DNA is useful for many things, starting with that whole molecule-of-heredity thing and moving on to identifying murderers and rapists while exonerating the innocent. These are merely the tip of the iceberg, it turns out, when it comes to DNA’s talents: the double helix also makes an excellent origami material.

As scientists led by chemist Jørgen Kjems of Denmark’s Aarhus University report today in Nature, strands of DNA can be stitched together to form three-dimensional boxes measuring 42 by 36 by 36 nanometers. The key to making this work is that DNA is formed of molecules that recognize and bind with one another. Starting from this premise, Kjems and his colleagues wrote a computer program that determined which particular sequences of those molecules were needed to make a six-sided, hollow box—including an openable lid. The program spit out the answer: 220 snippets of DNA that will attach to the long piece of DNA from a virus called a bacteriophage.

All that was left for the scientists to do was buy the specified snippets and mix them with the long viral DNA. The snippets bent and turned each strand until it made a wall, and then attached the six walls into a box. In just a couple of hours the snippets had induced the self-assembly of billions of boxes. “It's amazing that it works,” Kjems told Nature. “It’s like taking your car apart, putting the nuts and bolts into a bag, shaking it, and the car builds itself.”

For their next clever move, the scientists attached a short stretch of DNA to one side of the box which was the chemical mate of another short stretch. When the second bit of DNA was added to the solution containing the boxes, it acted like a zipper tongue, unstitching the link between one wall and its neighbor so that the wall opened like a boxtop.

The zippable lid means that the DNA origami box might be used to transport drug molecules to a target in the body, releasing its contents only when the DNA un-zipper is added. Nature has a nice write-up in lay language here.

It's bird week at Lab Notes, what with yesterday's blue tits and now this:

Just because one cockatoo named Snowball can bob and sway to the Backstreet Boys, as millions of YouTube fans know, doesn’t mean birds can sense musical beat and rhythm. As scientists are fond of saying, the plural of anecdote is not data. In this case, it was possible that Snowball had learned a specific routine that happened to go with a song, and not that he could dance in time to music.

But when neuroscientist Aniruddh Patel of The Neurosciences Institute in La Jolla tested the bird, as he and colleagues report online in Current Biology, electronically altering the beat and tempo of Snowball’s favorite song, they found that the bird adjusted his bobbing and swaying to the beat, “spontaneously adjust[ing] the tempo of its rhythmic movements to stay synchronized with the beat.” It’s the first evidence that an animal other than humans can move in time to a musical beat. “If the music speeds up or slows down across a wide range, he adjusts the tempo of his dancing,” said Patel.

None of humankind’s close primate relatives do that, and despite some pet owners’ beliefs, there is no good evidence that dogs and cats do it reliably either. The finding that parrots can (as another study in the same journal also reports) suggests that the ability is related to vocal learning or mimicry, perhaps using the same neural circuitry: after all, complex vocal learning and dancing to a beat both require a strong association between auditory and motor circuits in the brain.

To see for yourself, go here.

Everyone knows that females are programmed to be monogamous and males to be promiscuous, since a female is limited in how many offspring she can have in any period of time but males, by spreading their seed far and wide, have practically limitless opportunities for paternity. There is therefore little adaptive advantage to a female’s promiscuity, but lots of evolutionary pressure for males to mate with anything that will stand still, goes the dogma. Except that someone forgot to tell the blue tits.

These female birds often mate with males other than their regular partners, scientists have long known, but the reason has been a bit of a mystery. A new study suggests that the old favorite explanation—that these “extra-pair” copulations (as in, outside the pair bond) produce genetically-superior offspring—might not be the right explanation. Instead, scientists report online in Current Biology, when female blue tits stray, producing broods of chicks that have different fathers, the offspring of fathers other than mom’s regular mate may get a head start in life, suggests Michael Magrath of University of Groningen, The Netherlands.

Blue-tit eggs fertilized by males other than the regular mate tend to be laid before eggs fathered by the mate, they report, and to hatch earlier, too: almost 75 percent of the extra-pair eggs were laid in the first half of the clutch. As a general rule, “earlier hatching chicks perform better than later hatching siblings,” notes Magrath, because by hatching first they get an edge in competition for food. In fact, this early emergence accounts for almost all the differences between the offspring of mom’s regular mate and those of her extracurricular friend, he says, “indicating that non-genetic laying order effects largely accounted for the observed superiority of extra-pair offspring.”

What remains unknown is why Lothario’s eggs hatch first. But if extra-pair matings produce chicks that are first out of the gate, it may be a reflection of the ultimate reason for female promiscuity: as insurance against the possibility that her regular mate is infertile. By seeking other fathers for her chicks, the female blue tit can make sure that all her eggs are fertilized even if her mate is shooting blanks. “Because birds can store sperm for an extended period in specialized storage tubules, females may have little need to continue engaging in extra-pair copulation after laying starts, and this would rather neatly explain the decline in extra-pair offspring that we observed with laying order,” Magrath said.

When I wrote a year ago about surprising new evidence that Botox and other drugs containing the neurotoxin botulinum could travel from the site of injection to the brain, it wasn't clear whether this posed a threat to patients (the study I described was done on lab animals). But yesterday the Food and Drug Administration announced that the risk was all too real: it will now require Botox, Myobloc and the other botulinum drugs to carry a black box warning—the strongest there is—alerting doctors and patients that the drug can spread to distant parts of the body, posing serious risks such as trouble breathing and swallowing. The FDA is also requiring manufacturers to send doctors a letter warning of the risk.

The action comes in response to a petition from Public Citizen, which in 2008 asked FDA to require both a black box and the warning letters. If I’d had to guess, I would have predicted that FDA would have limited the warnings to non-cosmetic uses of the drugs, since that’s where most of the problems had occurred (botulinum is used to treat cervical dystonia, eyelid spasms and a few other medical conditions in addition to frown lines), especially in unapproved uses such a limb spasticity in kids with cerebral palsy.

But the agency said it found 225 cases in which the drug had migrated beyond the injection site. That was alarming enough to justify the black-box decision, even on cosmetic Botox. As FDA put it, "there is insufficient information to fully characterize the safety profile and potential risk factors for spread of botulinum toxin at this time, given that the mechanism by which spread may occur has not been confirmed. . . . Based on available information, it is not possible to precisely predict the role of injection site, injection technique, or dose in the spread of toxin or severity of the event. Therefore, we are seeking safety labeling changes for the both the dermatologic and neurologic indications."

In a post earlier this week on a study raising doubts about some high-profile studies in neuroscience, I was remiss in implying that the problem existed only in fMRI studies. As the paper’s lead author, Niko Kriegeskorte, reminds me, “this is not only about brain imaging (as your title suggests), but equally affects other fields of systems neuroscience,” including EEGs.

I also reported criticism that Kriegeskorte and his colleagues had not listed the studies they found to be problematic, which leaves scientists scratching their heads about what’s reliable and what's (maybe) not. The reason they did not make the list public, Kriegeskorte explains, is that their aim was “to educate, so that an alarming trend can be nipped in the bud before many incorrect claims accumulate in the literature” and not to accuse. “So we decided not to list papers. Every case is different and we could not have done justice to particular studies had we been more specific. We didn’t even want to give a percentage [of how many studies resort to the ‘double dipping’ they criticize], but one of the four reviewers and the editors were adamant about this. . . . We feel that starting a political fight with hundreds of authors is not helpful to our field—especially when most of the studies affected are likely to be correct in their overall conclusion. . . . A few bad apples can and should be listed. But literally thousands of overall good apples, each with a little brown spot, can and should not."

Since the original criticism of fMRI studies earlier this year by Ed Vul and his colleagues, neuroscientists seem to have gotten more aware of statistical pitfalls that can skew their results. But there is no question that passions are running high and that back-biting and defensiveness have set in. At least one attempt to get critics and criticized together in the same room blew up.

Official reports of swine flu cases always lag behind actual cases. It takes time for people to get to a doctor, get diagnosed, and have the results reported to public health surveillance networks. And that assumes people get checked out: illegal aliens and other uninsured people in the U.S. might try to treat themselves and stay under the radar. But you don’t need health insurance or a doctor’s appointment to use Google. Led by research showing that the popularity of certain search terms correlates with a rise in the incidence of flu, the company just published flu estimates for 16 states in Mexico and the country as a whole, as well as U.S. data, in an effort “to help track the spread of the swine flu outbreak,” Google said in a statement.

The effort began last week, when epidemiologists at the U.S. Centers for Disease Control and Prevention asked the researchers at Google Flu Trends if they could track the incidence of swine flu in Mexico. Google Flu Trends, which was launched last November, analyzes the popularity of various search terms to detect a sudden increase in flu cases: historical data have shown that when lots of people start Googling terms such as “flu,” “aches,” “fever” and “buy thermometer,” it correlates with a rise in flu cases. (The researchers described their methodology in a February paper in Nature.) By comparing historical search data with historical data on flu cases, the Google team has been able to filter out search terms—such as plain old “swine flu”—that indicate curiosity rather than an actual case of the flu.

In a blog post today, Google software engineers Jeremy Ginsberg and Matt Mohebbi say “the system has detected increases in flu-related searches in Mexico City (Distrito Federal) and a few other Mexican states in recent days, beginning early in the week of April 19-25,” which was a week before widespread media reports of a swine flu outbreak. The flu incidence is still quite low, with only pockets of "moderate" activity. That suggests that the few hundred officials cases are not the tip of a huge iceberg.

It’s not clear if the Mexican search data are reliable, however. Unlike for the U.S., Google does not have data correlating actual flu incidence in Mexico with the popularity of particular search terms among people in Mexico. The lack of such historical data means the Google researchers “cannot be fully confident that the data is correct,” they say. But “we are cautiously optimistic that the graphs reflect actual flu activity. . . . While we would prefer to validate this data and improve its accuracy, we decided to release an early version today so that it might help public health officials and concerned individuals get an up-to-date picture of the ongoing swine flu outbreak.” Google Flu Trends will update the Mexican data every day.

Interestingly, Google Flu Trends shows low flu activity in the U.S. While it’s too soon to breathe a sigh of relief, that just might be an indication that public health authorities are not missing a huge number of swine flu cases, which as of 11 a.m. today numbered 91 in 10 states with one fatality. Google Flu trends promises, however, that it will “be keeping an eye on the data to look for any spike in [U.S.] activity.”

It’s bad enough when a medication for asthma, hypertension or anything else doesn’t do what it’s supposed to. Even more exasperating is knowing that the way the system of drug discovery and testing is set up, it’s practically guaranteed to produce medications that will not help a lot of the people they’re aimed at.

The reason is that, in order to participate in a clinical trial that the manufacturer runs to test whether an experimental compound is effective, you need to be sick but not too sick. Often, if you have something other than the illness the drug is targeted at, you don’t qualify. Thanks to this and other problems, conclude scientists at the University of Pittsburgh Graduate School of Public Health, the results of clinical studies used to get federal approval for common antidepressants do not apply for most patients with depression.

For their paper in the May issue of the American Journal of Psychiatry, epidemiologist Stephen Wisniewski and his colleagues compared depressed patients who met the criteria for joining a phase III clinical trial (which compares the experimental compound to a placebo) of antidepressants to those who did not. Although the inclusion criteria vary from study to study and are not subject to federal guidelines (the manufacturer gets to decide who’s in and who’s out),  patients with milder forms of depression are typically excluded (they’re more likely to respond to the placebo, making the experimental drug look less effective by comparison). Excluded, too, are patients with chronic depression (they're harder to get a response from, also making the new drug look ineffective) or with additional psychiatric and medical illnesses (who might show dangerous side effects or not respond to the drug).

So, how many patients with depression are thereby excluded? Assessing 2,855 patients treated with citalopram (sold by Forest Labs as Celexa), the scientists find that only 22 percent of people with depression qualify for phase III clinical trials of antidepressants. That means that 78 percent of people with depression are getting the supposedly helpful drug, even though there are no data on whether it is safe and effective in people like them. “This raises major concerns about whether results from traditional phase III studies can be generalized to most people with depression, who also often suffer from anxiety, substance abuse and other medical and psychiatric problems,” said Wisniewski.

The concern is not merely hypothetical. When the scientists assessed how well patients did on antidepressants, they found that those few who would have been eligible for the clinical trial had higher remission rates (34 percent vs. 25 percent for the ineligible patients), and fewer serious side effects and adverse events. The clinical trials that serve as the basis for getting a new drug approved—and that the press trumpets as indicating how effective a drug is—“suggest more optimistic outcomes than may exist for real-world patients” with depression,” said Wisniewski.

Even when you’re skewering an entire field of science, the better part of valor might be to use terms such as “circular analysis” rather than, say, “voodoo.”

The latter is how a team of scientists characterized some findings from brain imaging, as I described in a print column and a previous post about an upcoming paper in Perspectives on Psychological Sciences (but available ahead of print here) by Ed Vul of MIT, Hal Pashler of UC San Diego and colleagues. Its original title of “Voodoo Correlations” in fMRI studies has been replaced by the much-politer “Puzzlingly High Correlations” in fMRI studies, but the message is the same: conclusions from brain-imaging studies of social and emotional aspects of human behavior (jealousy, altruism, social pain and the like) might be wrong and cannot be trusted unless they are re-done with greater statistical rigor.

Now a new study, published last night online for the May issue of Nature Neuroscience, offers an equally devastating critique. Nikolaus Kriegeskorte, Chris Baker and colleagues, of the National Institute of Mental Health, analyzed all the fMRI studies published last year in five top journals (Nature, Science, Nature Neuroscience, Neuron and Journal of Neuroscience). Of the 134 fMRI papers, 42 percent (57 papers) committed a statistical sin at least once: they were guilty of what the NIMH scientists call “double dipping.”

In double dipping, scientists start with a hypothesis that some region of the brain is involved in, say, feeling jealous, and therefore responds to (say) a photo of your romantic rival by becoming extremely active. It’s double dipping, and problematic from a statistical sense, if the scientists then look for those more-active brain regions and analyze only these areas to test the hypothesis. The problem is that brain regions may become more active when they see that photo (compared to seeing, say, a landscape) purely by chance, and analyzing only to these regions would give a misleading result. For the statisticians among you, it’s called non-independent selective analysis.

And it can turn dross into gold. When the NIMH team analyzed “noise”—that is, random data that was known not to show any effect—they still obtained results that seemed to connect a stimulus with a brain response. In other words, double dipping can do wonders for your study. As Kriegeskorte and his colleagues write, the practice “beautifies results, rendering them more attractive to authors, reviewers and editors, and thus more competitive for publication. These implicit incentives may create a preference for circular practices so long as the community condones them.”

As with the Vul et al. critique, this doesn’t mean that all the fMRI results are wrong. The point is, you can’t tell. “To decide which neuroscientific claims hold,” the NIMH scientists write, “the community needs to carefully consider each particular case, guided by both neuroscientific and statistical expertise. Reanalyses and replications may also be required.”

The British Psychological Society has a nice write-up of the new paper here. And Pashler, one of the “voodoo” authors, calls my attention to what he calls a “funny thing about the Kriegeskorte paper” that I didn't notice: unlike his team, the NIMH scientists “didn’t publish the list of which of the 2008 papers were, and were not, afflicted with problems. . . . I can well understand why, since he is a full time fMRI researcher and needs to avoid ticking those people off—something none of us were terribly worried about. And we do know how thin-skinned they are. But there is some tension between the idea of a secret list of bad studies, on the one hand, and the whole notion of science as a public self-correcting enterprise, with a Literature that can be relied upon.”

Science does tend to move at glacial speed, but isn’t it time the fMRI community came to grips with the growing criticism of its methods?

It isn’t every day that one federal agency says the work of another has such “serious scientific flaws” that the work is “not a product [we] should endorse as it does not reach the level of scientific rigor.” Nor is it every day that federal agency #1 (as we’ll call it) says that while federal agency #2 may have tried to get its act together in response to earlier criticism, the work is “essentially unchanged, and . . . [still] scientifically flawed.”

Yet this is how the U.S. Environmental Protection Agency characterizes an analysis by the U.S. Food and Drug Administration. The comments from EPA are in response to an analysis by FDA, issued in the waning days of the Bush administration, that lays the groundwork for a change in federal policy which currently warns people to avoid high-mercury fish and eat lots of the low-mercury kind (a list of what’s high and what’s low can be found here). FDA's analysis would support telling people to eat all the high-mercury fish they want. FDA had received 248 responses by the time the comment period closed earlier this week. (My thanks to the Environmental Working Group, whose own comments on the FDA proposal pointed me to and quote from the EPA response. You can find EWG’s letter here; the docket is a nuisance to navigate around, but if you go to that page and click on the pdf icon, you’ll get EWG’s comment. EPA’s is here; click on the Word icon.)

Here's the backstory. In January, FDA published what’s called a draft assessment of the benefits and risks of eating fish. It asked for public comment. While it’s hard summarize the technically dense, 350-page report, FDA’s conclusions seem to be that mercury risks are very small, and that telling women to eat more fish has greater public health benefits than telling them to eat low-mercury fish. (The focus is on women because fetuses are at the highest risk from mercury. Mercury is a neurotoxin and the developing brains of fetuses and kids are most vulnerable to its poisonous effects.) Next step (though FDA doesn't say this): stop telling people to limit their consumption of high-mercury fish.

Bizarrely, FDA has structured the choice as “either/or:” Eat more fish, or eat low-mercury fish. For reasons I can’t imagine, FDA left out of its analysis the scenario that combines the two: eat more fish, but only low-mercury fish. In every scenario it ran, fish benefits and mercury damage largely offset each other. Obviously, the eat-more-low-mercury fish approach would have greater benefits than doing one but not the other—as researchers (and FDA) have been saying for years. Why would you want to wipe out the brain-healthy effects of fish by having a little neurotoxin on the side?

Predictably, the fishing industry and professional mercury risk-deniers have lauded the FDA analysis. Industry comments describe it as “a comprehensive analysis supporting consumption of seafood.” David Martosko, research director at the Center for Consumer Freedom, wrote in his comments that FDA’s analysis shows that the current FDA/EPA advisory about limiting how much high-mercury fish you eat “has been sending consumers the wrong message.” He urges that the advisory be withdrawn or revised: just tell women to eat more fish, and stop telling them to consider the mercury levels in different fish.

Research scientists and EPA, however, say FDA’s analysis is junk science. For one thing, FDA’s estimate of mercury risk is based on a 22-year-old study in Iraq that observed the age when kids first talk—but the children’s actual ages were unknown. FDA “adjusted” that risk estimate with data from the Seychelles Islands, where the harmful effects of mercury were obscured by the benefits of eating fish. A report from a committee of the National Academy of Sciences concluded in 2000 that the Iraq and Seychelles studies are not the best evidence, and urged the government to use data from a study in the Faeroe Islands. FDA didn’t.

Which is one reason why EPA was so scathing in its comments on its sister-agency’s analysis. It takes FDA to task for dozens of errors and faulty assumptions. One example: FDA relied on research on the neurodevelopmental risks mercury poses which “had been completely abandoned by the scientific community as a basis for risk assessment for more than a decade,” says EPA. EPA’s comments go on to note many other “questionable, faulty or unfounded choices with the effect of boosting benefits or reducing risks from seafood consumption above what is justified scientifically,” EWG says in its comments. EPA concludes that “a fish consumption advisory strategy based on the design of the FDA draft analysis would be highly inconsistent with what is generally considered to be proper public health practice.”

In its soon-to-be 100 days in office (April 29), the Obama administration has begun to clear out the detritus of the junk science and politicized science it inherited from the Bush years. It will be interesting to see whether the idea that it’s fine to eat fish containing high levels of a neurotoxic compound will survive the culling or be recognized for the bad science it is.

One final note: as the battle over what information the public should be getting about fish and mercury rages, the tuna industry might need to watch its flank. In a little-noticed decision this Monday, the U.S. Supreme Court declined to hear an appeal from Tri-Union Seafoods, which makes Chicken-of-the-Sea tuna. Tri-Union sought to block a lawsuit by a New Jersey woman who got methylmercury poisoning from eating its product. The company argued that since the FDA considers canned tuna safe enough to be sold without a warning label, individuals can’t sue over alleged injuries. The Court’s decision lets stand a ruling by an appeals court that the lawsuit can proceed. The plaintiff, Deborah Fellner, ate almost noting but canned tuna for five years, and got a classic case of mercury poisoning, according to her lawsuit. She argues that the company was negligent in failing to warn her of its tuna's high mercury content.

Early birds wake up at the crack of dawn and struggle to stay alert and productive (especially in the cognitive realm) in the evening. Night owls perform well in the evening but are worthless if you yank them out of bed too early in the morning. There are less-well-known differences, too: early birds experience what scientists call “a faster build-up of homeostatic sleep pressure” during the day compared to night owls, who, like a certain battery-powered bunny, just seem to keep going and going, resisting the pressure to sleep. (That must be why I practically turn into a pumpkin by 9 p.m.) And when they do sleep, early birds experience a faster dissipation of that sleep pressure, feeling restored more quickly than night owls. Now a new study, in the journal Science, reports some intriguing differences between the brain-activity patterns of the two types that underlie the behavioral differences.

Scientists led by Christina Schmidt and Philippe Peigneux of the University of Liege in Belgium had 15 extreme night owls and 16 extreme early birds spend two nights in a sleep lab. The two groups were separated by about four hours in their sleep patterns; if early birds were happy waking up at 7, night owls slept til 11, and early birds were ready to go to sleep at 11 while night owls had no trouble staying up til 3 in the morning. An hour and a half after waking up, and again 10.5 hours after waking up, the volunteers had their brain activity measured by fMRI while they took a simple reaction-time test of their ability to maintain focused attention. Both the early birds and the night owls were sleeping and waking whenever they pleased, rather than being kept on an artificial schedule.

There was no real difference between the early birds and the night owls in their performance on the morning test. But the evening test was a different story: night owls were less sleepy and had faster reaction times than early birds. (Just to emphasize, 'evening' was a relative term: it was a different actual time for each group, but the exact same 10.5-hours-after-waking for both early birds and night owls.) So even though both groups were sleeping and waking according to their preferred schedule, night owls generally outlasted early birds in how long they could stay awake and mentally alert before becoming mentally fatigued. The fMRI supported the behavioral results: 10.5 hours after waking up, the early birds had lower activity in brain regions linked to attention and the circadian master clock, compared to night owls.

It’s hard to resist an astronomy discovery when it’s called a blob, even if the precise name is the Lyman-Alpha blob. In a paper being published this afternoon in Astrophysical Journal, astronomers are announcing that they spied such an object—thought to be an enormous body of gas that may be the precursor to a galaxy—dating from when the universe was a mere 800 million years old. Stretching for 55,000 light years (approximately the radius of our Milky Way galaxy’s disk), this Lyman-Alpha blob has astronomers scratching their heads.

Named Himiko for a legendary Japanese shaman queen, the blob is not the largest such object ever discovered. That honor goes to a Lyman-Alpha blob reported in 2006 and thought to be the biggest object in the universe. Instead, this one is notable because it is so far away, and in cosmic terms far away = long ago. “The farther out we look into space, the farther we go back in time,” says astronomer Masami Ouchi of the Observatories of the Carnegie Institution , who led the international team that made the discovery: because light travels at a finite velocity, it takes time for light from objects in space to reach the eyes of Earthlings or their telescopes, which means we are seeing the blob as it was near the dawn of time, when the universe was barely 6 percent of its current age of 13.7 billion years. That means light from Himiko has been traveling toward us for 12.9 billion years, which is equivalent to saying we are seeing it was it was 12.9 billion years ago.

And that makes astronomers a bit uneasy. Whether the blob is ionized gas powered by a supermassive black hole, a primordial galaxy, the collision of two young galaxies or a single giant galaxy with a mass of 40 billion Suns—all of which are on the table—it’s too big for its age. As Ouichi puts it, “I have never imagined that such a large object could exist at this early stage of the universe’s history. According to . . . Big Bang cosmology, small objects form first and then merge to produce larger systems. This blob had a size of typical present-day galaxies when the age of the universe was about 800 million years old.” In fact, other blobs had the decency to wait to show up, appearing when the universe was 2 to 3 billion years old. No extended blobs had been found from when the universe was younger, until Himiko, which means astronomers need to scurry back to the drawing boards to figure out how an object this massive managed to grow up so fast.

There’s all sorts of mumbo-jumbo about how sports drinks boost athletes’ performance, especially in endurance events such as yesterday’s Boston Marathon. But according to an intriguing new study, it isn’t the sports drinks’ calories (athletes benefit even if they spit out the drink rather than swallow it) or their sweet taste (drinks with artificial sweeteners do not boost performance). Instead, suggest Ed Chambers of the University of Birmingham and colleagues in a paper in The Journal of Physiology, carbohydrates in the drinks fit into receptors in the mouth that in turn activate the brain’s pleasure and reward centers, spurring athletes to push themselves harder without realizing how hard they're working.

For their study, the scientists prepared drinks containing either glucose (a sugar), maltodextrin (a tasteless carbohydrate) or plain water, mixed with artificial sweeteners so they tasted identical. Eight endurance cyclists rinsed their mouths for 10 seconds with one of the three drinks, and then got on a stationary bike. Results: athletes who swished with the glucose or maltodextrin drinks outperformed those on sweetened water by 2 to 3 percent, raising their pulse and sustaining a higher average power output—even though they said they didn’t feel they were working harder.

Chambers explains it this way: “Much of the benefit from carbohydrate in sports drinks is provided by signalling directly from mouth to brain rather than providing energy for the working muscles.”

That was born out by neuroimaging. Using fMRI to monitor brain activity after the athletes rinsed their mouths with one of the three drinks, the scientists found that glucose and maltodextrin increased activity in regions associated with reward or pleasure (the anterior cingulate cortex and striatum). The artificially sweetened water did not. They propose that the sugar or carbohydrate glommed onto receptors in the mouth, causing a signaling cascade that activated these brain regions, with the result that the athletes felt they were not working as hard as they actually were—contributing to endurance and power output. Once again, it seems as if the brain, not the muscles, ultimately govern how well we do even on what seems to be a purely physical task. As the scientists put it, “carbohydrate in the human mouth activates regions of the brain that can enhance exercise performance.”

For patients suffering from locked-in syndrome, in which they are completely paralyzed and able to do no more than blink their eyes, the greatest hope is not walking, not feeding themselves, not anything else having to do with moving: it is communicating. (An episode of House last month did a good job of depicting the horror of locked-in syndrome, which can be caused by amyotrophic lateral sclerosis, aka Lou Gehrig’s disease, brain-stem stroke or high spinal cord injury.) Hence the intense research effort to build brain-computer interfaces (BCI) for such patients. As a 2007 publication from the National Institutes of Health described a BCI system being developed there, “eight electrodes hitched to the computer . . . record the user’s electrical brain waves, which the computer analyzes and translates into specific commands, such as writing emails, selecting computer icons, or moving robotic devices. No surgery is required and users typically master the system within an hour or two.”

Writing emails is all well and good, but now brain-computer interfaces have made the big leagues: a BCI has been used to Tweet. Earlier this month Adam Wilson, a graduate student in biomedical engineering at the University of Wisconsin-Madison, sent “using EEG to send tweet.” He used what has become the standard methodology, in which EEGs pick up electrical signals from the brain and translate them into movements of a cursor, in this case on a screen with the 26 letters of the alphabet, as the scientists show in this video.

“The way this works is that all the letters come up, and each one of them flashes individually,” says Justin Williams, a UW-Madison assistant professor of biomedical engineering and Wilson’s adviser. “And what your brain does is, if you’re looking at the ‘R’ on the screen and all the other letters are flashing, nothing happens. But when the ‘R’ flashes, your brain says, ‘Hey, wait a minute. Something’s different about what I was just paying attention to.’ And you see a momentary change in brain activity.”

Although it’s a tedious process—Wilson likens it to texting, when you may have to press a key four times to get the desired character—people get better with practice. “I’ve seen people do up to eight characters per minute,” he says. Which just goes to show that Twitter is not the civilization-ending toy that so alarms some people: locked-in patients may be able to use it to give friends and families status updates merely by thinking.

You can always count on studies of daycare to scare the living daylights out of parents, especially when they find that the more hours kids spend in daycare the more likely they are to be aggressive (a conclusion that, critics said, reflected shortcomings in the study) and that poor-quality daycare can hinder kids’ cognitive development, as the original report of a long-running study and a more user-friendly write-up both note.

These findings and more have emerged from the longest-running and most comprehensive study of daycare, the Study of Early Child Care and Youth Development, which the National Institute of Child Health and Human Development began in 1991. But there has always been something funny about these findings, namely, the small size of the effect of daycare. It raises the possibility that daycare does have strong effects on kids, but that the effects differ depending on the child, with the result that when you take an average over thousands of kids in a study you wash out the individual effects.

Psychologist Jay Belsky suspects something like that is going on. Now at Birkbeck College in London (he also blogs for Psychology Today), he suggests that children have what he calls a “differential susceptibility” to their environment, including the environment called childcare. (I wrote about his work in a story on how children’s genetic differences affect how they’ll respond to various forms of parenting, such as learning from mistakes.) According to this model, the reason studies don’t find a greater effect of child care is that they mix apples and oranges: the apples are children who are susceptible to the effects of daycare and the oranges are children who are not. As Belsky and colleague Michael Pluess put it in a paper in the April issue of the Journal of Child Psychology and Psychiatry, “Inconsistencies regarding developmental effects of non-maternal childcare may be caused by neglecting the possibility that children are differentially susceptible towards such experiences.”

Using the NICHD data, they therefore looked for relationships between a child’s temperament (which is under at least partial genetic control), childcare and outcomes when the children were about four-and-a-half. Their conclusion: “children with difficult temperaments as infants exhibited both more behavior problems when faced with low quality care and fewer when experiencing high quality care than children with easy temperaments.” These difficult children—hard to soothe, cranky, sensitive—really feel the effects of daycare, for good or ill. That doesn’t mean you shouldn’t put such a child in daycare. Look carefully at the last part of the quote above. Although these “negatively-emotional infants” are apt to emerge from poor-quality daycare at age 5 with more social and behavioral problems that children not in daycare, they have fewer such problems than the stay-at-home kids when they receive high-quality daycare. Mellower children are like Teflon: the effects of daycare, of high or low quality, simply do not stick to them as much.

Message: know your child. Know your daycare.

There is no clearer evidence of how controversial geo-engineering (altering the atmosphere so as to reduce global warming, perhaps by lofting a haze of sulfate aerosols into the stratosphere to reduce incoming sunlight) is than the tempest stirred up when White House science adviser John Holdren told the Associated Press that the administration was discussing it. To lots of people concerned about global warming, merely mentioning geo-engineering detracts from the urgency to reduce emissions of greenhouse gases, conveying a message of, “oh, no problem; we can keep making this mess but deploy a techno-fix when we need to.” But let’s leave politics aside for a nanosecond. The risk of geo-engineering is, to put it bluntly, that we’re not smart enough to know what climate effects it will produce.

A 2007 study has already shown that using sulfate aerosols to reduce incoming sunlight, and thereby cool the planet, would also mess up precipitation patterns enough to trigger serious droughts. Now a new study pinpoints what can only be described as a truly ironic side-effect: lofting sulfates into the stratosphere would also reduce the effectiveness of solar power, the renewable, zero-carbon energy source that has the best chance of averting catastrophic climate change.

Writing in Environmental Science & Technology, Daniel Murphy of the National Oceanographic and Atmospheric Administration explains that while enhancing the stratospheric aerosol layer makes some of the incoming sunlight bounce back to space (producing cooling), it scatters even more of the sunlight. For every 1 watt of sunlight reflected away from the Earth, another 3 watts of direct sunlight are converted to diffuse sunlight. Unfortunately, it is direct, rather than scattered, sunlight that large concentrated-solar-energy facilities need in order to reach maximum efficiency; they can’t use diffuse light. (Concentrating systems focus sunlight onto photovoltaic cells, to produce electricity directly, or onto tubes to produce steam or a hot fluid.) Put another way, every 1 percent reduction in sunlight due to aerosols causes a 4 to 10 percent loss in output from concentrated-solar collectors.

Flat solar hot water and photovoltaic panels would experience less of a loss, because they can use diffuse sunlight. But “the performance loss will still exceed the reduction in total sunlight because a tilted panel does not capture diffuse sunlight as efficiently as direct sunlight,” notes Murphy. Bottom line: “a significant reduction in the efficiency of solar power generation systems.”