Uncomfortable Science

I accidentally read this, and now I have to respond to it.

Sigh.

I don't think it behooves me to get too into the generation war right here and now—but listen, not to point any fingers, but I'm not the one who titled her piece “The Greatest Generation of Scientists,”--but this is such a typical baby boomer op-ed. Why can't things be like the good old days, when we were young? Everything was great then. Some times were tough but folks were kind. Today's kids have it so easy, but everything is awful, because of Instagram, and Obama.

I always get fired up about these things, as a card-carrying Millennial. (Friend me on Facebook. You'll see.) But now I'm fired up as a scientist, too.

In case you decided to spare yourself the trouble of reading a New York Times opinion piece (in which case your self-restraint is admirable), here is the latest version of good-old-days mourning: The last of the great twentieth-century scientists, the ones who worked out the foundations of our modern understanding of molecular genetics, are dying, and along with them, a period of scientific growth and comradery that will never be recaptured. In the Good Old Days, scientists whiled away long hours hunched over instruments they'd built themselves from parts they'd barely scraped up the funds for, doggedly, giddily, chasing the secrets of life. They spent their off-hours brainstorming together in smoky bars, because science was tough but they were in it together, man. Today's young scientists don't understand that struggle because the government doles out grants and fancy new equipment to pretty much any Tom, Dick, or Sally, and everyone is so competitive and nobody cares about science anymore. (Implied, too: there isn't that much real science left to be done. DNA and RNA are the most fundamentally important questions, the great white men of the twentieth century solved that, and everything else is icing.)

Sigh.

(By the way—here I hope to preserve some tact by pointing out that last week's piece began as a response to the specific and recent loss of Alexander Rich and that I have no bones about mourning him individually; he was a great scientist who made important contributions and will absolutely be missed. But I don't think it's too insensitive to point out the generalizations that inflate the rest of the piece.)

First of all, Nocera's rosy view of science history is plainly revisionist. It might've seemed collaborative viewed from within the insiders' circle, but it was a boys' club through and through. Or a “gentlemen's club,” as read in the very first sentence of the Wikipedia article on the RNA Tie Club so wistfully recalled in the Times piece. White guys, every member.

I will elide a discussion of the systemic and cultural biases that kept many women and people of color out of science in those days and instead list a small (in no sense exhaustive) subset of women who, despite all that, made important contributions to molecular genetics in the twentieth century: Dorothy Hodgkin (1964 Nobel Prize for inventing X-ray crystallography), Kathleen Lonsdale (contributions to crystallography), Maxine Singer (contributions to interpreting the genetic code), Barbara McClintock (1983 Nobel Prize for discovering genetic transposition),  and of course, Rosalind Franklin (worked on DNA structure; produced Photo 51, the most important piece of evidence indicating it was a double-helix). They were there, they were just rarely invited to the parties.

And surely Rosalind Franklin would've had something to say about the delusion that scientists just talked freely about their ideas without the fear of being scooped. Watson and Crick practically stole Photo 51 from her—they got it without her knowledge or permission from her supervisor Maurice Wilkins, who regularly talked them through their intellectual obstacles, always regurgitating Franklin's ideas. (It was she, for example, who suggested the base pairs go on the inside of the double helix instead of the sugars and phosphates, although this was quite obvious to any chemist, and Watson and Crick didn't even heed that advice at first anyway.)

Another misconception: that science is somehow easier today than it was half a century ago. This is apparently partially because we have fancier machinery. Okay. As Obama said in that 2012 debate, our army also has fewer horses and bayonets. The instruments of science have evolved with the questions we ask. It's a good thing! Maybe you can practically get a pre-fab crystallography setup on ebay in 2015, but now if you really want to look at DNA you can also get an electron microscope. That produces a much clearer image of the DNA itself and how it interacts with other molecules. Lamenting this kind of progress is misguided nostalgia.

Let's also dispel the rumor that today's researchers are fat with funding from the federal government, which is so wrong as to be insulting. I don't know if Nocera's use of the words “hand out” here was intentional. But in case you haven't talked to many scientists lately, funding for basic research is through the floor in the last five years. I know of handfuls of professors at top institutions with long and established track records who're now struggling to keep their labs afloat. Look up any agency and you'll find the funding rate for research grants is plummeting. Your kid has a better shot at getting into Yale than my bosses do of getting money from Uncle Sam.

But let me end on a lighter note—today's scientists are f*#@&ng awesome. You only have to browse the #girlswithtoys hashtag that's been trending on Twitter this week to see all the things Nocera seems to think have gone from the science world—young investigators showing off their tools, eagerly asking the next big questions, Retweeting each other's pictures in a show of support and comradery. And despite the grim funding climate and the infinitesimal ratio of faculty job openings to PhD grads, many of these scientists are sticking it out, determined to make their careers solving the big mysteries of our time. That's good news.

Hey, internet, cool news. The PLOS blog network student blog (aptly named The Student Blog, don’t look at me, you know I’d’ve come up with something catchier, but it does the job anyway) is relaunching, and I’m joining their team of student bloggers! 

My first post won’t be for a few weeks, but you should all start reading The Student Blog now for lots of great writing on science, science education, and all kinds of other science-related issues, from the undergrad and grad student perspective. New pieces are posted on Fridays (and soon on Tuesdays, too).

A team of researchers at MIT recently showed that it's possible to wirelessly stimulate targeted brain regions, potentially expanding treatment options for disorders of brain function.

Deep-brain stimulation is a neuropsychiatric technique where neural activity is induced in targeted brain areas, usually to relieve symptoms of Parkinson's or, more experimentally, major depression. The current state of the art relies on macroscopic devices that have to be surgically implanted in patients' brains and powered by pulse generators implanted elsewhere (usually near the collarbone). But Ritchie Chen and colleagues have taken the first steps toward a new method using magnetic nanoparticles (MNPs), which can be injected in solution and might remain in the brain for months.

Magnetic nanoparticles are tiny (5-500 nanometers, where a human hair measures around 75,000 nanometers) molecules that interact with magnetic fields. They have a variety of potential medical applications, since they can easily be dissolved in a solution and injected into targeted tissues or regions like little sleeper agents, and the magnetic fields that activate them easily penetrate the body without doing any damage. Magnetic hyperthermia, for example, is a cancer treatment that takes advantage of MNPs' tendency to generate heat in certain magnetic environments, cooking tumor tissue to irreparable damage. In their recent study, Chen et al used magnetothermal excitation to trigger heat-sensitive neural receptors, inducing the same neural effects as old-school deep-brain stimulation without the hassle of invasive implants and external power devices.

The new technique hijacks a protein called TRPV1, which is one of many different molecular gate-keepers that sit embedded in neurons' cell membranes, regulating neural activity by controlling the calcium ions (Ca2+) coming into the cell. Calcium buildup makes the neuron fire, so, if you open the gate, you let in more Ca2+, and you activate the neuron. Under normal circumstances, TRPV1 opens when a molecule called capsaicin comes along and unlocks it. (Other types of receptors need different molecular keys.) But TRPV1 also opens at high temperatures.

To that end, Chen et al use the MNP iron(II,III) oxide (Fe3O4) to warm up the brain—not enough to damage the tissue, but just enough to open the TRPV1 gates. This happens by a process called hysteretic power loss. Basically, when they're exposed to a magnetic field that changes direction at a particular frequency, Fe3O4 molecules spin like wobbly tops. As they slow down, they lose energy, and this energy is released as heat.

So, just to recap the chain of events: you put Fe3O4 in the brain, you put the brain in a magnet, the brain gets warmer, TRPV1 gates open up, Ca2+ enters neurons, neurons fire.

The researchers showed that this actually works in the mouse brain. They injected a solution of Fe3O4 into a specific region, the ventral tegmental area, or VTA. (And it's worth noting that it took some additional chemical sleight of hand to get those molecules into a solution that would be stable and compatible with the chemical environment in the brain). They exposed the mouse to the prescribed magnetic fields using a machine much like a typical lab or hospital MRI. (That M stands for magnetic.) Then they showed that both the temperature and the amount of neural activity in VTA increased as expected. They also showed that the injection of Fe3O4 did less damage to the brain tissue than implanting a traditional deep-brain stimulation device. And, they got the same effects in the magnetic field a month after the initial injection, suggesting that the MNPs can hang around the brain for at least that long.

This is a huge step forward for potential psychiatric treatments... Also, remote-control brains? It's just cool. But along with the usual caveat about how long it takes to perfect, test, approve, and implement new treatments, it comes with certain inconveniences. Probably the biggest one is it requires specialized machinery to generate the appropriate magnetic field conditions—as far as I can tell, manipulating MNPs generally requires extremely strong magnetic fields, which means it takes a lot of bulky, powerful hardware—think of the MRI machine again. Parkinson's patients would basically have to live in there. And in treatment of major depression, stimulation is often controlled by the users as needed, so they'd have to drive to a lab or a clinic every time they needed a dose.

Plus, scientists don't know exactly why deep-brain stimulation works, when it works. Treatments do stimulate regions that are known to malfunction in disease, but these regions are just isolated pieces of complicated neural circuits distributed throughout the brain.  At the same time, taking a neuron-level perspective, DBS is quite broad—there are local circuits within each brain region, and many different types of neurons playing different roles.  It's basically poking the brain with a big stick; clinicians calibrate the pokes to get the best effects, but it's not yet clear what's really going on inside. (Though, to be fair, it is also unclear exactly why SSRI's work for depression. The chemical mechanism is known, but that plays out on a much faster time scale than the actual relief of symptoms. And though the chemical effects are the same in everyone, the amount of actual relief varies widely from patient to patient.)

I've also seen a bit of evidence suggesting that at least some of the benefits of deep-brain stimulation for Parkinson's patients slowly decline in the long term. I haven't done a thorough search of the literature, but this makes sense given that the brain does tend to adapt to certain kinds of inputs over time—that's how people build tolerance to drugs, or develop chemical addictions.

Still, this study paves the way for future research, and it's a very elegant illustration of a way magnetothermal properties can be finessed into gentle biomedical treatments, rather than just frying the crap out of cancerous tumors.

Try designing a plane. It has only one mission—but also only one shot—to get from its starting point somewhere in Canada to a small forest in Mexico. It'll need a sophisticated navigational system, since there's no pilot (not in the cockpit, and not in some Air Force base in Nevada, thanks very much). Oh, but the whole thing has to weigh less than two ounces, and the whole computer system—navigation and control—has to be roughly the size of the head of a pin.

That's the monarch butterfly.

Every fall, eastern North American monarchs migrate from their birthplaces in Canada and the northeastern U.S. to their overwintering grounds in Mexico. Just the distance of their commute—around 4,000 km, or 2,500 miles—is Herculean for an insect. But they also manage not to get lost during their journey, and to converge finally on a relatively precise endpoint—the mountains of Michoacán, Mexico, within an area of only about 800 square km (300 square miles). Experiments with migratory monarchs have shown that they take advantage of an array of available environmental cues, including the position of the sun and the angle of the Earth's magnetic field. But scientists are still working out a complete understanding of the suite of chemical machinery that allows the monarch to sense these signals and use them to direct their flight.

The migratory monarch's time-compensated sun compass ensures that it's always flying south. It's guided by the position of the sun in the sky, combined with an internal clock. That's the time-compensation part, which is critical—if you're going south in the morning, you want the sun on your left, but if it's afternoon, you want it to your right. So you have to calibrate your bearing according to the time of day. But monarchs don't have watches—their circadian savvy resides in the antennae, in a chemical cascade called a negative transcriptional feedback loop.

Basically, in the cells of the antennae, a few specialized molecules—proteins called CLOCK and CYCLE—hang out around the DNA to green-light the transcription of genes called period, timeless, and chryptochrome. These genes represent instructions for building proteins (PER, TIM, and CRY2) and are always present in the DNA, but they lie dormant unless CLOCK and CYCLE are there telling the cell to actually use them. With about a 12-hour delay, CRY2 begins to inhibit CLOCK and CYCLE, until it's used up, and then the CLOCK and CYCLE populations bounce back and signal the transcription of more CRY2, PER, and TIM—etc, etc, and thus a cyclical rise and fall of the concentration of these circadian proteins in the cells. (Side note, no molecules' names have been changed in this article. Those are all real. You can thank the biologists for that.)

There's one more important part of the circadian clock—light sensitivity. Because there's some randomness in the length of the spontaneous chemical rhythms, and because the lengths of the days change with the seasons, it's important that the sun be able to synchronize and reset the monarch's internal clock when necessary.

There's a very similar clock mechanism in the monarch brain, but we know the antennae are crucial because experiments show they can't navigate properly when the antennae are removed or painted black (to hinder their light sensitivity). In fact, the antennal clocks continue to run on their own even after they've been removed, which suggests they're independent from the brain clocks.

(A few brief asides about circadian rhythms: (1) I'm told that much of this process is conserved across evolution. The monarch's two types of chryptochrome proteins are the same ones used by mammals—including humans. (2) This is just one of many, many cool examples of epigenetics—changes in cells that aren't caused by changes in DNA, but rather by changes in the way the cell does or doesn't read out parts of the DNA. It's a fascinating subject that I might write more about here in the future, but you should Google it now anyway. It helps explain the variety of actual functions that arise from an essentially constant strand of DNA—how organisms mature over their lifetimes, and why even people with the exact same DNA aren't literally carbon copies of one another. (3) Excitation (CLOCK and CYCLE stimulating CRY2 production) + inhibition (CRY2 blocking CLOCK and CYCLE) + time delay is a pretty standard recipe for biological oscillations. You find it in the brain a lot, at various temporal and spatial scales.)

Unfortunately for migrants, skies aren't always blue in the fall. When it's overcast, it's hard to tell where the sun is (especially when you have compound eyes like the monarch's, which are great for seeing motion but lousy at resolving details). So a sun-compass isn't enough. As a supplement, monarchs (as well as other insects and migratory birds) can also use the Earth's magnetic field. Like their internal clocks, their magnetic compass fails when the antennae are painted black, so we know it also relies on some light-sensitive reaction happening there. The mechanism for magnetoreception is much less well understood than the sun-compass, but one hypothesis is that it has something to do with radical pairs—an explanation of which involves a quick dip into quantum mechanics (so get excited!).

Brief background info—molecules are made of atoms; most atoms have electrons; electrons carry negative electric charge. They also, like other elementary particles, have a property called spin, which is complicated, but the significance here is that electrons actually come in two flavors—spin up and spin down—pairs of which fall under the opposites-attract heuristic of physics. So, generally, electrons within a molecule prefer to hang out in pairs. But some molecules have odd numbers of electrons, meaning one of them is floating around the molecule all by itself—those molecules are called radicals. A pair of radicals can thus take two forms—S (singlet), when both lone electrons have the same spin, and T (triplet), when they have opposite spins. Finally, because electricity and magnetism are intimately related, a lone electron also carries its own tiny magnetic field.

Got it? Okay. So, one form of the radical-pairs hypothesis proposes that when exposed to the right wavelengths of light, cryptochrome can harness that light energy to form a pair of radicals, which would take either the S or the T state, with a certain probability. Because electron spin interacts with magnetic fields, a weak magnetic field—like the Earth's—could affect some of the radical pairs, turning them from S to T (or T to S), depending on its strength and direction. In other words, the S-to-T ratio would give you information about the direction of the magnetic field.

Getting back to monarchs, it's not known whether or how they'd be able to sense that information—speculative models involve subsequent chemical reactions that only occur with S (or T) pairs, that eventually interface with some sensory mechanism downstream, but nothing's been proven. What is pretty certain is that monarchs, as well as fruit flies and some birds, definitely use light-dependent magnetic compasses for navigation. Further, it probably has something to do with cryptochrome, since the monarch's CRY1 and CRY2 both help to restore magnetoreception in CRY-deficient fruit flies.

The similarity of the monarch's navigation tools to those of other migrant animals (insects, birds, and even sea turtles) make it a great candidate model organism, one that scientists could focus on with the aim of understanding long-distance migration in general. The Monarch Genome Project is well underway, which should make many more experiments possible. For example, the genome could be systematically manipulated to create butterflies deficient in some specific protein, which would help to illuminate the particular function of that protein.

Despite both their promise as a model organism and their beauty and popularity with more casual human observers, monarchs are among the many species affected by climate change and industrialization. The World Wildlife Foundation classifies them as “Near Threatened,” which is actually their second least urgent category, but the population has declined by 90 percent since the mid-1990's. This is largely due to habitat depletion; female monarchs only lay their eggs on milkweed plants, whose numbers have been reduced by 60 percent in the Midwest alone.

Some references:

1. Most of the information in this post I learned from this review article from Trends in Neurosciences. It's from Steven Reppert's lab, which is one of the leaders in research on this topic. And he opens that review with a Doors lyric, for whatever that's worth to you. (Also, surprise! this scholarly article is not pay-walled!)

2. This NYT article from a few days ago has more information about monarch conservation and the depletion of milkweed. These days I like to roll my eyes and huff "Old media" whenever I read a Times article. This one was actually worth the extra time it took to perform that ritual, though. 

Yeah, no, I don’t know about this either, you guys. (Fair warning: this post does touch on The Life In Science, as it were, but there’s very little actual science here...)

Oh, man. You know those songs we listened to so much in high school that just hearing them actually makes us feel sixteen again? A lot of people were probably into something really cool, like, you-probably-never-heard-of-them cool. My list is a short one including mostly albums you have definitely heard and probably rolled your eyes at. One of them is 2005's From Under The Cork Tree.

Yeah... Fall Out Boy are my Madeleines...

Anyway. From Under The Cork Tree was probably the album where Fall Out Boy “sold out” or “became so mainstream,” but I shuffled into some of the tracks on my iPod recently and, scoff all you want at its maybe overstated place in the expanding pop-punk landscape of the day, it was—still is—a fun album to listen to, and cathartic in its relatability.

There are some smart musical elements I'm probably wholly unqualified to discuss, except to say that most of the songs are just catchy as hell. But the lyrics—once you de-noise Patrick Stump's emo-slurred delivery—are why I got so into Fall Out Boy the first time around (probably? I don't really have that many memories from high school; I was really barely even sentient at that stage). And I wasn't let down on this recent listen, either. Bassist/lyricist Pete Wentz twisted cliches and turned some phrases that I continue to find clever even this far beyond my larval teenage phase. And the metaphors are versatile enough that even the songs that are “really” (purportedly) about heavy grownup stuff, like the lyricist's struggle with depression and attempted suicide, translated easily to the libidinous angst of high school. Not a huge jump, then, from one world of petty adolescent jealousies/delusions of grandeur/hero-worship to another: academia. (“[T]he cure to growing older,”[1] indeed.) After the critical success of Take This To Your Grave (2003), Fall Out Boy’s sophomore album grapples tangibly with—even obsesses over—the band's insecurity in its fame, and this manifests in some particular ways that ring so true in the academic world, it's almost hard for me to believe Wentz himself wasn't secretly a PhD student at some point.

From the first track, the lyrics are bitterly self-deprecating. “Brothers and sisters, put this record down” [2]. They’re convinced they’re over-hyped, you shouldn’t like them, but you do, and they’re rolling with it.  “We're only liars, but we're the best”[2]. Lying—compulsively, destructively, sometimes restlessly—becomes a motif throughout the album, coming to a head in “Sophomore Slump Or Comeback Of The Year”—”I'll keep singing this lie if you'll keep believing it”[3]—where it snowballs (in gloriously melodramatic emo-punk fashion) into a full-blown crisis of authenticity.

In fact, the paranoia of “Sophomore Slump…” makes it practically a grad school anthem. The fear that you aren't as smart as people seem to think you are (”It's just a matter of time until we're all found out”[3]), that your only real talent is tricking them into liking you (“We're the chemists who've found the formula/To make your heart swell and burst”[3]) is so pervasive in PhD culture, there's a name for it—imposter syndrome.

Let me stop and say a little more about imposter syndrome, because it’s an affliction on which I am especially qualified to comment. (Not because I have it, but because I am an actual imposter. Meaning I live with the exact same symptoms, except I'm not delusional; I'm just self-aware.)

Imposter syndrome is a specific failure of parsimony wherein you persist in explaining all of your apparent successes as the confluence of many random factors—coincidence, dumb luck, someone else's mistaken evaluation of you—rather than the much simpler story that you are actually smart. Since you aren't actually smart, you must constantly fake it (hence, “imposter”). To avoid being found out, you must vigilantly monitor everyone else’s mental states. Models of others' motivations and intentions become superfluously complex—especially when they’re complimenting your work. Compliments are probably somebody's sneaky, ironic new way of messing with you because they know. Or else they’re a last-ditch attempt to motivate you, because maybe some positive reinforcement is what you need to stop being awful and actually produce good work. In any case, no compliment can be attributed to true admiration. These models are of course penalized for all the extra parameters, but even then they're the only ones that fit, once you reject outright the absurd hypothesis that you are in fact a competent, intelligent individual.

Wentz has described From Under The Cork Tree as an introspective album, probing “the anxiety and depression that goes along with looking at your own life”[4]. This is stated like an accomplishment, or at least a kind of milestone, in his words, “part of growing up”[4].  

In vision science, where we study the tenuous links between things in the world and our visual experiences of them, we’re taught that introspection is one of the least reliable methods for accessing people’s perceptions. Simply asking an observer what she saw (“Adjust the brightness of the stimuli until they look indistinguishable to you.”) allows for results subject to bias, fluctuating attitudes and motivations, uncertainty--nearly endless sources of noise. You have to trick the observer into providing the cleanest possible data set through careful experimental design, with systematically varied stimulus parameters and seemingly straightforward instructions (“For each pair of stimuli, please indicate which one is brighter.”) You can’t just ask an observer to evaluate her own subjective experience.

No consistent logical system can prove its own consistency--Godel’s second incompleteness theorem[5]. Loosely translated, a sensible mathematical language cannot describe itself entirely. It cannot contain a statement of its own consistency unless it is in fact inconsistent. This sentence is a lie. “We’re only liars, but we’re the best.”[2] “I’ll keep singing this lie if you’ll keep believing it.”[3]

Among academics, imposter syndrome is so common it’s pretty much a giant cliché. (Though even knowing that doesn’t make you immune.) Maybe that’s just what happens when our tendency to inspect and analyze turns inward. Which it does, often, since feedback from advisers and mentors can be both seldom and, shall we say, oblique, maybe? But let’s not pretend academics are such special snowflakes: theoretically, anyone is susceptible to imposter syndrome, especially people who’ve suddenly achieved a goal they’ve spent a while working for--a few PubMed citations, a prom date, a record deal. There’s always at least that one moment when you wonder, How did I even get here? Was it just by chance? What are people going to expect of me now?

So we’re back to From Under The Cork Tree. (“Is this more than you bargained for yet?”[6]) What else can I tell you? It worked for me in 2006; it works for me now. Maybe because it captures the kind of angst that, although it might change form from time to time, we never really grow out of. (Look, I’m hoping I’m wrong about that, too, but I’m trying to make peace with it.)

Or maybe I just actually stopped aging at sixteen.

You can listen to From Under The Cork Tree on Spotify: https://play.spotify.com/album/0L0mzP8RGm5SNlwgZfBuxW

Specific songs/other sources I referred to:

1. I Slept With Someone In Fall Out Boy And All I Got Was This Stupid Song Written About Me

2. Our Lawyer Made Us Change The Name Of This Song So We Wouldn't Get Sued

3. Sophomore Slump Or Comeback Of The Year

4. Cripps, Charlotte (April 28, 2006). "Last year, Fall Out Boy's bassist Pete Wentz attempted suicide; now the band are punk-pop gods". The Independent (Independent Print Limited).

5. http://en.wikipedia.org/wiki/G%C3%B6del%27s_incompleteness_theorems

Elizabeth Cutrone on the shocking intelligence of slime mold.

Here's the piece I wrote for The Toast, which I was hinting at a while ago.

HEY SO after a totally planned and foreseen "summer hiatus," I am back to existing in the blog-world. I can hear your cries of resounding joy from here.

More reasons to get a good night's sleep after your Late Beginner Adult Ballet 1 class—it's not just a rest for your muscles, it's a chance for your brain to keep practicing.

Slow-wave sleep (SWS; the last stage of the sleep cycle before REM) is already known to be important for the consolidation of memories, including motor memories. One mechanism for this might be enhanced activity of motor control areas in the brain during SWS, which has been shown to correlate with better learning of motor skills. A new Nature Neuroscience paper suggests that this is not just a diffuse increase in motor cortex activity, but offline reactivation of the specific activity patterns involved in performing a newly learned task. The study is also notable for being one of the first (at least, the first I've seen...) to use a brain-machine interface (BMI) as a means to a scientific end other than just the development of the devices themselves.

A BMI is any of a wide variety of devices that can be stuck in the brain to read out neural activity and use it to control things like computer cursors or artificial limbs. In this study, an array of tiny electrodes recorded spikes—little electrical pulses that are the basic units of neural computation—produced by a small population of neurons in the rat primary motor cortex (M1). A computer then changed the position of a small feeding tube based on the activity of a preselected set of neurons. With some practice, the rats learned to increase the firing rate of the task-related (TR) neurons, moving the tube into the correct position and triggering the delivery of a small reward.

(I'll just pause and let you absorb this, in case you're new to the idea of BMIs. This is a study involving rats controlling machines with their brains. And that's not even the main finding. The future is now, you guys.)

There's a huge benefit to using a BMI to study motor learning—not only can you measure the activity at a very fine spatial scale, you can also define a task with respect to a particular, known, set of neurons. That makes it much easier to determine whether subsequent changes in neural activity are reflecting a general boost to the motor area, or are specific to the processing of the newly learned task (i.e., the TR neurons).

The interesting findings in this study deal with TR neuron activity during a period of SWS following the learning session, as compared to their SWS activity before. What matters is not so much the number of times neurons fire but when they fire, with respect to the local field potential (LFP). The LFP is a measure of the average electrical activity in the neuron's cellular neighborhood, which tends to oscillate in fairly regular cycles. (Probably important to note that it's still a bit of a mystery exactly what neural activity is reflected in the LFP—it's likely some aggregate including incoming and outgoing signals as well as local processing.) Phase locking analysis determines whether an individual neuron's spikes occur at the same phase (e.g., near the peak or the trough) of the LFP waves. You might think of the LFP as a kind of neural background music—neurons who're clapping with the beat are phase-locked. Neurons who are just randomly active (even if they have a high average firing rate), aren't.

Phase-locking is a common metric in memory studies. It's been shown that phase-locking of individual neurons increases during the learning portion of various different memory tasks, and that performance on these tasks correlates with the amount of phase-locking. So the alignment of individual neurons' spikes with the LFP might be one mechanism for encoding new memories—one hypothesis is that certain phases of the LFP facilitate the formation of new neural connections (synapses); or, the rhythmicity of the LFP might act like a time-keeper that helps coordinate the widespread brain activity necessary to retrieve a memory. Again, bit of a mystery, but the data are consistent: phase-locking is a key mechanism in memory formation.

But back to the rats: after they learned the task, TR neurons exhibited increased phase-locking during SWS, as compared to before the training. Phase-locking of task-unrelated (TU) neurons didn't change, nor did that of the TR neurons in rats who failed to actually learn the task. That's the first indication that task-related activity was being reactivated during sleep.

At the population level, correlations and interactions among the studied neurons were also different during the SWS periods after learning. First, there was an increase in spike-spike coherence between pairs of TR neurons, meaning that when one TR neuron fired, others tended to fire as well. They also used principle component analysis (PCA) to probe more complex interactions (“components”) within the population activity. PCA involves a lot of fancy math (read: REALLY FUN MATH but too tangential for this post), but as a quick example, imagine that neuron A is a TR neuron that fires a bunch of spikes whenever the animal performs the task. Imagine neuron A is linked to neuron B (as many neurons within a small cortical region might be) such that A can only fire more if B also calms down and fires less than its average baseline rate. PCA would find this interaction—the combined decrease in B's firing rate with the increase in A's—and other possibly more complicated “components,” and highlight them as important aspects of task-related activity at the population level. You might have already guessed, but these components, too, were both reactivated during SWS and phase-locked to the LFP, just like the individual TR neurons' spikes.

Those results are really cool—they show that in the period of SWS after rats learn a motor task, the increased activity in motor cortex is not simply a diffuse boost in the spiking rates throughout M1, but the specific rehearsal of (approximately) the actual population activity required to do the task.

But to show that these neural changes are actually the mechanism behind sleep-related performance improvements, you also have to show that performance actually improves after sleep, and that this improvement depends on the neural changes. The researchers find exactly that—they tested the animals in two sessions, before and after a period of sleep, and found that rats whose TR neurons became more phase-locked during SWS improved more between the end of session 1 and beginning of session 2. And, the more time a rat spent in SWS during his nap, the more he improved in session 2.

All together, this evidence suggests the reactivation of task-related neural activity during SWS as an important mechanism for sleep-dependent improvements in motor learning. It also helps define the roles of sleep and plasticity with respect to brain-machine interfaces, which can be used to replace lost sensation (for example, cochlear implants) or even (someday) amputated limbs.

Way back in December, I reviewed Trespassing on Einstein's Lawn: A Father a Daughter, the Meaning of Nothing, and the Beginning of Everything, by Amanda Gefter, for the Advanced Science Communication Workshop led by Prof. Stephen S. Hall at NYU. TL;DR version: this book actually makes physics fun. You can get it on Amazon. If you want. No one's paying me to recommend it; I'm not friends with the author, etc, etc. (Although I did meet her at the book release party and she was kind enough to sign my galley! or I was creepy enough to frighten her into it. Whatever.)

Amanda Gefter is like your cool college roommate walking you through the frat house of modern physics. A self-described physics party-crasher, the rebel teen turned aspiring novelist turned science writer maintains an outsider status that makes her an unintimidating guide throughout her cosmology review turned memoir. She doesn't mind letting you know she's totally starstruck any time she meets a famous physicist, which happens so many times it would sound like name-dropping if she weren't so palpably psyched for each encounter. Not that she wears overly rosy glasses—she's not afraid to call Sir Martin Rees a buzzkill, or admit that she's downright terrified of the “ten-dimensional fire raging” in the eyes of string-theoretician David Gross. In addition to a who's-who of modern cosmology, Trespassing on Einstein's Lawn satisfies as both a personal history and a caffeinated romp through the scientific literature.

The intellectual journey begins in a Chinese restaurant in 1995, when Gefter's father, Warren, asks her a question both simple and incredibly deep: “'How would you define nothing?'” A respected radiologist and closet “groovy lotus-posing dude,” Warren has thought that question through: an “infinite, unbounded, homogenous state,” he proposes, might be called nothing. But the follow-up is much trickier: How do you get something—like the universe, for example—from nothing? Tasked with divining the origin of the universe, teenage Amanda is rightly baffled. “'I think we should find out,'” Warren suggests, as if they could simply ask Siri. Never mind that the iPhone wouldn't be developed for another decade—that's a problem physicists and philosophers have struggled with since before anyone thought to draw a distinction between the two. Over the next fifteen years the father-daughter duo spends countless hours poring over the physics literature trying to crack the cosmological nut, even after Amanda moves to New York hoping to become a novelist.

Eventually this hobby becomes something more for Amanda: In 2003, she uses her job at Manhattan Bride magazine (whose name she conveniently truncates to Manhattan magazine) to score press passes to a physics conference at Princeton. After some suitably endearing fan-girling (“Holy shit! I just touched Brian Greene!”), the Gefters finally get to meet the legendary Princeton theorist John Archibald Wheeler, who's become something of a cosmology spirit-guide to them over the years. He leaves them with two oracular statements: “The universe is a self-excited circuit” and “The boundary of a boundary is zero.” He also galvanizes their intellectual mission and, perhaps unwittingly, Amanda's uncertain career trajectory. Realizing that press passes are her golden ticket to interviews with the scientists behind the books she's studied, she builds on the success of her fake-it-till-you-make-it model and strong-arms her way into the world of science journalism. “It's the perfect alibi.”

The book's biggest accomplishment is its distillation of the mind-boggling themes of modern physics into mercifully colloquial English. Gefter's treatment of the somewhat intangible central problem—the origin and ultimate reality of the universe—begins with the tension between Einstein's special and general relativity and quantum mechanics, and moves on to cover inflation (the Big Bang), the role of event horizons and clues from black holes, the holographic principle, string theory, and Gödel's struggle with self-reference. The story that emerges is a massive shift in scientists' fundamental conception of the universe, as the global, “God's-eye view” is gradually dismantled in favor of local, observer-dependent models. As string theory founding father Leonard Susskind hints (in one of several personal emails to Gefter), “Something is happening.”

Gefter not only illuminates the relevant research, she ruminates on it, allowing both the reader and herself time to digest and appreciate it. This is the cool, weird physics your mom warned you about, and Gefter, ever the rebellious teenager at heart, is not afraid to look it straight in the eye. On the uncertainty principle: “The quantum works in mysterious ways? Really, science?” Several more pages are then devoted to a thorough meditation on the problem, its possible solutions, and, pleasingly, its meaning in the real world (“I had never once found myself in Manhattan and Brooklyn […] If the world was really so quantum, where were all the simultaneously alive-and-dead cats?”).

Despite the spunky, no-nonsense attitude, all the talk of things like event horizons, entanglement, and space-time foam (which, yes, is a real thing) does feel a bit Doctor Who after a while. (“The fabric of reality bursts at the seams”?) But that's a good thing. I had great fun regaining the childlike excitement about physics I had only ever gotten from science fiction. (Warren's explanation of general relativity will feel particularly nostalgic to anyone who was ever captivated by Madeleine L'Engle's A Wrinkle In Time.)

The characters in this story are varied and multi-dimensional. (Maybe not eleven-dimensional, but at that point personality might curve in on itself to a pathological extent anyway.) Dozens of scientists make cameos throughout the book, some of whom seem to become friends and mentors, and Gefter lets their humanity shine through in the telling of her personal encounters with them. The chapter devoted to Wheeler's journals (and to the Gefters' reading of them) is particularly moving. From the hobbyist Warren Gefter to giants like Stephen Hawking, there's a consistent sense of urgency to complete the intellectual mission, though, at the same time, a resignation that it won't be finished in this lifetime, that it must be lovingly passed on to the next generation of scientists.

There's a larger discussion, too, about the philosophy of science (in which Gefter earned a Master's degree from the London School of Economics, partly because she wanted to and partly because it seemed like a good way to attract the attention of the book-agent-slash-Reality-Club-founder John Brockman). She frames the theme of observer-dependence, which results in two descriptions of the world that seem mutually exclusive (as Einstein famously noted, a free-falling man feels no force, but is accelerating according to observers on the ground), as a fragmentation of reality. It's the job of physics to glue it back together, by inventing theoretical models (like Einstein's general relativity) which account for the discrepancy and reassure us that the two observers are simply describing the same reality from different perspectives. She explores what this can tell us about what's really real in the world, what's just a neat mathematical story we tell ourselves, and the role of science in telling them apart.

There's a lot of deep stuff in this book—as she says explicitly, Gefter is a woman of ideas. But like so many scientists, her personal journey is nearly inseparable from the ideas she studies. And so interspersed with the heavily technical or philosophical passages are autobiographical episodes with themes that parallel the scientific concepts, betraying their hold on her everyday experience and giving the reader's brain the occasional breather. These analogies can get a little tired—there's an entire chapter called “Shrödinger's Rats” during which she periodically tries to convince herself that the rodents in her London flat might not be “ontologically valid.” But they are mostly enlightening, offering yet more lenses through which to understand the scientific concepts, as well as a true-to-life window into the mind of a science-lover.

Just wanted to point to this very important criticism of the latest iteration of “HEY LOOK WOMEN’S BRAINS REALLY ARE DIFFERENT” line of research that will. not. die.

I won't summarize too thoroughly here, because it's a nice short article and you should just read it, but in brief: There might be some differences between the brains of men and women, but they are likely miniscule in magnitude, and what they mean, if anything, is completely up in the air. And as the scare-quotes in the title imply, “hardwired” is a very strong word; it's almost always unwarranted to claim that any difference between individuals or groups is “hardwired”, especially given what we know about the extent to which the environment affects neural development. Except for the most profound deviations (like neurodevelopmental disorders), chalking it up to “hardwired” does no one any good. It ignores the world of external factors—social, emotional, dietary, even cultural—that shape the brain's development. And, especially on touchy issues like gender, it lends itself to continual support of existing stereotypes.

I also want to emphasize what I think is the most important sentence in the entire piece: “The trouble is, the study is riddled with faulty assumptions and methodological flaws.” This is something to keep in mind while reading any neuroimaging study, ever. I'm not kidding; for fMRI, it's practically tautology. (I say that as someone who uses the technique but aims for some standard of self-awareness, at least.)

Evidently there’s something going on with Disqus. Or I did something silly. But comments aren’t working. I’ll try to fix this soon. Sorry.

Amusing preliminary data from a tactile perception experiment in my lab seem to reveal that women are much more sensitive than men to increases in the intensity of small vibrations.

The experiment was designed to probe the brain's processing of tactile stimulation using two small vibrating pads. Participants in the experiment placed their thumb and middle finger against the apparatus as the vibrations varied from low to high intensity, and the experimenters measured their sensitivity. A bit like a hearing test, subjects responded when they felt stimulation to their thumbs. The “masking” stimulus, on the middle finger, was meant to degrade their performance. Measuring exactly how much the masking stimulus affects the sensitivity of the thumb will tell us more about how these stimuli are processed in the somatosensory cortex, the chunk of the brain that creates our sense of touch.

I generally have an automatic cringe-reflex to any and all mention of gender differences in psychology or neuroscience papers, for reasons we can discuss later, but a first pass over the data from this experiment shows some pretty un-subtle effects:

In the top panel, the data from the male participants (n=4); below, the women (n=4). The x-axis of each graph represents the intensity of the thumb stimulation, increasing from left to right. The y-axis indexes the percentage of trials on which participants correctly detected the target stimulus. Data from women is well described by typical psychometric functions: pretty bad at low intensity, increasing through the middle range until it reaches 100% (ceiling) accuracy. But look at the men's data—they do alright (better than the women, in fact) at low levels, but show basically no improvement with added intensity, no matter how much.

This data is absolutely begging for a sexy headline. If you can think of a joke about men, women, and vibrators, it's probably been made by some member of my lab this afternoon. Equally hilarious, some of the attempted justifications by the male half of the dataset: “It's our hands, they're so callused from physical labor!” “We were clearly just squeezing the apparatus so hard with our manly strength!” “But our performance is great at low intensity! It's great across the board!” etc.

Sorry about the lack of science around here lately; I've been working hard on a paper for submission to a research journal over the last few weeks. I'll make it up to you soon with monkeys, controlling other monkeys' arms, with their minds. Not joking.

Here's a slightly personal piece (I'll do that here, sometimes) about why I do science. I wrote it last spring for NYU's Science Communication Workshop, which is taught by Prof. Stephen Hall and offered to medical students and PhD students and post-docs in the sciences.

I Knew I Wanted To Be A Scientist When… |

I capitalize the phrase in a kind of mockery that makes me feel better about the fact that I have no idea how to attack this question. Am I supposed to pick the single moment upon which my entire professional life hinges? Do people really experience such concise periods of self-revelation?

I let the cursor blink at me for untold minutes.

I log in to Facebook to crowd-source this problem. What answers might my friends give? Maybe one of them will trigger a memory of my own.

Ellie Anders[1]: when i decided it was the best route to take to get close to my hot teacher in hopes of having taboo yet legal sex with him

Well. So much for that. I tap my keyboard lightly, to no noticeable consequence besides the small cracks of sound in my otherwise silent apartment.

I consider my application essay for admission to graduate school, which waxes lyrical on the subject of single-cell recordings in the feline visual system. Hubel and Wiesel won a Nobel Prize for their pioneering experiments, using electrodes like tiny exploratory vessels, stuck into the cat’s brain to measure the activity of individual neurons as the animal viewed the prescribed stimuli. It’s a beautiful and revolutionary oeuvre, the ground work for our understanding of visual computations at a cellular level, and it piqued my interest in that particular research area when I first learned of it.

But falling back on this story seems disingenuous. Or, at the very least, imprecise. This is the beginning of my study of vision science, but not the beginning of my study of science. When was that? You went to a tech school, I coach myself, Start with step one: defining the problem. Science. I let my mind wander. Dangerous—this usually ends in either confusion or accidental sleep.

I remember a brief thought experiment from one of my classes last year, where we came, mostly in passing, to a conclusion that sat pretty well with me. In just a few words: suppose you had a perfect working supercomputer-model of the brain, and you could program every single cell down to the subatomic particle interactions. What would you learn from it? You could do lots of stuff to it and observe the outcome, but then you’d just be making a bunch of measurements. You couldn’t predict what you’d get for any new input. You couldn’t generalize any of its particular behaviors, and if you thought the model was somehow wrong, you’d have so many zillions of parameters that you’d have no idea where to start fixing it. If you modeled every detail, at the end of the day you’d understand nothing.

Once you know everything about a system, you understand nothing. Understanding, in the scientific sense, means the ability to make reliable and testable predictions about the behavior of a system in novel environments, to generalize and describe the results of certain whole categories of input. You need abstract models with small numbers of abstract parameters that you can adjust to fit huge numbers of relevant situations. If you want to understand more, sometimes you have to know less.

It was a nice break from the usual talk about how science lets us creep infinitely close to grasping reality, and how important it is that we keep doing this even though we know we may always fall a bit short. But this idea of understanding more by knowing less put a new spin on that failure, for me. The little gap between what we can model and what’s real turns out not to be the nuisance we try to squish ever narrower but the place where science really lives; in fact science builds its own little gaps all over the place. Every model is a metaphor, which aids understanding by comparing two things that are similar but by definition not equivalent. Rather than persisting valiantly in the face of human limitations, science becomes an exploitation of failure, turning not-knowing into understanding. I liked this idea.

I tap my keyboard again and debate the apparent flakiness of deciding to be a scientist after beginning a PhD in science. To what extent does this incriminate me if it reaches my superiors? Or my peers, who might eventually be in a position to hire me? I decide that’s not really the beginning of the story either. That science is less about absolute reality than story-telling is something I have long suspected. I knew what I was doing when I applied here.

Okay. So I just have to remember a time when I first felt the need to tell such stories and I’ll have the answer. I lie back on my couch and stare at the dimly lit ceiling. One of the bulbs in the fixture burnt out a few months ago. I should ask somebody to fix that. A siren wails down Broadway. The clubs will let out soon.

(If I just made up a memory right now, would that constitute consistency or noncompliance?)

Maybe the best answer is that I don’t know after all. I just understand that my mind works well this way: inventing an idea, reasoning its implications, poking and prodding to see how well nature subscribes. Keeping itself mostly removed from the specifics of life’s gritty mechanisms.

I remind myself that models will fail, eventually, in some respect. I don’t know how this model of myself as a scientist will fail; I may not even manage, in my lifetime, to stumble upon any of the few predictions it gets wrong. That would be disappointing; the limits of a model’s ability to describe a system are often much more informative than continually predictable behavior. And it doesn’t mean you abandon the model. Just because you’ve found one limit doesn’t mean you’ve found them all. I yawn.

I sit up and decide to tell a story.

Vaccines do not cause autism. Period. Sorry. That's a totally awkward introduction, and only tangentially related to the rest of this post, but apparently some people are still confused about this and since I'm about to discuss autism and bacteria I'd feel remiss if I weren't obnoxiously explicit and clear about this. There is no scientific evidence that vaccination leads to autism. Period.

A mouse model for autism displays GI problems due to the abnormal gut bacteria, and the treatment of this problem alleviates their autism-like behavioral symptoms as well as their digestive problems, according to a study published in Cell in December.

The study was motivated by reports showing a high rate of comorbidity of GI distress in people with Autism Spectrum Disorders (ASD) and adds to the mounting evidence of strong connections between the brain and the gut; GI symptoms have been linked to other neurological diseases, including cerebral palsy and major depression. Treating GI problems by correcting the composition of the gut microbiome has been shown to ameliorate symptoms of multiple sclerosis and depression in mice, and in humans, may ease the emotional symptoms of chronic fatigue and psychological distress.

There are several mouse models used to study autism; this study, published by Elaine Hsiao and her colleagues at Cal Tech and Baylor College of Medicine, used maternal immune activation (MIA) mice, whose mothers had been injected with a virus called poly(I:C) while they were pregnant. The mice aren't officially classified as autistic, since that diagnosis rides on particular human social behaviors. As one of my most quotable lab mates says, by DSM standards, mice are all autistic: none of them can communicate verbally and they never make eye contact. But MIA mice display several behaviors analogous to some hallmarks of behavior in ASD children. The mice show high levels of anxiety, measured by their reluctance to explore new environments, and are more easily startled by—and less able to habituate to—experimental presentation of loud sounds. They also exhibit repetitive motor behaviors. They make fewer communicative noises and strongly prefer objects to other mice, and familiar mice to newer ones.

Hsiao and colleagues showed that MIA mice also have “leaky gut” syndrome, wherein the intestinal walls are more permeable than normal, allowing potentially harmful chemicals to escape into the bloodstream. This is due to the makeup of the bacterial population living in the intestines of the mice; bacteria in MIA mice were just as abundant as in the control animals, but species of certain classes of bacteria were represented in unusual proportions.

Scientists gave the MIA mice a dose of Bacteroides fragilis, a bacterium used to combat inflammation of the colon. The treatment reduced the permeability of the intestinal walls and rebalanced the microbiome composition. It also had profound effects on the mice's behaviors: they became less anxious and began to explore more and startle less, and their communicative vocalizations increased in frequency and duration. They did retain some social deficits, still preferring objects to mice and familiar mice to novel ones.

Stephen Hawking posted a new paper on the arXive on January 22, apparently sending black holes the way of the tooth fairy, and Pluto's planethood (which, let's be honest, was always dubious). The sensational edge of the short paper has been drawing mixed reactions from the physics community.

The paper—which is, by the way, a two-page write-up of a talk Hawking gave last year, and has not been submitted for peer review—actually didn't say that black holes don't exist at all, but rather that they don't exist exactly as we've understood them for the last 40 years, and that, as he told Nature, “the correct treatment remains a mystery.” (But that makes a much less sexy headline than “there are no black holes,” which, in fairness to the media coverage, is a direct quote from Hawking's paper.)

In particular, Hawking does away with the event horizon—the point-of-no-return boundary where, once inside, even light can never escape—replacing it with an “apparent horizon.” An apparent horizon bounds the region where energy and matter are trapped because they'll accelerate toward the center of the black hole no matter how fast they're going initially.

If those sound like the same thing, it's because they almost are. The difference is the “never” in the definition of the event horizon: no energy, matter, or information ever escapes an event horizon, ever. But an apparent horizon can fluctuate; theoretically, it could shrink so that something which was once trapped is suddenly outside the horizon, free. So even though, at any given moment, anything inside an apparent horizon will head toward the center, nothing is necessarily stuck there for eternity.

Hawking's paper says the things we think are event horizons are actually apparent horizons, and since black holes are technically defined as regions surrounded by event horizons, black holes as we thought we knew them don't really exist.

While it forces us to redefine the phenomena we call black holes, the proposal does offer potential resolution to what's called the black hole information paradox. This arises because Einstein's general relativity says that any matter or energy that falls into a black hole is irrevocably lost, while quantum mechanics says information is always conserved. In other words, according to quantum laws, complete knowledge of the state of a black hole should enable us (theoretically) to figure out what's fallen in, but general relativity says the infinite gravity of a black hole makes this impossible, destroying infalling material beyond recognition. Physics is stuck with the choice between completely reworking quantum theory to allow information loss and trying to find information where there seems to be none.

In the '70's, Hawking made a case for information loss, showing that black holes emit radiation (which now bears his name) and that that radiation is totally random, meaning it carries no information. In the late 90's, a mathematical proof drawing on string theory showed that the information had to be there. It was a far cry from solving black holes completely, because no one knew how the seemingly random Hawking radiation could possibly be decoded, but the proof was strong enough that even Hawking, himself who had stood staunchly for Team Information Loss for decades, had to concede.

The most popular and, to physicists, the most frustrating attempt yet to reclaim information from black holes is the majestically named firewall paradox. The term is actually not hyperbole: the solution, published in 2012 by a team of physicists led by Joseph Polcinski at the Kavli Institute of Theoretical Physics, proposes that black hole event horizons actually are giant walls of fire. Their argument depends on entanglement, a phenomenon where the quantum states of two particles become tightly linked (even though they may be separated by large distances). Polcinski's paper suggests that entanglements among the Hawking radiation particles might allow the whole system to encode information even though its individual particles don't. But those particles are already spoken for, each entangled with an in-falling anti-particle complement. The monogamy of entanglement (yes, that's an actual technical term) precludes the particles from simultaneous entanglement with both their radiated cohort and their fallen friends, so they must first break the original links. This releases huge amounts of energy--enough to literally burn you up, leaving you feeling way, way worse than when Brad and Jen split. (But how could anything possibly be worse than that?! Physics.)

This has been driving physicists nuts ever since it was published, not least because it flies in the face of general relativity, which says that the event horizon should be an unremarkable place if you're falling through it. (You can see how a giant wall of fire might be a decidedly remarkable clue that you're reaching the point of no return.) But they've yet to settle on an alternative model that still conserves information.

So Hawking's solution, in almost childlike simplicity, is to simply get rid of event horizons. If the only way to conserve information is to turn the horizon into a firewall, and we know there can't really be a firewall, then maybe the horizon isn't eternal. Maybe, as Hawking says, it's an apparent horizon, strong enough to trap matter and energy inside its bounds, but impermanent, subject to quantum fluctuations that eventually release the captive material. By that point whatever came out would be so mangled it would be hard to figure out what it once was—Hawking compares it to trying to accurately predict the weather many days in advance—but theoretically, the information would be there.

But getting back to my point, which was to be a little bit critical, a lot of physicists are expressing doubt about Hawking's claims. Sabine Hossenfelder at the Nordic Institute for Theoretical Physics in Stockholm, wrote in her blog that while apparent horizons may not be eternal, they do “exceed the lifetime of the universe by several orders of magnitude,” so the distinction, even if it's theoretically important, is practically irrelevant. Sure, instead of black holes they're The Artists Formerly Known As Black Holes, but they're basically the same beasts.

In the same post, Hossenfelder also writes that she and others have already argued that black holes have temporary rather than eternal horizons. None of them are mentioned in Hawking's paper. Even physicists who don't have a bone to pick with Hawking agree that this idea isn't so new—Don Page, a physicist at the University of Alberta who worked with Hawking in the 70's, echoed Hossenfelder's claim, telling SPACE.com, “The idea that a black hole does not truly have an event horizon goes back more than a third of a century, and I would not be surprised if someone could trace it back even many years earlier.”

Polcinski, he of firewall fame, doubts that Hawking's proposal is practically viable, pointing out that apparent horizons would require quantum fluctuations so huge they probably don't happen in nature. “We never see space-time fluctuate in our neighbourhood: it is just too rare on large scales,” he told Nature.

Plus, some physicists, including Polcinski and theoretical physicist Raphael Bousso of U.C. Berkeley aren't even sure Hawking's solution would eliminate the firewall.