your life or cut it short, and whether routine angioplasty works better than pills to unclog heart arteries.
But beyond the headlines, Ioannidis was shocked at the range and reach of the reversals he was seeing in everyday medical research. “Randomized controlled trials,” which compare how one group responds to a treatment against how an identical group fares without the treatment, had long been considered nearly unshakable evidence, but they, too, ended up being wrong some of the time. “I realized even our gold-standard research had a lot of problems,” he says. Baffled, he started looking for the specific ways in which studies were going wrong. And before long he discovered that the range of errors being committed was astonishing: from what questions researchers posed, to how they set up the studies, to which patients they recruited for the studies, to which measurements they took, to how they analyzed the data, to how they presented their results, to how particular studies came to be published in medical journals.
This array suggested a bigger, underlying dysfunction, and Ioannidis thought he knew what it was.
“The studies were biased,” he says. “Sometimes they were overtly biased. Sometimes it was difficult to see the bias, but it was there.” Researchers headed into their studies wanting certain results—and, lo and behold, they were getting them. We think of the scientific process as being objective, rigorous, and even ruthless in separating out what is true from what we merely wish to be true, but in fact it’s easy to manipulate results, even unintentionally or unconsciously. “At every step in the process, there is room to distort results, a way to make a stronger claim or to select what is going to be concluded,” says Ioannidis. “There is an intellectual conflict of interest that pressures researchers to find whatever it is that is most likely to get them funded.”
Perhaps only a minority of researchers were succumbing to this bias, but their distorted findings were having an outsize effect on published research. To get funding and tenured positions, and often merely to stay afloat, researchers have to get their work published in well-regarded journals, where rejection rates can climb above 90 percent. Not surprisingly, the studies that tend to make the grade are those with eye-catching findings. But while coming up with eye-catching theories is relatively easy, getting reality to bear them out is another matter. The great majority collapse under the weight of contradictory data when studied rigorously. Imagine, though, that five different research teams test an interesting theory that’s making the rounds, and four of the groups correctly prove the idea false, while the one less cautious group incorrectly “proves” it true through some combination of error, fluke, and clever selection of data. Guess whose findings your doctor ends up reading about in the journal, and you end up hearing about on the evening news? Researchers can sometimes win attention by refuting a prominent finding, which can help to at least raise doubts about results, but in general it is far more rewarding to add a new insight or exciting-sounding twist to existing research than to retest its basic premises—after all, simply re-proving someone else’s results is unlikely to get you published, and attempting to undermine the work of respected colleagues can have ugly professional repercussions.
In the late 1990s, Ioannidis set up a base at the University of Ioannina. He pulled together his team, which remains largely intact today, and started chipping away at the problem in a series of papers that pointed out specific ways certain studies were getting misleading results. Other meta-researchers were also starting to spotlight disturbingly high rates of error in the medical literature. But Ioannidis wanted to get the big picture across, and to do so with solid data, clear reasoning, and good statistical analysis. The project dragged on, until finally he retreated to the tiny island of Sikinos in the Aegean Sea, where he drew inspiration from the relatively primitive surroundings and the intellectual traditions they recalled. “A pervasive theme of ancient Greek literature is that you need to pursue the truth, no matter what the truth might be,” he says. In 2005, he unleashed two papers that challenged the foundations of medical research.
He chose to publish one paper, fittingly, in the online journal PLoS Medicine, which is committed to running any methodologically sound article without regard to how “interesting” the results may be. In the paper, Ioannidis laid out a detailed mathematical proof that, assuming modest levels of researcher bias, typically imperfect research techniques, and the well-known tendency to focus on exciting rather than highly plausible theories, researchers will come up with wrong findings most of the time.
Simply put, if you’re attracted to ideas that have a good chance of being wrong, and if you’re motivated to prove them right, and if you have a little wiggle room in how you assemble the evidence, you’ll probably succeed in proving wrong theories right. His model predicted, in different fields of medical research, rates of wrongness roughly corresponding to the observed rates at which findings were later convincingly refuted: 80 percent of non-randomized studies (by far the most common type) turn out to be wrong, as do 25 percent of supposedly gold-standard randomized trials, and as much as 10 percent of the platinum-standard large randomized trials. The article spelled out his belief that researchers were frequently manipulating data analyses, chasing career-advancing findings rather than good science, and even using the peer-review process—in which journals ask researchers to help decide which studies to publish—to suppress opposing views. “You can question some of the details of John’s calculations, but it’s hard to argue that the essential ideas aren’t absolutely correct,” says Doug Altman, an Oxford University researcher who directs the Centre for Statistics in Medicine.
Still, Ioannidis anticipated that the community might shrug off his findings: sure, a lot of dubious research makes it into journals, but we researchers and physicians know to ignore it and focus on the good stuff, so what’s the big deal? The other paper headed off that claim. He zoomed in on 49 of the most highly regarded research findings in medicine over the previous 13 years, as judged by the science community’s two standard measures: the papers had appeared in the journals most widely cited in research articles, and the 49 articles themselves were the most widely cited articles in these journals. These were articles that helped lead to the widespread popularity of treatments such as the use of hormone-replacement therapy for menopausal women, vitamin E to reduce the risk of heart disease, coronary stents to ward off heart attacks, and daily low-dose aspirin to control blood pressure and prevent heart attacks and strokes. Ioannidis was putting his contentions to the test not against run-of-the-mill research, or even merely well-accepted research, but against the absolute tip of the research pyramid. Of the 49 articles, 45 claimed to have uncovered effective interventions. Thirty-four of these claims had been retested, and 14 of these, or 41 percent, had been convincingly shown to be wrong or significantly exaggerated. If between a third and a half of the most acclaimed research in medicine was proving untrustworthy, the scope and impact of the problem were undeniable. That article was published in the Journal of the American Medical Association.
Driving me back to campus in his smallish SUV—after insisting, as he apparently does with all his visitors, on showing me a nearby lake and the six monasteries situated on an islet within it—Ioannidis apologized profusely for running a yellow light, explaining with a laugh that he didn’t trust the truck behind him to stop. Considering his willingness, even eagerness, to slap the face of the medical-research community, Ioannidis comes off as thoughtful, upbeat, and deeply civil. He’s a careful listener, and his frequent grin and semi-apologetic chuckle can make the sharp prodding of his arguments seem almost good-natured. He is as quick, if not quicker, to question his own motives and competence as anyone else’s. A neat and compact 45-year-old with a trim mustache, he presents as a sort of dashing nerd—Giancarlo Giannini with a bit of Mr. Bean.
The humility and graciousness seem to serve him well in getting across a message that is not easy to digest or, for that matter, believe: that even highly regarded researchers at prestigious institutions sometimes churn out attention-grabbing findings rather than findings likely to be right. But Ioannidis points out that obviously questionable findings cram the pages of top medical journals, not to mention the morning headlines. Consider, he says, the endless stream of results from nutritional studies in which researchers follow thousands of people for some number of years, tracking what they eat and what supplements they take, and how their health changes over the course of the study.
“Then the researchers start asking, ‘What did vitamin E do? What did vitamin C or D or A do? What changed with calorie intake, or protein or fat intake? What happened to cholesterol levels? Who got what type of cancer?’” he says. “They run everything through the mill, one at a time, and they start finding associations, and eventually conclude that vitamin X lowers the risk of cancer Y, or this food helps with the risk of that disease.” In a single week this fall, Google’s news page offered these headlines: “More Omega-3 Fats Didn’t Aid Heart Patients”; “Fruits, Vegetables Cut Cancer Risk for Smokers”; “Soy May Ease Sleep Problems in Older Women”; and dozens of similar stories.
The entropy level at the beginning of the expansion of the Universe must have been staggeringly small, which implies that the initial conditions were very special indeed. — John Barrow (2007)
Entropy seems to be one of those things that professions use to keep outsiders outside. One can almost hear the physicists conspiring: Order is too easy so we need some mystery. Thus when something has more order it has less entropy.
Physicists agree the early universe’s entropy was very low. Low entropy means almost perfect order. This may be the way it was; it’s not the way it goes. If one watches anything that’s isolated so disorder can’t slip out the back door and go someplace else―one sees it get disordered over time. This is sometimes called the Second Law. Some situations seem to show that it is wrong. What could be more striking than the order of a growing human brain? Well, this is where that back-door rule comes in. A human brain won’t grow in isolation. To check the Second Law, one must watch the infant that comes with it, and its inputs and its outputs: the baby food and diapers; the factories that make them; the garbage dumps; the once-clean water going, dirty, down the drain; CO2 emissions and the whole shebang. Life forms make their order using energy to move disorder someplace else. Total entropy goes up.
The order of the universe is a big problem. Since the universe is everything there’s nothing that can interfere. It’s a truly isolated system, no back door. The Second Law says that its order must run down. So it must be more ordered as we look far back in time. The order of the universe at its beginning must have been extremely high. Ever since―every microsecond, every million years―its order has gone down. Translated into English this means the Beginning was orderly to an almost inconceivable degree. Barrow called it very special. So in entropic terms the Problem of Perfection is: In the first instant of the universe its entropy was very low. How did it get to be that way? - See more at: http://www.timeone.ca/clues/the-problem-of-perfection/#sthash.JGLShS26.c8uMtWDH.dpuf
Let’s get real. It doesn’t matter if you think Monsanto is evil. Genetically modified food is safe—no matter what logical fallacies will lead liberals like Bill Maher to believe.
AP Photo/HBO, Janet Van Ham
If Monsanto has anything to do with it, it must be evil. That seems to be the prevailing opinion on the monolithic biotech company. Following that logic, if they produce corn or soybeans or another crop that has been genetically modified (GM), those too must be evil. That’s Bill Maher’s reasoning at least—reasoning that lures liberals away from science and towards denial.
Making the leap from Monsanto’s business practices—whatever you may think of them—to the “dangers” of GM foods is a mistake in logical reasoning. It is akin to saying landscape paintings are potentially evil because the painter was a serial killer. The conclusion does not follow from the premise. And giving some product or process the attributes of its user is the logical fallacy that currently leads typically pro-science liberals like Maher astray on questions of nuclear power, vaccination, and especially GMOs. Whether genetically modified foods are safe is a scientific, not a political, question. To intertwine views of Monsanto with GM foods is therefore an argumentum ad monsantum, a disturbingly popular logical fallacy, and Bill Maher is the classic example.
I am a fan of Real Time with Bill Maher. It’s HBO’s version of The Daily Show, with a liberal host poking fun at the foibles of government and politicians. But every so often, satire can veer off course, lampooning scientific findings as if they were the latest sex scandal. This is the case with Bill Maher. Though you will hear him on Real Time staunchly defending the science of climate change and evolution against politically-charged deniers, you will also hear him railing against vaccines, nuclear power, and GMOs with the same polemic language he satirizes.
For example, in episode #294 of Real Time, Maher invites the director of “GMO OMG” for a conversation about the “dangers” of GM foods. (Note that fellow Scientific American writer Ferris Jabr has convincingly argued why “GMO OMG” is an emotionally manipulative film that skimps on the science.) Maher begins the conversation with a question: “I don’t want to start things off by asking why Monsanto is evil…but why is Monsanto evil?” The director goes on to explain why, with the rest of the panel chiming in. Then you see something very telling. CNN contributor David Frum, a Republican, interrupts to explain how humans have been genetically modifying food ever since we prioritized seeds from desirably growing crops at the dawn of agriculture. He was booed and hissed by the crowd. I mentioned Frum’s political affiliation because Real Time has an admitted slant towards liberalism, and Republicans encounter much resistance on each episode. This time was no different. Though Frum was exactly right on the science, he was treated as exactly wrong. The argumentum ad monsantum struck again.
Maher, who I think gets a lot of science right, gets the science of GM food so wrong because he is unable or unwilling to disentangle the politics from the science. Many liberals seem to have the same problem.
The first component to the liberal opposition to genetically modified food appears to be a genuine misunderstanding of how it works. The genetic modification of food is a much more exact science than many opponents realize. As this fantastic explainer outlines, genetic modification is typically about inserting a single gene—whose effects we test for toxicity and allergenic properties—into a crop. It is not a haphazard Frankenstein process of sowing and suturing animal and plant parts together. In fact, a Frankenstein-style process is exactly what was done before genetic modification.
In the early days of agriculture, farmers crossbred plants to take advantage of the genetic diversity thrown up by evolutionary processes. Whatever beneficial properties emerged were saved in the seeds and transplanted into the next generation. This is a Mary Shelly-style process, with more recent farmers exposing their plants to radiation in the hopes of increasing the genetic variations at their disposal. That’s a fact that is absent from many a Monsanto discussion. If anything exemplifies the messy, unknown nature of altering crops, it’s what farming looked like before genetic modification.
Even when we are taking genes from animals and inserting them into plants or vice-versa, the results are still safe, reduce pesticide use, and dramatically increase crop yields. In fact, this year, a review of over 1,700 papers [PDF] concerning the safety of GM food in the journal Critical Reviews in Biotechnology concluded, “The scientific research conducted so far has not detected any significant hazards directly connected with the use of genetically engineered crops.”
Increasing the hardiness of our crops to better feed the world is also the main benefit of genetic modification, often omitted from the curious liberal opposition to GM food. As climate change picks up its pace, we will need crops that can feed more people while at the same time resisting parasites, infections, and drought. Scientifically established safety is bolstered by moral obligation.
While Bill Maher has a habit of outright denying the safety of GM food, he does sometimes taper his views by offering the alternative—growing food “organically” (GM food is still organic material, of course, but it may not fit the FDA’s designations of what “organic” food is). However, the supposed superiority of organically grown food has little scientific justification. Organically grown food still uses pesticides, those pesticides are largely untested, the pesticide reduction that organic food does offer is insignificant at best, and the food itself is no more nutritious or safe than its engineered alternative.
Still, even though the scientific community is in agreementover thesafety of GM foods, there is a question of disclosure—the second component of the argumentum ad absurdum. To Maher, the “evil” nature of Monsanto is bound-up in the fact that GM foods are not currently labeled as such.
We deserve to know what we are eating, and if Monsanto won’t tell us, GM food must be bad for us, or so the argument seems to go. But again, the science must be separated from the politics. No one will deny that Monsanto had a dog in the fight to prevent GM labeling in California, but Maher might be surprised to hear that labeling genetically modified foods is a bad idea, despite the benefits of transparency. There is no scientific reason to label from a safety standpoint, and doing so would likely only create more fear around the already beleaguered technology. And that fear would probably have damaging implications for all advances in food technology. Just look at what happens when people realize that fluoride—a safe and amazingly effective addition to our public water supply—is coming from their tap.
For questions that science, and not politics, has a bearing on, it really does not matter what you think of Monsanto. It does not matter what you think of the corporation’s business tactics or how it treats its customers or employees. Similarly, it does not matter if you think Al Gore a hypocrite or Charles Darwin a heathen–climate change and evolution are real and established. By calling GMOs “poison” and “evil”, Bill Maher poisons the well of reasoned scientific discussion with ideologically driven fear mongering.
It’s fashionable to think that the conservative parties in America are the science deniers. You certainly wouldn’t have trouble supporting that claim. But liberals are not exempt. Though the denial of evolution, climate change, and stem cell research tends to find a home on the right of the aisle, the denial of vaccine, nuclear power, and genetic modification safety have found a home on the left (though the extent to which each side denies the science is debatable). It makes one wonder: Why do liberals like Maher—psychologically considered open to new ideas—deny the science of GM food while accepting the science in other fields?
The answer to that giant question is an unsettled one, but themes do leap out of the literature. Simplifying greatly, cognitive bias and ideology play a large role. We tend to accept information that confirms our prior beliefs and ignore or discredit information that does not. This confirmation bias settles over our eyes like distorting spectacles for everything we look at. Could this be at the root of the argumentum ad monsantum? It isn’t inconsistent with the trend Maher has shown repeatedly on his show. A liberal opposition to corporate power, to capitalistic considerations of human welfare, could be incorrectly coloring the GM discussion. Perhaps GMOs are the latest casualty in a cognitive battle between confirmation bias and reality.
But just how much psychology plays into the opposition of GMOs is a question that can’t even be asked until the politics and the science are untangled.
To his credit, Bill Maher has a record of seeing the science forest for the political trees when it comes to topics like climate change and evolution. He spots the political manipulation of climate change when the Koch brothers fund disinformation. He picks out when arguments to “teach the controversy” are just semantic manipulations to get religious ideology into science classes. I hope that he, and the liberal bastion of science denial he sometimes represents, one day gets real and recognizes how much his political views are manipulating his stance on genetically modified foods.
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Tip of the hat to Brian Dunning who came up with the phrase “argumentum ad monsantum” on Twitter.
About the Author: Kyle Hill is a freelance science writer and communicator who specializes in finding the secret science in your favorite fandom. Follow on Twitter @Sci_Phile.
An artist's view of the extrasolar planet GJ 1214b, which scientists say has an atmosphere thick with clouds.
One of the best-studied nearby extrasolar planets — a body just a little larger than Earth — is swaddled in alien clouds, researchers have found. It is the smallest and most Earth-like world yet to yield the secrets of its atmosphere. Another study has found tantalizing hints of clouds on a larger, Neptune-sized world. The pair of discoveries, reported today in Nature1, 2, suggests that clouds may envelop many exoplanets.
“We always knew the clouds must be there for some planets, but now we have a wave of results telling us that clouds are actually very common,” says Heather Knutson, a planetary astronomer at the
California Institute of Technology in Pasadena and lead author of the second paper.
The two planets are among the closest to our Solar System to have been found so far. The super-Earth discovery involves GJ 1214b, just 13 parsecs (about 42 light years) from us, which whirls around a red dwarf star once every 38 hours. As the planet passes across the star’s face, some of the starlight should filter through the planet's atmosphere, revealing its composition.
In 2010, scientists reported finding no imprint of GJ 1214b's atmosphere in the spectrum of light from its host star (see ‘A gaze at exoplanet haze’). Researchers speculated that clouds could be blocking the background starlight from getting through.
Another option was that the planet's atmosphere was made mostly of heavy molecules, such as water, rather than lightweight hydrogen. If this was the case, then the planet's gravity would compress its atmosphere into a thin, dense layer. This thin layer shrinks the size of features in the spectrum to below what the measurements could detect.
At the time, researchers could not distinguish between these two options. Now, observations from the Hubble Space Telescope show that clouds must be present.
A team led by Laura Kreidberg, of the University of Chicago in Illinois, studied near-infrared light filtering around GJ 1214b during 15 of its passages in front of its star. This gave the scientists enough data to have seen the spectral imprint of a water-rich atmosphere had it been there. They saw nothing, and concluded that the planet must be swathed in clouds.
Kreidberg says that the atmosphere could still be made mostly of water vapour. “But we found there also have to be clouds,” she says.
Those clouds would be unlike any on Earth, given the temperatures and pressures in the exoplanet's atmosphere. They could be made of something like zinc sulphide or potassium chloride, as both of these compounds would condense into microscopic droplets, and thus form clouds, under such conditions. “It’s the first time that we’ve been able to characterize the atmosphere of an exoplanet smaller than Neptune,” says Kreidberg.
Similar clouds may also enshroud the larger, Neptune-sized world known as GJ 436b, located about 10 parsecs (33 light years) away. Caltech’s Knutson and her colleagues targeted this planet because it is an archetype of many similarly sized exoplanets. Like Kreidberg’s team, these scientists also used the Hubble telescope to watch changes in a star’s light as the planet passed in front of it.
Once again, the star’s spectrum showed no imprint of the planet's atmosphere. And once again, the options are either a cloudy atmosphere or one that is weighed down by heavy molecules such as water or methane.
Neptune is made mostly of hydrogen and helium, so a Neptune-sized object with a heavy atmosphere would defy expectations, says Knutson. “We thought this was just a Neptune analogue, but if it doesn’t have a lot of hydrogen we have to think harder about how you would build a planet like that,” she says.
Both new studies show how clouds are cropping up on more and more exoplanets, says Julianne Moses, a planetary scientist at the Space Science Institute in Boulder, Colorado. In October, a team led by scientists at the Massachusetts Institute of Technology in Cambridge reported mapping clouds on two different hemispheres of a planet called Kepler-7b3.
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