A potato affected by P. infestans, the pathogen responsible for the Irish Potato Famine. The exact strain involved in the 1840s famine has now been identified for the first time. Image via USDA

For nearly 150 years, starting in the late 17th century, millions of people living in Ireland subsisted largely off one crop: the potato. Then, in 1845, farmers noticed that their potato plants’ leaves were covered in mysterious dark splotches. When they pulled potatoes from the ground, most were shrunken, mushy and inedible. The blight spread alarmingly quickly, cutting yields from that year’s harvest in half. By 1846, harvest from potato farms had dropped to one quarter of its original size.

The disease—along with a political system that required Ireland to export large amounts of corn, dairy and meat to England—led to widespread famine, and nearly all of the few potatoes available were eaten, causing shortages of seed potatoes that ensured starvation would continue for nearly a decade. Ultimately, over one million people died, and another million emigrated to escape the disaster, causing Ireland’s population to fall by roughly 25 percent; the island has still not reached its pre-famine population levels today.

At the time, the science behind the blight was poorly understood, and most believed it was caused by a fungus. During the twentieth century, scientists determined that it was caused by an oomycete (a fungus-like eukaryote) called Phytophthora infestans. However, without access to the 1840s-era specimens, they couldn’t identify exactly which strain of the organism was responsible.

Now, an international group of scientists has gone back and sampled the DNA of Irish potato leaves preserved in the collections of London’s Kew Gardens since 1847. In doing so, they discovered that a unique, previously unknown strain of P. infestans that they call HERB-1 caused the blight.

Irish potato leaves from 1847, the height of the famine, used as part of the study. Image via eLife/Kew Gardens

The researchers, from the Sainsbury Laboratory in the UK and the Max Planck Institutes in Germany, came to the finding as part of a project sequencing DNA from 11 different preserved historial samples and 15 modern ones to track the evolution of the pathogen over time, published today in the journal eLife [PDF].

Currently, P. infestans is distributed worldwide, with the vast majority comprised of the destructive. strain US-1. Most of the other strains of P. infestans occur only in Mexico’s Toluca Valley, where wild potato varieties are indigenous, so scientists long believed that US-1 had been responsible for the 1840s famine.

But when the researchers extracted small pieces of intact DNA from the old dried-out potato leaves, originally collected from from Ireland, Great Britain, Europe and North America, and compared them with present-day P. infestans specimens, they found that the strain responsible for the famine differed slightly from today’s US-1.

Based on their analysis of the genetic variation between the two strains and the other historical samples, they suggest that sometime in 1842 or 1843, the ancestor of the HERB-1 strain of P. infestans specimen made it out of Mexico to North America and then to Europe, perhaps contained within the potatoes that ships carried as food for their passengers. Soon, it spread across the world, triggering famine in Ireland, and persisting until the 1970s, when it died out and was largely replaced by the US-1 strain. The two strains likely split apart sometime soon after their common ancestor made it out of Mexico.

The study is the first time that the genetics of a plant pathogen have been analyzed by extracting DNA from dried plant samples, opening up the possibility that researchers can study other plant diseases based on the historical collections of botanical gardens and herbaria around the world. Better understanding the evolution of plant diseases over time, the team says, could be instrumental in figuring out ways to breed more robust plant varieties that are resistant to the pathogens that infect plants today.

Tweets from around the world, plotted by location as part of a new study. Click to enlarge. Image via First Monday/Leetaru et. al.

It’s hard to appreciate just how quickly and thoroughly Twitter has taken over the world. Just seven years ago, in 2006, it was an idea sketched out on a pad of paper. Now, the service is used by an estimated 554 million users—a number that amounts to nearly 8 percent of the all humans on the planet—and an estimated 170 billion tweets have been sent, with that number climbing by roughly 58 million every single day.

All these tweets provide an invaluable source of news, entertainment, conversation and connection between people. But for scientists, they’re also valuable as something rather different: raw data.

Because Twitter features an open API (which allows for tweets to be downloaded as raw, analyzable data) and many tweets are geotagged, researchers can use billions of these tweets and analyze them by location to learn more about the geography of humans across the planet. Last fall, as part of the Global Twitter Heartbeat, a University of Illinois team analyzed the language and location of over a billion tweets from across the U.S. to create sophisticated maps of things like positive and negative emotions expressed during Hurricane Sandy, or support for Barack Obama or Mitt Romney during the Presidential election.

As Joshua Keating noted on Foreign Policy‘s War of Ideas blog, members of the same group, led by Kalev Leetaru, have recently gone one step further. As published in a new study earlier this week in the online journal First Monday, they analyzed the locations and languages of 46,672,798 tweets posted between October 23 and November 30 of last year to create a stunning portrait of human activity around the planet, shown at the top of the post. They made use of the Twitter decahose, a data stream that  captures a random 10 percent of all tweets worldwide at any given time (which totaled 1,535,929,521 for the time period), and simply focused on the tweets with associated geographic data.

As the researchers note, the geographic density of tweets in many regions—especially in the Western world, where computers, mobile devices, and Twitter are all used at peak levels—closely matches rates of electrification and lighting use. As a result, the maps of tweets (such as the detail view of the continental U.S., below) end up looking a lot like satellite images of artificial light at night.

Click to enlarge. Image via First Monday/Leetaru et. al.

As a test to see how well tweets matched artificial light use, they created the composite map below, in which tweets are shown as red dots and nighttime lighting is shown as blue. Areas where they correspond in frequency (and effectively cancel each other out) are shown as white, and areas where one outweighs the other remain red or blue. Many areas end up looking pretty white, with some key exceptions: Iran and China, where Twitter is banned, are noticeably blue, while many countries with relatively low electrification rates (but where Twitter is still popular) appear as red.

Click to enlarge. Image via First Monday/Leetaru et. al.

The project got even more interesting when the researchers used an automated system to break down tweets by language. The most common language in Twitter is English, which is represented in 38.25 percent of all Tweets. After that came Japanese (11.84 percent), Spanish (11.37 percent), Indonesian (8.84 percent), Norwegian (7.74 percent) and Portugese (5.58 percent).

The team constructed a map of all tweets written in the 26 most popular languages, with each represented by a different color, below:

Click to enlarge. Image via First Monday/Leetaru et. al.

While most countries’ tweets are dominated by their official languages, many are revealed to include tweets in a variety of other languages. Look closely enough, and you’ll see a rainbow of colors subtly popping out from the grey dots (English tweets) that blanket the U.S.:

Click to enlarge. Image via First Monday/Leetaru et. al.

Among other analyses, the research team even looked at the geography of retweeting and referencing—the average distance between a user and someone he or she retweets, as well as the average distance between that user and someone he or she simply references in a tweet. On average, the distance for a retweet was 1,115 miles and 1,118 for a reference. But, counterintuitively, there was a positive relationship between the number of times a given user retweeted or referenced another user and their distance: Pairs of users with just a handful of interactions, on the whole, were more likely to be closer together (500-600 miles apart) than those with dozens of retweets and references between them.

This indicates that users who live far apart are more likely to use Twitter to interact on a regular basis. One explanation might be that the entities with the most followers—and thus the most references and retweets—are often celebrities, organizations or corporations, users that people are familiar with but don’t actually have a personal relationship with. A global map of retweets between users is below:

Click to enlarge. Image via First Monday/Leetaru et. al.

The paper went into even more detail on other data associated with tweets: the ratio between mainstream news coverage and number of tweets in a country (Europe and the U.S. get disproportionate media coverage, while Latin America and Indonesia are overlooked), the places Twitter has added the most users recently (the Middle East and Spain) and the places where users have, on average, the most followers (South America and the West Coast).

There are a few caveats to all this data. For one, though the tweets analyzed number in the tens of millions, they are still just 0.3 percent of all tweets sent, so they might not adequately represent all Twitter patterns, especially if users who enable geotagging behave differently than others. Additionally, in the fast-changing world of Twitter, some trends might have already changed significantly since last fall. But as Twitter continues to grow and as more data become available, it stands to reason that this sort of analysis will only become more popular for demographers, computer scientists and other researchers.

New research shows our brains have a specialized system to anticipate the location of moving objects, located in V5 region of the visual cortex. Image via Wikimedia Commons/Calebrw

Throwing a baseball is hard. As xkcd pointed out just yesterday, accurately throwing a strike requires that a pitcher release the ball at an extremely precise moment—doing so more than half a millisecond too early or too late causes it to miss the strike zone entirely. Because it takes far longer (a full five milliseconds) just for our nerve impulses to cover the distance of our arm, this feat requires the brain to send a signal to to the hand to release the ball well before the arm has reached its proper throwing position.

The one feat even more difficult than throwing a fastball, though, might be hitting one. There’s a 100 millisecond delay between the moment your eyes see an object and the moment your brain registers it. As a result, when a batter sees a fastball flying by at 100 mph, it’s already moved an additional 12.5 feet by the time his or her brain has actually registered its location.

How, then, do batters ever manage to make contact with 100 mph fastballs—or, for that matter, 75 mph change-ups?

In a study published today in the journal Neuron, UC Berkeley researchers used fMRI (functional magnetic resonance imaging) to pinpoint the prediction mechanisms in the brain that enable hitters to track pitches (and enable all sorts of people to envision the paths of moving objects in general). They found that the brain is capable of effectively “pushing” forward objects along in their trajectory from the moment it first sees them, simulating their path based on their direction and speed and allowing us to unconsciously project where they’ll be a moment later.

The research team put participants in an fMRI machine (which measures blood flow to various parts of the brain in real time) and had them watch a screen showing the “flash-drag effect” (below), a visual illusion in which a moving background causes the brain to mistakenly interpret briefly flashed stationary objects as moving. “The brain interprets the flashes as part of the moving background, and therefore engages its prediction mechanism to compensate for processing delays,” said Gerrit Maus, the paper’s lead author, in a press statement.

Because the participants’ brains thought these briefly flashing boxes were moving, the researchers hypothesized, the area of their brain responsible for predicting the motion of objects would show increased activity. Similarly, when shown a video where the background didn’t move but the flashing objects actually did, the same motion-prediction mechanism would cause similar neuron activity to occur. In both cases, the V5 region of their visual cortex showed distinctive activity, suggesting this area is home to the motion-prediction capabilities that allow us to track fast-moving objects.

Previously, in another study, the same team had zeroed in on the V5 region by using transcranial magnetic stimulation (which interferes with brain activity) to disrupt the area and found that participants were less effective at predicting the movement of objects. “Now not only can we see the outcome of prediction in area V5, but we can also show that it is causally involved in enabling us to see objects accurately in predicted positions,” Maus said.

It’s not much of a leap to suppose that this prediction mechanism is more sophisticated in some people than others—which is why most of us would whiff when trying to hit the fastball of a major league pitcher.

A failure in this mechanism might be at work, the researchers say, in people who have motion perception disorders such as akinetopsia, which leaves the ability to see stationary objects completely intact but renders a person essentially blind to anything in motion. Better understanding how neurological activity in the V5 region—along with other areas of the brain—allows us to track and predict movement could, in the long-term, help us develop treatments for these sorts of disorders.

Recent testing shows that, contrary to prior findings, new plastic helmets reduce the risk of concussions by 45 to 96 percent. Image via Journal of Neurosurgery, Rowson et. al.

In the past century or so, football helmets have come a long way, evolving from crude “leatherheads” crafted by shoemakers to plastic-and-rubber hybrids that can be customized to fit a player’s head and have radios built in.

Nevertheless, the sport currently faces a serious and growing problem: brain injuries. Studies have shown that former NFL players are about three times more likely to die from Alzheimer’s, Parkinson’s and Lou Gehrig’s diseases as the general population, a result of the alarming number of concussions they experience over the course of their careers. The NFL has responded by changing its rules to minimize head impacts, instituting stricter guidelines for concussed players returning to games and pouring money into attempts to develop safer helmets.

But some critics argue that, no matter how much research we undertake, there’s simply no way to create a concussion-proof helmet—no technology can stop a fundamentally violent game from inflicting harm. A 2011 study, in fact, found that with many types of impacts, modern helmets were no better than vintage leather ones at protecting players’ heads.

But now, fans who find their desire for a safe game at odds with their love of it can take comfort in a new study, published today in the Journal of Neurosurgery, that determines otherwise: Compared to “leatherheads,” new helmets are indeed much more effective at protecting the human head. Researchers from Virginia Tech came to the finding by using an automated head impact simulation system to test the effectiveness of a pair of vintage Hutch H-18 leather helmets from the 1930s against 10 plastic helmets currently in use, and found that, depending on the force of impact, modern helmets reduced the concussion risk by anywhere from 45 to 96 percent.

The impact system used in the study, simulating a frontal impact (A) and a side impact (B). Image via Journal of Neurosurgery, Rowson et. al.

The team used the system to measure four different types of head impacts (on the front, side, rear and top of the head), and dropped the head from a range of heights (12, 24, 36, 48 and 60 inches) with each helmet on to simulate in-game impacts of a variety of intensities. Sensors inside the head were used to measure the force of each type of impact. This same type of testing, developed by the Virginia Tech team, has been used extensively to classify the safety of modern helmets on a 1 through 5-star scale.

The researchers found that there was some difference in the performance of the modern helmets—but, as you’d probably expect just from looking at them, the vintage leather helmets performed significantly more poorly than any of the plastic ones. At the lowest intensity impacts (from a 12 inch drop height), the modern helmets reduced the impact on the head by by 59 to 63 percent, and at the medium-intensity impacts (from 36 inches), they provided a 67 to 73 percent reduction. The researchers didn’t even try dropping the head with the leather helmets on from 48 or 60 inches for fear of damaging them.

At the same time, it’s worth noting that the vintage helmets tested were each about 80 years old, so their age might have meant weaker leather fibers than if pristine leatherheads had been tested. Additionally, the leather helmets had presumably taken some beating during their years of use, while the plastic ones were unused before being subjected to the drops, which might have further skewed the results.

Still, both these factors were also included in the previous 2011 finding that leather helmets were just as effective as modern ones—so what accounts for the fact that this experiment so thoroughly contradicted it? The authors of this study say that the experimental setup used in the previous one—in which two helmeted heads were smashed together, one with a modern helmet and the other with either a modern or leather one—distorted the findings and masked the differences between helmet types. Some of the impact, they say, was actually absorbed by the padding in the modern plastic helmet even when the leather one was being tested.

Of course, given the distressing numbers on the concussions suffered by football players even with the latest helmets, this sort of testing shouldn’t suggest that the goal of designing safer head gear has been achieved. But it should give us a bit of hope in showing that 100 years of helmet design has provided some benefits—and future efforts to create and rigorously test new helmet technologies might be able to cut down on concussions long-term.

Our bodies convert asparagusic acid into sulfur-containing chemicals that stink—but some of us are spared from the pungent aroma. Photo by Gunnar Magnusson

If you’ve ever noticed a strange, not-entirely-pleasant scent coming from your urine after you eat asparagus, you’re definitely not alone.

Distinguished thinkers as varied as Scottish mathematician and physician John Arbuthnot (who wrote in a 1731 book that “asparagus…affects the urine with a foetid smell”) and Marcel Proust (who wrote how the vegetable “transforms my chamber-pot into a flask of perfume”) have commented on the phenomenon.

Even Benjamin Franklin took note, stating in a 1781 letter to the Royal Academy of Brussels that “A few Stems of Asparagus eaten, shall give our Urine a disagreable Odour” (he was trying to convince the academy to “To discover some Drug…that shall render the natural Discharges of Wind from our Bodies, not only inoffensive, but agreable as Perfumes”—a goal that, alas, modern science has still not achieved).

But modern science has, at least, shed some light on why this one particular vegetable has such an unusual and potent impact on the scent of urine. Scientists tell us that the asparagus-urine link all comes down to one chemical: asparagusic acid.

Asparagusic acid, as the name implies, is (to our knowledge) only found in asparagus. When our bodies digest the vegetable, they break down this chemical into a group of related sulfur-containing compounds with long, complicated names (including dimethyl sulfide, dimethyl disulfide, dimethyl sulfoxide and dimethyl sulfone). As with many other substances that include sulfur—such as garlic, skunk spray and odorized natural gas—these sulfur-containing molecules convey a powerful, typically unpleasant scent.

All of these molecules also share another key characteristic: They’re volatile, meaning that have a low enough boiling point that they can vaporize and enter a gaseous state at room temperature, which allows them to travel from urine into the air and up your nose. Asparagusic acid, on the other hand, isn’t volatile, so asparagus itself doesn’t convey the same rotten smell. But once your body converts asparagusic acid into these volatile, sulfur-bearing compounds, the distinctive aroma can be generated quite quickly—in some cases, it’s been detected in the urine of people who ate asparagus just 15-30 minutes earlier.

Of course, the whole asparagus-urine scent issue is complicated by an entire separate issue: Some people simply don’t smell anything different when urinate after they eat asparagus. Scientists have long been divided into two camps in explaining this issue. Some believe that, for physiological reasons, these people (which constitute anywhere from 20 to 40 percent of the population) don’t produce the aroma in their urine when they digest asparagus, while others think that they produce the exact same scent, but somehow lack the ability to smell it.

On the whole, the evidence is mixed. Initially, a pair of studies conducted in the 1980s with participants from France and Israel found that everyone produced the characteristic scent, and that a minority of people were simply unable to smell it. People with the ability to detect the scent, though, were able to smell it even in the urine of those who couldn’t smell it, indicating that the differences were rooted in perception, not production.

More recent studies, though, suggest the issue is a bit more complicated. The most recent study, from 2010, found that differences existed between individuals in both the production and detection of the scent.

Overall, scientists now conclude that most of the difference is in perception—that is, if your urine doesn’t seem to smell any differently after you eat asparagus, it’s likely that you simply can’t perceive the sulfurous compounds’ foul odor, but there’s a small chance it’s because your body digests asparagus in a way that reduces the concentration of these chemicals in your urine.

It’s still unclear why some people don’t produce the smell, but we do seem to have a clear explanation of why some people don’t perceive it. In 2010, the genetic sequencing company 23andMe conducted a study in which they asked nearly 10,000 customers if they noticed any scent in their urine after eating asparagus, and looked for genetic similarities among those who couldn’t. This peculiarity—which you might consider useful if you eat asparagus frequently—appears to stem from a single genetic mutation, a switched base-pair among a cluster of 50 different genes that code for olfactory receptors.

We’re still waiting for some enterprising team of scientists to attempt gene therapy to convert smellers into non-smellers—but given other priorities to use genetic modification to cure blindness and breast cancer, it seems likely that those suffering from asparagus-scented urine might have to wait a while.

Swedish researchers are developing a system that tests for 12 different drugs on your breath, including cocaine, marijuana and amphetamines. Image via SensAbues

Your breath says a lot about you. Recent research has found that the chemicals present in each person’s breath can provide a unique “breathprint” that differs from person to person, while other scientists have worked on breathalyzer-like tests that can indicate the presence of a bacterial infection inside someone’s body.

In the decades since the 1960s, though, when the first electronic breathalyzer for blood alcohol content was developed, research has led to relatively little advancement in the use of chemical breath analysis for law enforcement purposes. Police have long been able to instantly test a person’s level of alcoholic intoxication by the side of a road, but testing for other drugs has required blood or saliva—substances that are more invasive acquire and that typically have to be sent to a crime lab for processing. Both factors make it difficult to figure out who’s under the influence at, say, the scene of a car accident right after it occurs.

But new research suggests that the status quo might be changing in a hurry. A study published yesterday in the Journal of Breath Research reveals that scientists can now use breath analysis to test for the presence of 12 different drugs in the body, including cocaine, marijuana and amphetamines. Previous work has shown that such technology can reliably test for several of these drugs, and this new study is the first time the drugs alprazolam (commercially known as Xanax, useful for treating anxiety disorders) and benzoylecgonine (a topical pain killer) have been detected. Members of the research group, from Sweden’s Karolinska Institutet, have already created a commercially-available breath testing system, called SensAbues—and it’s easy to speculate that law enforcement across the U.S. (and around the world) would love to get their hands on such technology as soon as possible.

Tiny amounts of microparticles in your breath reflect the substances in your bloodstream—and can be trapped and preserved for analysis. Image via SensAbues

The research team, led by Olof Beck, conducted the new study by testing the breath of 46 individuals who were checked into a drug addiction emergency clinic, had taken drugs about 24 hours earlier and volunteered to participate in the study. Each participant exhaled about 20 deep breaths into the SensAbues filter (which takes 2-3 minutes), and the solid and liquid microparticles suspended in their breath were trapped on a disk for analysis.

These tiny quantities of microparticles reflect the substances in a person’s bloodstream, because small amounts of the molecules from our blood diffuse into the fluid that lines our lungs’ bronchioles and then into our breath. By isolating these particles and analyzing them with liquid chromatography and mass spectrometry, the research team was able to determine the drugs present in each person’s body with a decent level of accuracy.

They compared the results to blood and urine samples taken from each of the participants, as well as their own reports of what drugs they’d taken in the previous 24 hours, and on the whole, the tests performed pretty well—although some progress clearly still needs to be made. All 46 people had reported taking one of the 12 detectable illegal substances, and drugs were detected in the breath of 40 of them (87 percent). Most of the particular drugs detected matched with self-reports and blood tests, but 23 percent of the time, the breath tests also indicated the presence of a drug that hadn’t actually been taken. This level of accuracy was higher than previous studies the team has done, as they’ve slowly refined the system to cut down on false positives and improve the detection rate.

Currently, using the SensAbues system would only allow officials to collect a sample and send it elsewhere for analysis. But the researchers say that advances in the cost and portability of chemical analysis could eventually allow for the same sort of roadside breath testing for drugs that we currently have for alcohol.

Another scientific hurdle is data: Unlike for alcohol, we still don’t know what a given quantity of drug molecules detected on someone’s breath means in terms of how much of the drug is actually in their bloodstream (although an accurate detection of any illegal substance might be all law enforcement officials may be after). We also don’t know how long traces of these drugs remain on a person’s breath, and how quickly they degrade.

If scientists are able to make some progress in figuring this all out, though—and if they can make the testing procedure more accurate—roadside drug tests could become a routine part of law enforcement protocol.

An investing strategy based on the frequency of certain words Google searches, it turns out, might provide sizable profits. Image via Flickr user velorowdy

Google, as many researchers know well, is more than a search engine—it’s a remarkably comprehensive barometer of public opinion and the state of the world at any given time. By using Google Trends, which tracks the frequency particular search terms are entered into Google over time, scientists have found seasonal patterns, for example, in searches for information about mental illnesses and detected a link between searching behavior and a country’s GDP.

A number of people have also had the idea to use these trends to try achieving a more basic desire: making money. Several studies in recent years have looked at the number of times investors searched for particular stock names and symbols and created relatively successful investing strategies based on this data.

A new study published today in Scientific Reports by a team of British researchers, though, harnesses Google Trends data to produce investing strategies in a more nuanced way. Instead of looking at the frequency that the names of stocks or companies were searched, they analyzed a broad range of 98 commonly used words—everything from “unemployment” to “marriage” to “car” to “water”—and simulated investing strategies based on week-by-week changes in the frequencies of each of these words as search terms by American internet users.

A listing of the 98 words used in the study, from most effective at predicting market declines (debt) to least effective (ring). Image via Scientific Reports/Preis et. al.

The changes in the frequency of some of these words, it turns out, are very useful predictors of whether the market as a whole—in this case, the Dow Jones Industrial Average—will go down or up (the Dow is a broad index commonly considered a benchmark of the overall performance of the U.S. stock market).

The strategy was relatively straightforward: The system tracked whether a word such as “debt” increased in search frequency or decreased in search frequency from one week to the next. If the term was suddenly searched much less frequently, the investment simulation bought all the stocks of the Dow on the first Monday afterward, then sold all the stocks one week later, essentially betting that the overall market would rise in value.

If a term such as “debt” was suddenly searched much more frequently, the simulation did the opposite: It bought a “short” position in the Dow, selling all its stocks on the first Monday and then buying them all a week later. The concept of a “short” position like this might seem a bit confusing to some, but the basic thing to remember is that it’s the exact opposite of conventionally buying a stock—if you have a “short” position, you make money when the stock goes down in price, and lose money when it goes up. So for any given term, the system predicted that more frequent searches meant the market as a whole would decline, and less frequent searched meant it would rise.

During the period of time studied (2004-2011), making investment choices based on a few of these words in particular would have yielded overall profits several times higher than a conservative investment strategy of simply buying and holding the stocks of the Dow for the entire time. For example, basing a strategy solely on the search frequency of the word “debt,” which turned out to be the single most profitable term in the study, would have generated a profit of 326% over the seven years studied—compared to a profit of just 16% if you owned all the stocks of the Dow for the whole period.

So if you systematically bought a “short” position in the market every time the word “debt” suddenly started getting searched more often, you’d have made a ton of money over the seven years studied. But what about other words? The system simulated how this strategy would have performed for each of 98 words chosen, listed in the chart at right from most useful at predicting the movement of the markets (debt) to least useful (ring). As seen in the chart, for some of these terms the frequency that we type them into Google seems to serve as a very effective early-warning system for declines in the market.

Stock market declines typically reflect investors’ overall belief that, at any given time, it’s better to sell stock than buy it, and they often happen suddenly, when investors move in a herd to a new position—so the researchers speculate that rises in the terms’ frequencies in search convey a nascent feeling of concern about the market, before it’s expressed via actual transactions. All these searches might also reflect countless investors in an information-gathering phase, seeking to find out as much as they possibly can about an industry or a stock before selling it.

Even beyond the practical investment strategies that this type of analysis might generate, simply looking through the words provides a striking—and oftentimes confusing—window into the collective American psyche. It’s seemingly obvious why a sudden increase in the amount of people searching for the word “debt” might signal overall negative feelings about the market, and would likely precede a drop in stock values, and why “fun” might precede increases in the market. But why do searches for the words “color” and “restaurant” predict declines nearly as accurately as “debt”? Why do “labor” and “train” also predict stock market rises?

We find different pitches attractive because of the body size they signal—and a touch of breathiness is crucial to take the edge off a man’s deep voice. Image via Flickr user linda

Who you’re physically attracted to might seem like a frivolous, random preference. In recent years, though, science has told us that our seemingly arbitrary tastes often reflect unconscious choices that are based upon very relevant biological traits.

In general, we find symmetric faces more attractive, likely because they reflect a healthy underlying genome. Women typically prefer men with more distinctively masculine facial features because they indicate high testosterone levels and physical strength, while men prefer women with exaggerated youthful features, possibly because of the evolutionary advantages a male gets when coupling with a younger mate.

Despite all this research into our visual appearances, though, scientists have done relatively little digging into our auditory preferences when it comes to sexual attraction. Why do we find certain peoples’ voices attractive–and why do we sometimes find other types of voices such a turn-off? Specifically, why do women generally prefer men with deep voices, and men prefer women with higher ones?

At least according to a paper published today in PLOS ONE, the explanation is relatively simple: It’s all about body size. Researchers from University College London found that, at least among a sample of 32 participants, high-pitched female voices females were found to be attractive because they indicated the speaker had a small body. Deep male voices, on the other hand, were judged as more attractive because they conveyed that the speaker had a large frame—but were found to be most attractive when tempered by a touch of “breathiness,” suggesting the speaker had a low level of aggression despite his large size.

The group, led by Yi Xu, figured this out by playing recordings of digitally manipulated voices to the participants. The males in the study heard a computer-generated female voice saying phrases such as “I owe you a yo-yo” in which the voice was manipulated with a number of digital alterations in terms of pitch, formant (the particular peaks and valleys in a sound’s frequency spectrum) and other qualities.

The specific manipulations either conveyed a smaller body size or a larger one, based upon previous research that matched various voice qualities with different body sizes in humans. When asked to rate the voice’s attractiveness on a 1 to 5 scale, the men preferred the voices that suggested a smaller female. Past a certain point, though, higher voices were judged as no more attractive that slightly deeper ones. Listen to the most and least attractive (both, admittedly creepy) voices below:

The female participants’ voice preferences were similar, but slightly more nuanced. On the whole, they preferred deeper voices, which signaled a large body size, but another trait was also crucial: “breathiness.” The researchers hypothesized that this breathiness effectively takes the edge off a voice, making a man with a presumed large frame seem less aggressive and angry. They also polled the participants on whether they thought the simulated voices sounded angry or happy, and the breathy deep males voices were generally perceived as much happier and less angry than the less breathy (i.e. “pressed”) deep ones. Listen to the most and least attractive male voices below:

Beyond explaining the popularity of Barry White, the researchers say these findings correspond to much of what we know about voice preferences in the rest of the animal kingdom. Birds and other mammals, it turns out, have long been known to advertise their physical characteristics via the sound qualities in their mating calls.

All this points to an obvious question, though: Why would males prefer smaller females, and females prefer larger males in the first place? The researchers don’t attempt to address this question, but this duality reflects the sexual dimorphism present in most animal species. These differences generally result from sexual selection giving incentive to different mating strategies—so in this case, our voice preferences suggest that women benefit, in evolutionary terms, by mating with larger, but less aggressive men, while males benefit from mating with smaller females.

As the same time, what we commonly consider attractive varies dramatically over time and location—for example, dozens of prehistoric “Venus figurines,” discovered all over the world, portray extremely voluptuous female figures. So, if we tested the preferences of all humans throughout history, we might find a less obvious trend. This preference for small-voiced females and big-voiced males, then, might simply be an artifact of our contemporary cultural concepts of “attractiveness,” rather than a deep-seated evolutionary choice after all.

Brain scans show that the neurological patterns linked with pangs of empathy for humans also occur when we see a robot like WALL-E treated harshly. Image via Flickr user Rob Boudon

If, while watching WALL-E, your heart broke just a little bit when you saw the title character desperately travel across outer space in search of true love, it doesn’t mean you’re crazy. Sure, WALL-E is a robot. But its cute, anthropomorphized look and all too human desire to end its loneliness made us subconsciously forget that it is not human.

The ability to forget that key point wasn’t just a matter of clever storytelling. New research shows that, at least in a small sample of people tested, the same neural patterns that occur when we feel empathy for a human onscreen are present in our brains when we see a robot onscreen.

A robot is shaken and beat up during the videos viewed as part of the experiment. Image via Rosenthal-von der Pütten et al

A group of researchers from the University of Duisburg Essen in Germany used fMRI (functional magnetic resonance imaging) to come to the finding, tracking blood flow in the brains of 14 study participants when they were shown videos of humans, robots and inanimate objects being treated either affectionately or harshly. The researchers, who will present their findings at the June International Communication Association conference in London, found that when participants were shown videos of a robot (a product called Pleo, which resembles a dinosaur) petted, tickled and fed, areas in their limbic structures—a region of the brain believed to be involved in emotional responses—activated. When they were shown videos of a human getting a massage, the same sorts of neural activity occurred.

The same pattern also occurred when the participants were shown videos of the robots and humans being treated harshly—shaken, dropped or suffocated with a plastic bag—but with a twist. Interestingly, their fMRI results showed levels of limbic activity much greater when they saw humans treated poorly than when they saw the robots. This correlated with the responses on surveys that the participants took after watching the videos, on which they reported some empathy for the robots, but more for the humans.

The results suggest that the reason we feel empathy for robots like WALL-E is that, when we see them treated a certain manner, it triggers the same sort of neural activity as seeing a human treated that way. In a sense, our mind interprets the robot to be human-like in a way that it doesn’t for, say, a rock. On the other hand, one possible explanation for why, despite this pattern, they still arouse less empathy than humans when being treated harshly is that we interpret them as something slightly less than human—something more like a pet.

Of course, this explanation comes with an important caveat: correlation vs. causation. We don’t know for sure that these neurological patterns cause empathy, per se, just that they reliably occur at the same time. (Further, we can’t say for sure that this effect is unique to robots—stuffed animals and dolls might engender the same feelings of empathy.)

Even if the patterns only correlate with empathy, though, they could be an effective objective measure of how much empathy people feel when observing various types of robots—and research into that area has practical implications that go far beyond Hollywood. One of the main areas, the scientists say, is in the engineering of robots that engage with humans on a frequent and long-term basis.

“One goal of current robotics research is to develop robotic companions that establish a long-term relationship with a human user, because robot companions can be useful and beneficial tools. They could assist elderly people in daily tasks and enable them to live longer autonomously in their homes, help disabled people in their environments, or keep patients engaged during the rehabilitation process,” Astrid Rosenthal-von der Pütten, the study’s lead author, said in a press statement. “A common problem is that a new technology is exciting at the beginning, but this effect wears off especially when it comes to tasks like boring and repetitive exercise in rehabilitation. The development and implementation of uniquely humanlike abilities in robots like theory of mind, emotion and empathy is considered to have the potential to solve this dilemma.”

In one previous long-term study, two out of six elderly participants appeared to develop emotional attachments with a companion robot—giving it a name, speaking to it and at times even smiling at it—while the other four did not. Further exploring our empathy for robots and figuring out just which of their characteristics (whether physical, such as having a human-like face, or behavioral, such as smiling or walking on two legs) lead more people to feel for them could help engineers design robotic devices that elicit more empathy over the long-term—and devices that people can readily connect with on an emotional level might make more effective rehab coaches and home companions over the long-term.

By combining genes from different bacteria species, scientists created E. coli that can produce diesel fuel from fat. Image via Marian Littlejohn/PNAS

Over the past few decades, researchers have developed biofuels derived from an remarkable variety of organisms—soybeans, corn, algae, rice and even fungi. Whether synthesized into ethanol or biodiesel, though, all of these fuels suffer from the same limitation: They have to be refined and blended with heavy amounts of conventional, petroleum-based fuels to run in existing engines.

Though this is far from the only current problem with biofuels, a new approach by researchers from the University of Exeter in the UK appears to solve at least this particular issue with one fell swoop. As they write today in an article in Proceedings of the National Academy of Sciences, the team has genetically engineered E. coli bacteria to produce molecules that are interchangeable to the ones in diesel fuels already sold commercially. The products of this bacteria, if generated on a large-scale, could theoretically go directly into the millions of car and truck engines currently running on diesel worldwide—without the need to be blended with petroleum-based diesel.

The group, led by John Love, accomplished the feat by mixing and matching genes from several different bacteria species and inserting them into the E. coli used in the experiment. These genes each code for particular enzymes, so when the genes are inserted into the E. coli, the bacteria gains the ability to synthesize these enzymes. As a result, it also gains the ability to perform the same metabolic reactions that those enzymes perform in each of the donor bacteria species.

By carefully selecting and combining metabolic reactions, the researchers built an artificial chemical pathway piece-by-piece. Through this pathway, the genetically modified E. coli growing and reproducing in a petri dish filled with a high-fat broth were able to absorb fat molecules, convert them into hydrocarbons and excrete them as a waste product.

Hydrocarbons are the basis for all petroleum-based fuels, and the particular molecules they engineered the E. coli to produce are the same ones present in commercial diesel fuels. So far, they’ve only produced tiny quantities of this bacterial biodiesel, but if they were able to grow these bacteria on a massive scale and extract their hydrocarbon products, they’d have a ready-made diesel fuel. Of course, it remains to be seen whether fuel produced in this way will be able to compete in terms of cost with conventional diesel.

Additionally, energy never comes from thin air—and the energy contained within this bacterial fuel mostly originates in the broth of fatty acids that the bacteria are grown on. As a result, depending on the source of these fatty acids, this new fuel could be subject to some of the same criticisms leveled at biofuels currently in production.

For one, there’s the argument that converting food (whether corn, soybeans or other crops) into fuel causes ripple effects in global food market, increasing the volatility of food prices, as a UN study from last year found. Additionally, if the goal of developing new fuels is to fight climate change, many biofuels fall dramatically short, despite their environmentally-friendly image. Using ethanol made from corn (the most widely used biofuel in the U.S.), for example, is likely no better than burning conventional gasoline in terms of carbon emissions, and maybe actually be worse, due to all the energy that goes into growing the crop and processing it info fuel.

Whether this new bacteria-derived diesel suffers from these same problems largely depends upon what sort of fatty acid source is eventually used to grow the bacteria on a commercial scale—whether it would by synthesized from a potential food crop (say, corn or soy oil), or whether it could come from a presently-overlooked energy source. But the new approach already has one major advantage: Just the steps needed to refine other biofuels so they can be used in engines use energy and generate carbon emissions. By skipping these steps, the new bacterial biodiesel could be an energy efficient fuel choice from the start.

New research finds that the superstorm’s massive ocean waves produced seismic activity as far away as Seattle. Image via NASA

If you weren’t on the East Coast during Hurricane Sandy, you likely experienced the disaster through electronic means: TV, radio, the internet or phone calls. As people across the country tracked the storm by listening to information broadcast through electromagnetic waves, a different kind of wave, produced by the storm itself, was traveling beneath their feet.

Keith Koper and Oner Sufri, a pair of geologists at the University of Utah, recently determined that the crashing of massive waves against Long Island, New York and New Jersey—as well as waves hitting each other offshore—generated measurable seismic waves across much of the U.S., as far away as Seattle. As Sufri will explain in presenting the team’s preliminary findings today during the Seismological Society of America‘s annual meeting, they analyzed data from a nationwide network of seismometers to track microseisms, faint tremors that spread through the earth as a result of the storm waves’ force.

The team constructed a video (below) of the readings coming from 428 seismometers over the course of a few days before and after the storm hit. Initially, as it traveled up roughly parallel to the East Coast , readings remained relatively stable. Then, “as the storm turned west-northwest,” Sufri said in a press statement, “the seismometers lit up.” Skip to about 40 seconds into the video to see the most dramatic seismic shift as the storm hooks toward shore:

The microseisms shown in the video differ from the waves generated by earthquakes. The latter arrive suddenly, in distinct waves, while the microseisms that resulted from Sandy arrived continuously over time, more like a subtle background vibration. That makes converting these waves to the moment magnitude scale used to measure earthquakes somewhat complicated, but Koper says that if the energy from these microseisms was compressed into a single wave, it would register as a 2 or 3 on the scale, comparable to a minor earthquake that can be felt by a few people but causes no damage to buildings.

The seismic activity peaked when Sandy changed direction, the researchers say, triggering a sudden increase in the number of waves running into each other offshore. These created massive standing waves, which sent significant amounts of pressure into the seafloor bottom, shaking the ground.

It’s not uncommon for events other than earthquakes to generate seismic waves—Hurricane Katrina produced shaking that was felt in California, landslides are known to have distinct seismic signatures and the meteor that crashed in Russia in February produced waves as well. One of the reasons the readings from Sandy scientifically interesting, though, is the potential that this type of analysis could someday be used to track a storm in real-time, as a supplement to satellite data.

That possibility is enabled by the fact that a seismometer detects seismic motion in three directions: vertical (up-and-down shaking) as well as North-South and East-West movement. So, for example, if most of the shaking detected by a seismometer in one location is oriented North-South, it indicates that the source of the seismic energy (in this case, a storm) is located either North or South of the device, rather than East or West.

A nationwide network of seismometers—such as Earthscope, the system that was used for this research and is currently still being expanded—could eventually provide the capacity to pinpoint the center of a storm. “If you have enough seismometers, you can get enough data to get arrows to point at the source,” Koper said.

Satellites, of course, can already locate a hurricane’s eye and limbs. But locating the energetic center of the storm and combining it with satellite observations of the storm’s extent could eventually enable scientists to measure the energy being released by a hurricane in real-time, as the storm evolves. Currently, the Saffir-Simpson scale is used to quantify hurricanes, but there are several criticisms of it—it’s solely based on wind speed, so it overlooks the overall size of a storm and the amount of precipitation in produces. Including the raw seismic energy released by a storm could be a way of improving future hurricane classification schemes.

The prospect of seismometers (instruments typically used to detect earthquakes) being employed to supplement satellites in tracking storms is also interesting because of a recent trend in the exact opposite direction. Last month, a satellite data was used for the first time to detect an earthquake by picking up extremely low pitched sound waves that traveled from the epicenter through outer space. The fields of meteorology and geology, it seems, are quickly coming together, reflecting the real-world interaction between the Earth and the atmosphere that surrounds it.

The rare coealacanth’s genome is slowly evolving—and contrary to prior speculation, it probably isn’t the common ancestor of all land animals. Image via Wikimedia Commons/Amelia Guo

On December 23, 1938, South African Hendrick Goosen, the captain of the fishing trawler Nerine, found an unusual fish in his net after a day of fishing in the Indian Ocean off of East London. He showed the creature to  local museum curator Marjorie Courtenay-Latimer, who rinsed off a layer of slime and described it as “the most beautiful fish I had ever seen…five foot long, a pale mauvy blue with faint flecks of whitish spots; it had an iridescent silver-blue-green sheen all over. It was covered in hard scales, and it had four limb-like fins and a strange puppy dog tail.”

The duo, it turned out, had made one of the most significant biological discoveries of the 20th century. The fish was a coelacanth, a creature previously known only from fossilized specimens and believed to have gone extinct about 80 million years earlier. Moreover, its prehistoric appearance and unusual leg-like lobed fins immediately suggested to biologists that it could be an ancient ancestor of all land animals—one of the pivotal sea creatures that first crawled onto solid ground and eventually evolved into amphibians, reptiles, birds and mammals.

Now, though, the coelacanth’s full genome has been sequenced for the first time, and the results, published by an international team of researchers today in Nature, suggest otherwise. Genetic analysis suggests that the coelacanth doesn’t appear to be the most recent shared ancestor between sea and land animals—so its lobed fins didn’t make that first fateful step onto land after all.

When the researchers used what they found out about the coelacanth’s genome to build an evolutionary tree of marine and terrestrial animals (below), they found it’s more likely that ancestors of closely-related class of fish called lungfish played this crucial role. The ancestors of coelacanths and lungfish split off from each other before the latter group first colonized any land areas.

The genetic sequencing showed that terrestrial animals share a more recent common ancestor with lungfish, rather than coelacanths. Image via Nature/Amemiya et. al.

Additionally, the coelacanth’s prehistoric appearance has led to it commonly being considered a “living fossil”: a rare, unchanging biological time capsule of a bygone prehistoric era. But the genomic sequencing indicated that the fish species is actually still evolving—just very, very slowly—supporting the recent argument that it’s time to stop calling the fish and other seemingly prehistoric creatures “living fossils.”

“We found that the genes overall are evolving significantly slower than in every other fish and land vertebrate that we looked at,” Jessica Alföldi, a scientist at MIT and Harvard’s Broad Institute and a co-author, said in a press statement. Small segments of the fish’s DNA had previously been sequenced, but now, she said, “This is the first time that we’ve had a big enough gene set to really see that.”

The fact that the fish is evolving isn’t surprising—like all organisms, it lives in a changing world, with continuously fluctuating selection pressures that drive evolution. What’s surprising (though reflected by its seemingly-prehistoric appearance) is that it’s evolving so slowly, compared to a random sampling of other animals. According to the scientists’ analysis of 251 genes in the fish’s genome, it evolved with an average rate of 0.89 base-pair substitutions for any given site, compared to 1.09 for a chicken and 1.21 for a variety of mammals (base-pair substitution refers to the frequency with with DNA base-pairs—the building blocks of genes—are altered over time).

The research team speculates that the coelacanth’s extremely stable deep Indian Ocean environment and relative lack of predators might explain why it has undergone such slow evolutionary changes. Without new evolutionary pressures that might result from either of these factors, the coelacanth’s genome and outward appearance have only changed slightly in the roughly 400 million years since it first appeared on the planet.

A new study shows that fathers and mothers are equally skilled at picking out their infant’s unique cry—if they spend the same amount of their time parenting. Image via Wikimedia Commons/Voiceboks

After a baby orangutan is born, it’ll spend the first two years of its life completely dependent on its mother—maintaining direct physical contact with her for at least the first four months—and breastfeeding for up to five years in total. During that time, it will likely never meet its father. Polar bears are also born helpless, surviving on their mothers’ milk through the harsh Arctic winter, but polar bear fathers provide no parenting, and have even been known to eat their cubs on occasion if they get the chance.

Both of these facts reflect a pattern common across the animal kingdom: In most species, mothers are inherently much more involved in parenting than fathers, and evolution has driven them to develop parenting instincts that are absent in their male counterparts.

A new experiment, though, suggests that contrary to conventional wisdom, one animal species remains a pretty significant exception to this rule: humans. It’s often believed that nobody can recognize a baby’s cry as accurately as his or her mother, but a study published today in Nature Communications by a team of French scientists led by Erik Gustafsson of the University de Saint-Etienne found that fathers can do it equally well—if they spend as much time with their offspring as mothers do.

The study involved 29 babies from France and the Democratic Republic of Congo, all less than half a year old, along with each of their mothers and 27 of their fathers (2 could not be located for the study). The researchers recorded the cries these infants made while being bathed, and then played them back to their parents (along with the cries of other babies) later on. To this non-parenting bystander, the cries (published along with the paper) generally seem pretty similar—like the one below, they all sound, well, like a quintessential baby’s cry:

In one of those astounding feats of parenthood, though, the parents did way better than chance in identifying which of the seemingly-identical cries belonged to their child from the sound alone. Each parent heard a random sequence of 30 different cries (24 from 8 other babies, and 6 from their own), and on average, they correctly identified 5.4 of their baby’s cries, while making 4.1 false-positives (incorrectly identifying another infant’s cry as their child’s). Although having this skill doesn’t necessarily indicate that a parent provides expert care, it does reflect a remarkably well-attuned connection between parent and infant.

When the researchers split the data along gender lines, they found something interesting. The factor that best predicted which parents were best at identifying their child’s cries was the amount of time the parent spent with their babies, regardless of if they were the mother or father.

Of the 14 fathers who spent an average of 4 or more hours a day with their babies, 13 correctly identified 98% of their total cries (and the outlier still got 90% right). The 29 mothers who spent a comparable amount of time with their children (that is, all the mothers in the study) got the same 98% correct. The remaining 13 fathers who spent less than 4 hours a day with their kids, though, were only able to identify 75% of the cries correctly.

The finding might not seem particularly surprising—of course whichever parents spend the most time with their children will be best at identifying the nuances of his or her pitch—but it cuts against the grain of previous research on this topic, which found that mothers seemed to be naturally better than fathers at identifying their own infants’ cries. (People often make the same assumption, the researchers say—in an informal survey they took of 531 students at the University de Saint-Etienne, 43% felt mothers were better, and the rest thought fathers and mothers were equally good at identifying their baby’s cries, while none felt fathers were.) But previous studies didn’t take into account the amount of time parents typically spent with their children on a daily basis.

The results suggest that experience and learning may be more critical to good parenting than innate skills. Far from being inherently disadvantaged in recognizing their babies’ cries, males who spent lots of time parenting turned out to be just as good as females at the task—so in terms of this particular skill, at least, parenting is less an inherent talent than a one to be practiced and developed. This also implies that whoever is the primary caregivers for a baby—whether grandparents, aunts, uncles or people unrelated to the child—may develop the same ability to distinguish the cries of the child in their care from other children.

Of course, while the findings don’t depict any innate asymmetry in parenting skills between the sexes, they do reveal an enormous asymmetry in the behavior of parents regardless of their continent, predicated on traditional gender roles. Every mother participating in the study spent enough time with their kids to develop the skill tested, while just about half of the fathers did—and two fathers couldn’t even be located to participate in the study in the first place.

Fathers might have the same innate parenting skills as mothers, but only if they make the enormous time investment necessary. This study indicates that it’s usually not the case, and though its sample size was extremely limited, broader data sets show the same. According to the most recent Pew Research data on parenting, the average American mother spends 14 hours per week in child care duties, compared to just 7 hours for the average father—so while men can develop the ability to know their babies just as well as women, most fathers out there probably haven’t so far.

New research shows just a sip of beer can cause a rush of pleasure due to the potent neurotransmitter dopamine. Image via Flickr user Mr. T in DC

If you take just a sip of beer, and moments later—before you’ve had close to enough alcohol to get intoxicated, perhaps even before the beer has hit your stomach—feel a distinctly pleasurable sensation, it might not be strictly due to subtle aromas that result from the beverage’s blend of malt, hops and yeast. The cause of your pleasure might be due to tangible changes in your brain chemistry—specifically, a surge in levels of the neurotransmitter dopamine.

Scientists have long known that part of the reason alcohol induces pleasure is that intoxication leads to the release of dopamine, which is associated with the use of other drugs (as well as sleep and sex) and acts as a reward for the brain. But new research suggests that, for some people, intoxication isn’t necessary: Simply the taste of beer alone can provoke a release of the neurotransmitter within minutes.

A group of researchers led by David Kareken of Indiana University came to the finding, published today in the journal Neuropsychopharmacology, by giving tiny amounts of beer to 49 adult men and tracking changes in their brain chemistry with a positron emission tomography (PET) scanner, which measures levels of various molecules in the brain. They chose participants with varying levels of typical alcohol consumption—from heavy drinkers to near-teetotalers—and even tested them with the beer they reported that they drank most frequently. Because they used an automated system to spray just 15 milliliters (about half an ounce) of beer on each participant’s tongue over the course of 15 minutes, they could be sure that any changes in brain chemistry wouldn’t be due to intoxication.

The effect was significant. When the men tasted the beer, their brains released much higher levels of dopamine within minutes, compared to when the same test was conducted on the subjects at other times with both water and Gatorade. They were also asked to rate how much they “craved” a beer at several points during the experiment, and perhaps less surprisingly, their cravings were generally much higher after tasting beer than Gatorade or water.

Interestingly, the amount of dopamine release per person wasn’t random. People who had a family history of alcoholism (as reported on a survey) showed notably higher dopamine levels after tasting beer as compared to others. But participants who were heavy drinkers but didn’t have the family history had merely average dopamine levels.

The researchers believe this could be a clue as to why some people are predisposed towards alcoholism—and why it’s more difficult for them stay on the wagon if they’re trying to quit. The immediate release of dopamine from just a taste of beer would likely serve as a powerful mechanism that drives their cravings, and a tendency towards experiencing this burst of pleasure might be genetically inheritable. This could be part of the reason that people with a family history of alcoholism are twice as likely to experience alcoholism themselves.

Previous work has shown that in people with alcoholic tendencies, stimuli that are merely associated with drinking (such as the smell and sight of a alcoholic drinks or a bar) can trigger dopamine release in the brain. This work shows that for an unlucky group predisposed to suffering from alcoholism, bursts of dopamine can occur even if they’re not heavy drinkers—and it only takes a sip for the pattern to start.

Humid air causes hydrogen bonds to form between the proteins in your hair, triggering curls and frizz. Image via Flickr user Simon Gotz

If you have long hair, you probably don’t need to look up a weather report to get an idea of how much humidity’s in the air: You can simply grab a fistful of hair and see how it feels. Human hair is extremely sensitive to humidity—so much that some hygrometers (devices that indicate humidity) use a hair as the measuring mechanism, because it changes in length based on the amount of moisture in the air.

Straight hair goes wavy. If you have curly hair, humidity turns it frizzy or even curlier. Taming the frizz has become a mega industry, with different hair smoothing serums promising to “transform” and nourish hair “without weighing hair down.” But just why does humidity have this strange effect on human hair?

Bundles of keratin proteins (the middle layer of black dots above) are susceptible to changing shape on a humid day. Image from Gray’s Anatomy

Hair’s chemical structure, it turns out, makes it unusually susceptible to changes in the amount of hydrogen present in the air, which is directly linked to humidity. Most of a hair’s bulk is made up of bundles of long keratin proteins, represented as the middle layer of black dots tightly packed together in the cross-section at right.

These keratin proteins can be chemically bonded together in two different ways. Molecules on neighboring keratin strands can form a disulfide bond, in which two sulfur atoms are covalently bonded together. This type of bond is permanent—it’s responsible for the hair’s strength—and isn’t affected by the level of humidity in the air.

But the other type of connection that can form between adjacent keratin proteins, a hydrogen bond, is much weaker and temporary, with hydrogen bonds breaking and new ones forming each time your hair gets wet and dries again. (This is the reason why, if your hair dries in one shape, it tends to remain in roughly that same shape over time.)

Hydrogen bonds occur when molecules on neighboring keratin strands each form a weak attraction with the same water molecule, thereby indirectly bonding the two keratin proteins together. Because humid air has much higher numbers of water molecules than dry air, a given strand of hair can form much higher numbers of hydrogen bonds on a humid day. When many such bonds are formed between the keratin proteins in a strand of hair, it causes the hair to fold back on itself at the molecular level at a greater rate.

On the macro level, this means that naturally curly hair as a whole becomes curlier or frizzier due to humidity. As an analogy, imagine the metal coil of a spring. If you straighten and dry your hair, it’ll be like the metal spring, completely straightened out into a rod. But if it’s a humid day, and your hair is prone to curling, water molecules will steadily be absorbed and incorporated into hydrogen bonds, inevitably pulling the metal rod back into a coiled shape.