Nosimpler

The amplitude of high broadband activity in human cortical field potentials indicates local processing and has repeatedly been shown to reflect motor control in the primary motor cortex. In a group of male and female subjects affected by essential tremor and undergoing deep brain stimulation surgery, ventral intermediate nucleus low-frequency oscillations (<30 Hz) entrain the corticomotor high broadband activity (>40 Hz) during rest, relinquishing that role during movement execution. This finding suggests that there is significant cross-rhythm communication between thalamocortical regions, and motor behavior corresponds to changes in thalamocortical phase-amplitude coupling profiles. Herein, we demonstrate that thalamocortical coupling is a crucial mechanism for gating motor behavior.

SIGNIFICANCE STATEMENT We demonstrate, for the first time, how thalamocortical coupling is mediating movement execution in humans. We show how the low-frequency oscillation from the ventral intermediate nucleus, known as the motor nucleus of the thalamus, entrains the excitability of the primary motor cortex, as reflected by the phase-amplitude coupling between the two regions. We show that thalamocortical phase-amplitude coupling is a manifestation of a gating mechanism for movement execution mediated by the thalamus. These findings highlight the importance of incorporating cross-frequency relationship in models of motor behavior; and given the spatial specificity of this mechanism, this work could be used to improve functional targeting during surgical implantations in subcortical regions.

Everyone knows that spiders fly through the air on strands of their silk.  But it's not just a matter of chance and favorable winds.  The relevance of a surprising factor (earth's electrical field) is explained in a recent issue of Cell:Current Biology:

... the involvement of electrostatic forces in ballooning has never been tested. Several issues have emerged when models using aerodynamic drag alone are employed to explain ballooning dispersal. For example, many spiders balloon using multiple strands of silk that splay out in a fan-like shape. Instead of tangling and meandering in light air currents, each silk strand is kept separate, pointing to the action of a repelling electrostatic force. Questions also arise as to how spiders are able to rapidly emit ballooning silk into the air with the low wind speeds observed in ballooning; the mechanics of silk production requires sufficient external forces to pull silk from spinnerets during spinning...

In the early 20^th century, atmospheric electricity was intensively studied, establishing the ubiquity of the atmospheric potential gradient (APG); from fair to stormy weather, an APG is always present, varying in strength and polarity with local meteorological conditions. Over a flat field on a day with clear skies, the APG is approximately 120 Vm⁻¹... Closer to the tree, around sharp leaf, needle, and branch tips, e-fields easily reach tens of kilovolts per meter... the spider’s unlearned response to e-fields is to engage in ballooning, and, on becoming airborne, switching the e-field on and off results in the spider moving upward (on) or downward (off) [video at the link].

Ed Yong discusses this research in his Atlantic column.

Plants, being earthed, have the same negative charge as the ground that they grow upon, but they protrude into the positively charged air. This creates substantial electric fields between the air around them and the tips of their leaves and branches—and the spiders ballooning from those tips.

Many of the spiders actually managed to take off, despite being in closed boxes with no airflow within them.

Image cropped for clarity from the original, where there is a little bit of relevant commentary about why this happens and how someone can use context to read it.  Readers here may be able to offer additional insights.

Addendum:   Several good comments from readers, and a hat tip to Aleksejs for providing this dissection of the cursive "chinchilla":

Models of sequence evolution typically assume that all sequences are possible. However, restriction enzymes that cut DNA at specific recognition sites provide an example where carrying a recognition sequence can be lethal. Motivated by this observation, we studied the set of strings over a finite alphabet with taboos, that is, with prohibited substrings. The taboo-set is referred to as T and any allowed string as a taboo-free string. We consider the graph {Gamma}n(T) whose vertices are taboo-free strings of length n and whose edges connect two taboo-free strings if their Hamming distance equals 1. Any (random) walk on this graph describes the evolution of a DNA sequence that avoids deleterious taboos. We describe the construction of the vertex set of {Gamma}n(T). Then we state conditions under which {Gamma}n(T) and its suffix subgraphs are connected. Moreover, we provide a simple algorithm that can determine, for an arbitrary T, if all these graphs are connected. We concluded that bacterial taboo-free Hamming graphs are nearly always connected, although 4 properly chosen taboos are enough to disconnect one of its suffix subgraphs.

Chunking in language comprehension is a process that segments continuous linguistic input into smaller chunks that are in the mental lexicon of reader. Effective chunking during reading facilitates disambiguation and enhances efficiency for comprehension. However, the mechanisms of chunking remain elusive, especially in reading given that information arrives simultaneously yet the written systems may not have explicit cues for labeling boundaries such as Chinese. What are the mechanisms of chunking operation that mediates the reading of the text that normally contains hierarchical information? We investigated this question by manipulating the lexical status of the chunks at distinct levels of grain-size in four-character Chinese strings, including the two-character local chunk and four-character global chunk. Participants were asked to make lexical decision on these strings in a behavioral experiment, followed by a passive reading task when their electroencephalography (EEG) were recorded. The behavioral results showed that the lexical decision time of lexicalized two-character local chunks was influenced by the lexical status of four-character global chunk, but not vice versa, which indicated that the processing of global chunks possessed priority over the local chunks. The EEG results revealed that familiar lexical chunks were detected simultaneously at both levels and further processed in a different temporal order -- the onset of lexical access for the global chunks was earlier than that of local chunks. These consistent behavioral and EEG results suggest that chunking in reading occurs at multiple levels via a two-stage operation -- simultaneous detection and global-first recognition.

You’ve seen the stories—in the Financial Times, Technology Review, CNET, Facebook, Reddit, Twitter, or elsewhere—saying that a group at Google has now achieved quantum computational supremacy with a 53-qubit superconducting device. While these stories are easy to find, I’m not going to link to them here, for the simple reason that none of them were supposed to exist yet.

As the world now knows, Google is indeed preparing a big announcement about quantum supremacy, to coincide with the publication of its research paper in a high-profile journal (which journal? you can probably narrow it down to two). This will hopefully happen within a month.

Meanwhile, though, NASA, which has some contributors to the work, inadvertently posted an outdated version of the Google paper on a public website. It was there only briefly, but long enough to make it to the Financial Times, my inbox, and millions of other places. Fact-free pontificating about what it means has predictably proliferated.

The world, it seems, is going to be denied its clean “moon landing” moment, wherein the Extended Church-Turing Thesis gets experimentally obliterated within the space of a press conference. This is going to be more like the Wright Brothers’ flight—about which rumors and half-truths leaked out in dribs and drabs between 1903 and 1908, the year Will and Orville finally agreed to do public demonstration flights. (This time around, though, it thankfully won’t take that long to clear everything up!)

I’ve known about what was in the works for a couple months now; it was excruciating not being able to blog about it. Though sworn to secrecy, I couldn’t resist dropping some hints here and there (did you catch any?)—for example, in my recent Bernays Lectures in Zürich, a lecture series whose entire structure built up to the brink of this moment.

This post is not an official announcement or confirmation of anything. Though the lightning may already be visible, the thunder belongs to the group at Google, at a time and place of its choosing.

Rather, because so much misinformation is swirling around, what I thought I’d do here, in my role as blogger and “public intellectual,” is offer Scott’s Supreme Quantum Supremacy FAQ. You know, just in case you were randomly curious about the topic of quantum supremacy, or wanted to know what the implications would be if some search engine company based in Mountain View or wherever were hypothetically to claim to have achieved quantum supremacy.

Without further ado, then:

Q1. What is quantum computational supremacy?

Often abbreviated to just “quantum supremacy,” the term refers to the use of a quantum computer to solve some well-defined set of problems that would take orders of magnitude longer to solve with any currently known algorithms running on existing classical computers—and not for incidental reasons, but for reasons of asymptotic quantum complexity. The emphasis here is on being as sure as possible that the problem really was solved quantumly and really is classically intractable, and ideally achieving the speedup soon (with the noisy, non-universal QCs of the present or very near future). If the problem is also useful for something, then so much the better, but that’s not at all necessary. The Wright Flyer and the Fermi pile weren’t useful in themselves.

Q2. If Google has indeed achieved quantum supremacy, does that mean that now “no code is uncrackable”, as Democratic presidential candidate Andrew Yang recently tweeted?

No, it doesn’t. (But I still like Yang’s candidacy.)

There are two issues here. First, the devices currently being built by Google, IBM, and others have 50-100 qubits and no error-correction. Running Shor’s algorithm to break the RSA cryptosystem would require several thousand logical qubits. With known error-correction methods, that could easily translate into millions of physical qubits, and those probably of a higher quality than any that exist today. I don’t think anyone is close to that, and we have no idea how long it will take.

But the second issue is that, even in a hypothetical future with scalable, error-corrected QCs, on our current understanding they’ll only be able to crack some codes, not all of them. By an unfortunate coincidence, the public-key codes that they can crack include most of what we currently use to secure the Internet: RSA, Diffie-Hellman, elliptic curve crypto, etc. But symmetric-key crypto should only be minimally affected. And there are even candidates for public-key cryptosystems (for example, based on lattices) that no one knows how to break quantumly after 20+ years of trying, and some efforts underway now to start migrating to those systems. For more, see for example my letter to Rebecca Goldstein.

Q3. What calculation is Google planning to do, or has it already done, that’s believed to be classically hard?

So, I can tell you, but I’ll feel slightly sheepish doing so. The calculation is: a “challenger” generates a random quantum circuit C (i.e., a random sequence of 1-qubit and nearest-neighbor 2-qubit gates, of depth perhaps 20, acting on a 2D grid of n = 50 to 60 qubits). The challenger then sends C to the quantum computer, and asks it apply C to the all-0 initial state, measure the result in the {0,1} basis, send back whatever n-bit string was observed, and repeat some thousands or millions of times. Finally, using its knowledge of C, the classical challenger applies a statistical test to check whether the outputs are consistent with the QC having done this.

So, this is not a problem like factoring with a single right answer. The circuit C gives rise to some probability distribution, call it D_C, over n-bit strings, and the problem is to output samples from that distribution. In fact, there will typically be 2ⁿ strings in the support of D_C—so many that, if the QC is working as expected, the same output will never be observed twice. A crucial point, though, is that the distribution D_C is not uniform. Some strings enjoy constructive interference of amplitudes and therefore have larger probabilities, while others suffer destructive interference and have smaller probabilities. And even though we’ll only see a number of samples that’s tiny compared to 2ⁿ, we can check whether the samples preferentially cluster among the strings that are predicted to be likelier, and thereby build up our confidence that something classically intractable is being done.

So, tl;dr, the quantum computer is simply asked to apply a random (but known) sequence of quantum operations—not because we intrinsically care about the result, but because we’re trying to prove that it can beat a classical computer at some well-defined task.

Q4. But if the quantum computer is just executing some random garbage circuit, whose only purpose is to be hard to simulate classically, then who cares? Isn’t this a big overhyped nothingburger?

No. As I put it the other day, it’s not an everythingburger, but it’s certainly at least a somethingburger!

It’s like, have a little respect for the immensity of what we’re talking about here, and for the terrifying engineering that’s needed to make it reality. Before quantum supremacy, by definition, the QC skeptics can all laugh to each other that, for all the billions of dollars spent over 20+ years, still no quantum computer has even once been used to solve any problem faster than your laptop could solve it, or at least not in any way that depended on its being a quantum computer. In a post-quantum-supremacy world, that’s no longer the case. A superposition involving 2⁵⁰ or 2⁶⁰ complex numbers has been computationally harnessed, using time and space resources that are minuscule compared to 2⁵⁰ or 2⁶⁰.

I keep bringing up the Wright Flyer only because the chasm between what we’re talking about, and the dismissiveness I’m seeing in some corners of the Internet, is kind of breathtaking to me. It’s like, if you believed that useful air travel was fundamentally impossible, then seeing a dinky wooden propeller plane keep itself aloft wouldn’t refute your belief … but it sure as hell shouldn’t reassure you either.

Was I right to worry, years ago, that the constant drumbeat of hype about much less significant QC milestones would wear out people’s patience, so that they’d no longer care when something newsworthy finally did happen?

Q5. Years ago, you scolded the masses for being super-excited about D-Wave, and its claims to get huge quantum speedups for optimization problems via quantum annealing. Today you scold the masses for not being super-excited about quantum supremacy. Why can’t you stay consistent?

Because my goal is not to move the “excitement level” in some uniformly preferred direction, it’s to be right! With hindsight, would you say that I was mostly right about D-Wave, even when raining on that particular parade made me unpopular in some circles? Well, I’m trying to be right about quantum supremacy too.

Q6. If quantum supremacy calculations just involve sampling from probability distributions, how do you check that they were done correctly?

Glad you asked! This is the subject of a fair amount of theory that I and others developed over the last decade. I already gave you the short version in my answer to Q3: you check by doing statistics on the samples that the QC returned, to verify that they’re preferentially clustered in the “peaks” of the chaotic probability distribution D_C. One convenient way of doing this, which Google calls the “linear cross-entropy test,” is simply to sum up Pr[C outputs s_i] over all the samples s₁,…,s_k that the QC returned, and then to declare the test a “success” if and only if the sum exceeds some threshold—say, bk/2ⁿ, for some constant b strictly between 1 and 2.

Admittedly, in order to apply this test, you need to calculate the probabilities Pr[C outputs s_i] on your classical computer—and the only known ways to calculate them require brute force and take ~2ⁿ time. Is that a showstopper? No, not if n is 50, and you’re Google and are able to handle numbers like 2⁵⁰ (although not 2¹⁰⁰⁰, which exceeds a googol, har har). By running a huge cluster of classical cores for (say) a month, you can eventually verify the outputs that your QC produced in a few seconds—while also seeing that the QC was many orders of magnitude faster. However, this does mean that sampling-based quantum supremacy experiments are almost specifically designed for ~50-qubit devices like the ones being built right now. Even with 100 qubits, we wouldn’t know how to verify the results using all the classical computing power available on earth.

(Let me stress that this issue is specific to sampling experiments like the ones that are currently being done. If Shor’s algorithm factored a 2000-digit number, it would be easy to check the result by simply multiplying the claimed factors and running a primality test on them. Likewise, if a QC were used to simulate some complicated biomolecule, you could check its results by comparing them to experiment.)

Q7. Wait. If classical computers can only check the results of a quantum supremacy experiment, in a regime where the classical computers can still simulate the experiment (albeit extremely slowly), then how do you get to claim “quantum supremacy”?

Come on. With a 53-qubit chip, it’s perfectly feasible to see a speedup by a factor of many millions, in a regime where you can still directly verify the outputs, and also to see that the speedup is growing exponentially with the number of qubits, exactly as asymptotic analysis would predict. This isn’t marginal.

Q8. Is there a mathematical proof that no fast classical algorithm could possibly spoof the results of a sampling-based quantum supremacy experiment?

Not at present. But that’s not quantum supremacy researchers’ fault! As long as theoretical computer scientists can’t even prove basic conjectures like P≠NP or P≠PSPACE, there’s no hope of ruling out a fast classical simulation unconditionally. The best we can hope for are conditional hardness results. And we have indeed managed to prove some such results—see for example the BosonSampling paper, or the Bouland et al. paper on average-case #P-hardness of calculating amplitudes in random circuits, or my paper with Lijie Chen (“Complexity-Theoretic Foundations of Quantum Supremacy Experiments”). The biggest theoretical open problem in this area, in my opinion, is to prove better conditional hardness results.

Q9. Does sampling-based quantum supremacy have any applications in itself?

When people were first thinking about this subject, it seemed pretty obvious that the answer was “no”! (I know because I was one of the people.) Recently, however, the situation has changed—for example, because of my certified randomness protocol, which shows how a sampling-based quantum supremacy experiment could almost immediately be repurposed to generate bits that can be proven to be random to a skeptical third party (under computational assumptions). This, in turn, has possible applications to proof-of-stake cryptocurrencies and other cryptographic protocols. I’m hopeful that more such applications will be discovered in the near future.

Q10. If the quantum supremacy experiments are just generating random bits, isn’t that uninteresting? Isn’t it trivial to convert qubits into random bits, just by measuring them?

The key is that a quantum supremacy experiment doesn’t generate uniform random bits. Instead, it samples from some complicated, correlated probability distribution over 50- or 60-bit strings. In my certified randomness protocol, the deviations from uniformity play a central role in how the QC convinces a classical skeptic that it really was sampling the bits randomly, rather than in some secretly deterministic way (e.g., using a pseudorandom generator).

Q11. Haven’t decades of quantum-mechanical experiments–for example, the ones that violated the Bell inequality–already demonstrated quantum supremacy?

This is purely a confusion over words. Those other experiments demonstrated other forms of “quantum supremacy”: for example, in the case of Bell inequality violations, what you could call “quantum correlational supremacy.” They did not demonstrate quantum computational supremacy, meaning doing something that’s infeasible to simulate using a classical computer (where the classical simulation has no restrictions of spatial locality or anything else of that kind). Today, when people use the phrase “quantum supremacy,” it’s generally short for quantum computational supremacy.

Q12. Even so, there are countless examples of materials and chemical reactions that are hard to classically simulate, as well as special-purpose quantum simulators (like those of Lukin’s group at Harvard). Why don’t these already count as quantum computational supremacy?

Under some people’s definitions of “quantum computational supremacy,” they do! The key difference with Google’s effort is that they have a fully programmable device—one that you can program with an arbitrary sequence of nearest-neighbor 2-qubit gates, just by sending the appropriate signals from your classical computer.

In other words, it’s no longer open to the QC skeptics to sneer that, sure, there are quantum systems that are hard to simulate classically, but that’s just because nature is hard to simulate, and you don’t get to arbitrarily redefine whatever random chemical you find in the wild to be a “computer for simulating itself.” Under any sane definition, the superconducting devices that Google, IBM, and others are now building are indeed “computers.”

Q13. Did you (Scott Aaronson) invent the concept of quantum supremacy?

No. I did play some role in developing it, which led to Sabine Hossenfelder among others generously overcrediting me for the whole idea. The term “quantum supremacy” was coined by John Preskill in 2012, though in some sense the core concept goes back to the beginnings of quantum computing itself in the early 1980s. In 1993, Bernstein and Vazirani explicitly pointed out the severe apparent tension between quantum mechanics and the Extended Church-Turing Thesis of classical computer science. Then, in 1994, the use of Shor’s algorithm to factor a huge number became the quantum supremacy experiment par excellence—albeit, one that’s still (in 2019) much too hard to perform.

The key idea of instead demonstrating quantum supremacy using a sampling problem was, as far as I know, first suggested by Barbara Terhal and David DiVincenzo, in a farsighted paper from 2002. The “modern” push for sampling-based supremacy experiments started around 2011, when Alex Arkhipov and I published our paper on BosonSampling, and (independently of us) Bremner, Jozsa, and Shepherd published their paper on the commuting Hamiltonians model. These papers showed, not only that “simple,” non-universal quantum systems can solve apparently-hard sampling problems, but also that an efficient classical algorithm for the same sampling problems would imply a collapse of the polynomial hierarchy. Arkhipov and I also made a start toward arguing that even the approximate versions of quantum sampling problems can be classically hard.

As far as I know, the idea of “Random Circuit Sampling”—that is, generating your hard sampling problem by just picking a random sequence of 2-qubit gates in (say) a superconducting architecture—originated in an email thread that I started in December 2015, which also included John Martinis, Hartmut Neven, Sergio Boixo, Ashley Montanaro, Michael Bremner, Richard Jozsa, Aram Harrow, Greg Kuperberg, and others. The thread was entitled “Hard sampling problems with 40 qubits,” and my email began “Sorry for the spam.” I then discussed some advantages and disadvantages of three options for demonstrating sampling-based quantum supremacy: (1) random circuits, (2) commuting Hamiltonians, and (3) BosonSampling. After Greg Kuperberg chimed in to support option (1), a consensus quickly formed among the participants that (1) was indeed the best option from an engineering standpoint—and that, if the theoretical analysis wasn’t yet satisfactory for (1), then that was something we could remedy.

[Update: Sergio Boixo tells me that, internally, the Google group had been considering the idea of random circuit sampling since February 2015, even before my email thread. This doesn’t surprise me: while there are lots of details that had to be worked out, the idea itself is an extremely natural one.]

After that, the Google group did a huge amount of analysis of random circuit sampling, both theoretical and numerical, while Lijie Chen and I and Bouland et al. supplied different forms of complexity-theoretic evidence for the problem’s classical hardness.

Q14. If quantum supremacy was achieved, what would it mean for the QC skeptics?

I wouldn’t want to be them right now! They could retreat to the position that of course quantum supremacy is possible (who ever claimed that it wasn’t? surely not them!), that the real issue has always been quantum error-correction. And indeed, some of them have consistently maintained that position all along. But others, including my good friend Gil Kalai, are on record, right here on this blog predicting that even quantum supremacy can never be achieved for fundamental reasons. I won’t let them wiggle out of it now.

[Update: As many of you will have seen, Gil Kalai has taken the position that the Google result won’t stand and will need to be retracted. He asked for more data: specifically, a complete histogram of the output probabilities for a smaller number of qubits. This turns out to be already available, in a Science paper from 2018.]

Q15. What’s next?

If it’s achieved quantum supremacy, then I think the Google group already has the requisite hardware to demonstrate my protocol for generating certified random bits. And that’s indeed one of the very next things they’re planning to do.

[Addendum: Also, of course, the evidence for quantum supremacy itself can be made stronger and various loopholes closed—for example, by improving the fidelity so that fewer samples need to be taken (something that Umesh Vazirani tells me he’d like to see), by having the circuit C be generated and the outputs verified by a skeptic external to Google. and simply by letting more time pass, so outsiders can have a crack at simulating the results classically. My personal guess is that the basic picture is going to stand, but just like with the first experiments that claimed to violate the Bell inequality, there’s still plenty of room to force the skeptics into a tinier corner.]

Beyond that, one obvious next milestone would be to use a programmable QC, with (say) 50-100 qubits, to do some useful quantum simulation (say, of a condensed-matter system) much faster than any known classical method could do it. A second obvious milestone would be to demonstrate the use of quantum error-correction, to keep an encoded qubit alive for longer than the underlying physical qubits remain alive. There’s no doubt that Google, IBM, and the other players will now be racing toward both of these milestones.

[Update: Steve Girvin reminds me that the Yale group has already achieved quantum error-correction “beyond the break-even point,” albeit in a bosonic system rather than superconducting qubits. So perhaps a better way to phrase the next milestone would be: achieve quantum computational supremacy and useful quantum error-correction in the same system.]

Another update: I thought this IEEE Spectrum piece gave a really nice overview of the issues.

Last update: John Preskill’s Quanta column about quantum supremacy is predictably excellent (and possibly a bit more accessible than this FAQ).

Calcium imaging has rapidly developed into a powerful tool for recording from large populations of neurons in vivo. Imaging in rhesus macaque motor cortex can enable the discovery of new principles of motor cortical function and can inform the design of next generation brain-computer interfaces (BCIs). Surface two-photon (2P) imaging, however, cannot presently access somatic calcium signals of neurons from all layers of macaque motor cortex due to photon scattering. Here, we demonstrate an implant and imaging system capable of chronic, motion-stabilized two-photon (2P) imaging of calcium signals from in macaques engaged in a motor task. By imaging apical dendrites, some of which originated from deep layer 5 neurons, as as well as superficial cell bodies, we achieved optical access to large populations of deep and superficial cortical neurons across dorsal premotor (PMd) and gyral primary motor (M1) cortices. Dendritic signals from individual neurons displayed tuning for different directions of arm movement, which was stable across many weeks. Combining several technical advances, we developed an optical BCI (oBCI) driven by these dendritic signals and successfully decoded movement direction online. By fusing 2P functional imaging with CLARITY volumetric imaging, we verify that an imaged dendrite, which contributed to oBCI decoding, originated from a putative Betz cell in motor cortical layer 5. This approach establishes new opportunities for studying motor control and designing BCIs.

The cardiac ventricular action potential depends on several voltage-gated ion channels, including Nav, Cav, and Kv channels. Mutations in these channels can cause Long QT Syndrome (LQTS) which increases the risk for ventricular fibrillation and sudden cardiac death. Polyunsaturated fatty acids (PUFAs) have emerged as potential therapeutics for LQTS because they are modulators of voltage-gated ion channels. Here we demonstrate that PUFA analogues vary in their selectivity for human voltage-gated ion channels involved in the ventricular action potential. The effects of specific PUFA analogues range from selective for a specific ion channel to broadly modulating all three cardiac ion channels (NaV, CaL, and IKs). In addition, PUFA analogues do not modulate these channels through a shared mechanism. Our data suggest that different PUFA analogues could be tailored towards specific forms of LQTS, which are caused by mutations in distinct cardiac ion channels, and thus restore a normal ventricular action potential.

An efficient pipeline for brain-wide imaging and morphological reconstruction of individual neurons, including long-range projection neurons, is presented along with a searchable database containing more than 1,000 fully reconstructed neurons in the mouse neocortex, hippocampus, thalamus, and hypothalamus.

The stability of social relationships is important to animals living in groups, and social network analysis provides a powerful tool to help characterize and understand their (in)stability and the consequences at the group level. However, the use of dynamic social networks is still limited in this context as it requires long-term social data and new analytical tools. Here, we study the dynamic evolution of a group of 29 Guinea baboons using a dataset of automatically collected cognitive tests comprising more than 16M records collected over 3 years. We first built a monthly aggregated temporal network describing the baboon's co-presence in the cognitive testing booths. We then used a null model, considering the heterogeneity in the baboons' activity, to define both positive (association) and negative (avoidance) monthly networks. We tested social balance theory by combining these positive and negative social networks. The results showed that the networks were structurally balanced and that newly created edges also tended to preserve social balance. We investigated several network metrics to gain insights into the individual level and group level social networks long-term temporal evolution. A measure of similarity between successive monthly networks was able to pinpoint periods of stability and instability and to show how some baboons' ego-networks remained stable while others changed radically. Our study confirms the prediction of social balance theory but also shows that large fluctuations in the numbers of triads may limit its applicability to study the dynamic evolution of animal social networks. In contrast, the use of the similarity measure proved to be very versatile and sensitive in detecting relationships' (in)stabilities at different levels. The changes we identified can be linked, at least in some cases, to females changing primary male, as observed in the wild.

This is part 4 of an intermittent yet always enjoyable series:

• Part 1: the rise of the ancient Puebloan civilization in the American Southwest from 10,000 BC to 750 AD.

• Part 2: the rise and collapse of the ancient Puebloan civilization from 750 AD to 1350 AD.

• Part 3: a simplified model of civilizational collapse.

This time let’s look at the collapse of Greek science and resulting loss of knowledge!

The Antikythera mechanism, found undersea in the Mediterranean, dates to somewhere between 200 and 60 BC. It’s a full-fledged analogue computer! It had at least 30 gears and could predict eclipses, even modelling changes in the Moon’s speed as it orbits the Earth.

What Greek knowledge was lost during the Roman takeover? We’ll never really know.

They killed Archimedes and plundered Syracuse in 212 BC. Ptolemy the Fat—”Physcon” —put an end to science in Alexandria in 154 BC with brutal persecutions.

Contrary to myth, Library of Alexandria was not destroyed once and for all in a single huge fire. The sixth head librarian, Aristarchus of Samothrace, fled when Physcon took over. The library was indeed set on fire in the civil war of 48 BC. But it seems to have lasted until 260 AD, when it basically lost its funding.

When the Romans took over, they dumbed things down. In his marvelous book The Forgotten Revolution, quoted below, Lucio Russo explains the evil effects.

Another example: we have the first four books by Apollonius on conic sections—the more elementary ones—but the other three have been lost.

Archimedes figured out the volume and surface area of a sphere, and the area under a parabola, in a letter to Eratosthenes. He used modern ideas like ‘infinitesimals’! The letter was repeatedly copied and made its way into a 10th-century Byzantine parchment manuscript. But this parchment was written over by Christian monks in the 13th century, and only rediscovered in 1906.

There’s no way to tell how much has been permanently lost. So we’ll never know the full heights of Greek science and mathematics. If we hadn’t found one example of an analogue computer in a shipwreck in 1902, we wouldn’t have guessed they could make those!

And we shouldn’t count on our current knowledge lasting forever, either.

Here are some more things to read. Most of all I recommend this book:

• Lucio Rosso, The Forgotten Revolution: How Science Was Born In 300 BC And Why It Had To Be Reborn, Springer, Berlin, 2013. (First chapter.)

Check out the review by Sandro Graffi (who taught me analysis when I was an undergrad at Princeton):

• Sandro Graffi, La Rivoluzione Dimenticata (The Forgotten Revolution), AMS Notices (May 1998), 601–605.

Only in 1998 did scholars get serious about recovering information from the Archimedes palimpsest using ultraviolet, infrared and other imaging techniques! You can now access it online:

• The Archimedes Palimpsest Project.

Here’s a good book on the rediscovery and deciphering of the Archimedes palimpsest, and its mathematical meaning:

• Reviel Netz and William Noel, The Archimedes Codex: Revealing the
Secrets of the World’s Greatest Palimpsest, Hachette, UK, 2011.

Here’s a video:

• William Noel, Revealing the lost codex of Archimedes, TED, May 29, 2012.

Here are 9 videos on recreating the Antikythera mechanism:

• Machining the Antikythera mechanism, Clickspring.

The Wikipedia articles are good too:

• Wikipedia, Antikythera mechanism.

• Wikipedia, Archimedes palimpsest.

• Wikipedia, Library of Alexandria.

As reported by Ars Technica:

The US Food and Drug Administration this week released an important health warning that everyone should heed: drinking bleach is dangerous—potentially life-threatening—and you should not do it. The warning may seem unnecessary, but guzzling bleach is an unfortunately persistent problem.

Unscrupulous sellers have sold “miracle” bleach elixirs for decades, claiming that they can cure everything from cancer to HIV/AIDS, hepatitis, flu, hair loss, and more. Some have promoted it to parents as a way to cure autism in children—prompting many allegations of child abuse.

Of course, the health claims are false, not to mention abhorrent. When users prepare the solution as instructed, it turns into the potent bleaching agent chlorine dioxide, which is an industrial cleaner. It’s toxic to drink and can cause severe diarrhea, vomiting, life-threatening low blood pressure, acute liver failure, and damage to the digestive tract and kidneys.

In this week’s warning, the FDA noted that some sellers will warn consumers that vomiting and diarrhea are common but say that those unpleasant effects indicate the solution is “working.”

“That claim is false,” the FDA wrote succinctly.

What kind of society have we evolved that it becomes necessary to warn the public - repeatedly - not to drink bleach because someone has suggested they do so??

The FDA says that the products have been hard to scrub out because of claims on social media, where the drinks are promoted along with false health information. Most of the claims can be traced back to Jim Humble, founder and “archbishop” of the Genesis II Church of Health and Healing, aka “The Church of Bleach.”

Humble has been touting the solution for nearly two decades, referring to it as MMS—Miracle or Master Mineral Solution. (It’s also known as the Miracle Mineral Supplement, the Chlorine Dioxide (CD) Protocol, and Water Purification Solution (WPS).) Humble is a former Scientologist who reportedly claims to be a billion-year-old god from the Andromeda galaxy.

Author(s): Bruno Mera, Krzysztof Sacha, and Yasser Omar

Using ideas from topological materials, researchers propose a way to quantize work.

[Phys. Rev. Lett. 123, 020601] Published Tue Jul 09, 2019

COPENHAGEN—After rebuffing Donald J. Trump’s hypothetical proposal to purchase Greenland, the government of Denmark has announced that it would be interested in buying the United States instead.

“As we have stated, Greenland is not for sale,” a spokesperson for the Danish government said on Friday. “We have noted, however, that during the Trump regime pretty much everything in the United States, including its government, has most definitely been for sale.”..

If Denmark’s bid for the United States is accepted, the Scandinavian nation has ambitious plans for its new acquisition. “We believe that, by giving the U.S. an educational system and national health care, it could be transformed from a vast land mass into a great nation,” the spokesperson said.

Excerpted from The Borowitz Report in The New Yorker.

Recent scholarship suggests that the genomes of those around us affect our own phenotypes. Much of the empirical evidence for such “metagenomic” effects comes from animal studies, where the socio-genetic environment can be easily manipulated. Among humans, it is more difficult to identify such effects given the nonrandom distribution of...

I won't link to reports of his rich people fundraiser, but the dirty little secret of America is that rich people are ridiculously stupid people who support the most ridiculously stupid president. And not in the "oh they just like their tax cuts" sense, which is how the press likes to portray them. In the "god I hope I get to shoot a round a golf with him" kind of way. Rich MAGAs are as absurdly stupid and gross as poor MAGAs, just with bigger credit lines.

Some of the oddballs on the nn-Café are interested in odd-dimensional balls, but here’s a nice thing about even-dimensional balls: the volume of the 2n2n-dimensional ball of radius rr is

(πr 2) nn! \frac{(\pi r^2)^n}{n!}

Dillon Berger pointed out that summing up over all nn we get

∑ n=0 ∞(πr 2) nn!=e πr 2 \sum_{n=0}^\infty \frac{(\pi r^2)^n}{n!} = e^{\pi r^2}

It looks nice. But what does it mean?

First, why is it true?

We can show the volume of the 2n2n-dimensional ball of radius rr is (πr 2) n/n!(\pi r^2)^n/n! inductively. How? It’s enough to show that the volume of the unit 2(n+1)2(n+1)-ball is π/(n+1)\pi /(n+1) times the volume of the unit 2n2n-ball. Wikipedia gives a proof, and Greg Egan recently summarized the argument in a couple of tweets.

The idea is to map the unit 2(n+1)2(n+1)-ball to the unit disk in the plane via a projection ℝ 2n→ℝ 2\mathbb{R}^{2n} \to \mathbb{R}^2. Imagine a 2(n+1)2(n+1)-ball hovering over a disk! I can’t draw it, because the first interesting example is 4-dimensional. But imagine it.

The points over any point in the disk at distance rr from the origin form a 2n2n-ball of radius 1−r 2\sqrt{1 - r^2}. We can get the volume of the 2(n+1)2(n+1)-ball by integrating the volumes of these 2n2n-balls over the disk.

That’s the idea. The rest is just calculus. Let the volume of the unit 2n2n-ball be V 2nV_{2n}. We do the integral using polar coordinates:

V 2(n+1)=∫ 0 1V 2n(1−r 2) n2πrdr V_{2(n+1)} = \int_0^1 V_{2n} (1 - r^2)^n \; 2 \pi r d r

Make the substitution u=1−r 2u = 1 - r^2, so du=−2rdrd u = -2 r d r. Some minus signs and 2’s cancel and we get

V 2(n+1)=πV 2n∫ 0 1u ndu=πn+1V 2n V_{2(n+1)} = \pi V_{2n} \int_0^1 u^n \, d u = \frac{\pi}{n+1} V_{2n}

as desired!

So that’s simple enough: since V 0=1V_0 = 1 we get

V 2n=π nn! V_{2n} = \frac{\pi^n}{n!}

And maybe that’s all that needs to be said. But suppose we follow Dillon Berger and sum the volumes of all even-dimensional balls of radius rr. We get

∑ n=0 ∞V 2nr 2n=∑ n=0 ∞(πr 2) nn!=e πr 2 \sum_{n=0}^\infty V_{2n} r^{2n} = \sum_{n=0}^\infty \frac{(\pi r^2)^n}{n!} = e^{\pi r^2}

This looks cute, but what does it mean?

It seems odd to sum the volumes of shapes that have different dimensions. But we do it in classical statistical mechanics when considering a system of particles with a variable numbers of particles: a so-called ‘grand canonical ensemble’, like an open container of gas, where molecules can flow in and out.

If we have a single classical harmonic oscillator, with momentum pp and position qq, its energy is

12(p 2+q 2) \frac{1}{2}(p^2 + q^2)

where I’m choosing units that get rid of distracting constants.

If we impose the constraint that the energy is ≤E\le E for some number EE, the state of the oscillator is described by a point (p,q)(p,q) with

p 2+q 2≤2E p^2 + q^2 \le 2 E

This is a point in the disk of radius r=2Er = \sqrt{2E}.

Now suppose we have an arbitrary collection of such oscillators—and we don’t know how many there are! Then the state of the system is described first by a natural number nn, the number of oscillators, and then momenta p 1,…,p np_1, \dots, p_n and positions q 1,…,q nq_1, \dots, q_n obeying

p 1 2+q 1 2+⋯+p n 2+q n 2≤2E p_1^2 + q_1^2 + \cdots + p_n^2 + q_n^2 \le 2 E

In other words, it’s a point in the union of all even-dimensional balls of radius r=2Er = \sqrt{2E}.

Physicists would call this union the ‘phase space’ of our collection of oscillators. Its volume is the sum of the volumes of the balls:

∑ n=0 ∞V 2nr 2n=e πr 2=e 2πE \sum_{n=0}^\infty V_{2n} r^{2n} = e^{\pi r^2} = e^{2 \pi E}

Actually physicists would include the constants that I’m hiding, and this is actually somewhat interesting. For example, to make the area 2-form dp∧dqd p \wedge d q dimensionless, they divide it by Planck’s ‘quantum of action’. This is what allows them to add up volumes of balls (or other symplectic manifolds) of different dimensions and get something that doesn’t break the rules of dimensional analysis. But this also brings Planck’s constant into a question of classical statistical mechanics—a surprise, but a surprise that’s well-known among experts: it shows up in computations such as the entropy of an ideal gas.

And indeed, entropy is exactly what I’m interested in now! Why would a physicist care about the volume of phase space for a collection of arbitrarily many oscillators with total energy ≤E\le E? Here’s why: if all we know about this collection of oscillators is that it’s energy is ≤E\le E, its entropy is just the logarithm of this phase space volume! So, it’s

ln(e 2πE)=2πE \ln (e^{2 \pi E}) = 2 \pi E

Nice!

Physicists will not like how I tucked all the constants under the rug, getting a formula for entropy that’s missing things like Boltzmann’s constant and Planck’s constant, as well as the mass of the particles in our harmonic oscillators, and their spring constant. I leave it as a challenge to restore all these constants correctly. I suspect that the 2π2\pi will get eaten by the 2π2 \pi hiding in Planck’s constant ℏ=h/2π\hbar = h / 2\pi. This I know: we will get something proportional to EE, but with units of entropy.

This is what I’ve got so far. It’s not yet a new proof that the volumes of all even-dimensional balls of radius rr sum to

e πr 2 e^{\pi r^2}

but rather a physical interpretation of this fact. Maybe someone can keep the ball rolling….

From techdirt:

Recently, the North Carolina State Board of Elections asked suppliers of electronic voting machines a simple question: who owns you?..

This seems like very basic information -- information the Board should know and should be able to pass on to the general public. After all, these are the makers of devices used by the public while electing their representatives. They should know who's running these companies and who their majority stakeholders are. If something goes wrong (and something always does), they should know who's ultimately responsible for the latest debacle.

It's not like the state was asking the manufacturers to cough up code and machine schematics. All it wanted to know is the people behind the company nameplates. But the responses the board received indicate voting system manufacturers believe releasing any info about their companies' compositions will somehow compromise their market advantage.

Hart InterCivic, a corporation that derives independent actual value from this information not being generally known or readily ascertainable and makes reasonable efforts to maintain the secrecy of this information, requests that it be designated as a trade secret pursuant to G.S. § 132-1.2(1)d. and G.S. § 66-152(3).

One of ES&S's subsidiaries (and there are at least 39 of those) -- Meritage Homes Corp. -- shuffled some securities ownership the same day the North Carolina election board asked it to provide information about the company's ownership. Maybe it's a coincidence. Or maybe ES&S was offloading a politically-inconvenient owner. Whatever the case is, it certainly doesn't look good. 

More at the link, via BoingBoing.  Informed discussion at the Technology subreddit.

Cartoon from XKCD.

Nature Physics, Published online: 05 August 2019; doi:10.1038/s41567-019-0617-2

Synchronization can induce both order and disorder, betraying a multistability that is rife in living systems. Evidence now suggests that the circadian clock synchronizes with the cell cycle, and that this behaviour is common to different species.

For much of the last 25 years, a huge question hanging over the field of fundamental physics has been that of what judgement results from the LHC would provide about supersymmetry, which underpins the most popular speculative ideas in the subject. These results are now in, and conclusively negative. In principle one could still hope for the HL-LHC (operating in 2026-35) to find superpartners, but there is no serious reason to expect this. Going farther out in the future, there are proposals for an extremely expensive 100km larger version of the LHC, but this is at best decades away, and there again is no serious reason to believe that superpartners exist at the masses such a machine could probe.

The reaction of some parts of the field to this falsification of hopes for supersymmetry has been not at all the abandonment of the idea that one would expect. For example, today brings the bizarre news that failure has been rewarded with a $3 million Special Breakthrough Prize in Fundamental Physics for supergravity. For uncritical media coverage, see for instance here, here, and here.

Some media outlets do better. I first heard about this from Ryan Mandelbaum, who writes here. Ian Sample at the Guardian does note that negative LHC results are “leading many physicists to go off the theory” and quotes one of the awardees as saying:

We’re going through a very tough time… I’m not optimistic. I no longer encourage students to go into theoretical particle physics.

At Nature, the sub-headline is “Three physicists honoured for theory that has been hugely influential — but might not be a good description of reality” and Sabine Hossenfelder is quoted. At her blog, she ends with the following excellent commentary:

Awarding a scientific prize, especially one accompanied by so much publicity, for an idea that has no evidence speaking for it, sends the message that in the foundations of physics contact to observation is no longer relevant. If you want to be successful in my research area, it seems, what matters is that a large number of people follow your footsteps, not that your work is useful to explain natural phenomena. This Special Prize doesn’t only signal to the public that the foundations of physics are no longer part of science, it also discourages people in the field from taking on the hard questions. Congratulations.

In related news, yesterday I watched this video of a recent discussion between Brian Greene and others which, together with a lot of promotional material about string theory, included significant discussion of the implications of the negative LHC results. A summary of what they had to say would be:

• Marcelo Gleiser has for many years been writing about the limits of scientific knowledge, and sees this as one more example.
• Michael Dine has since 2003 been promoting the string theory landscape/multiverse, with the idea that one could do statistical predictions using it. Back then we were told that “it is likely that this leads to a prediction of low energy supersymmetry breaking” (although Dine soon realized this wasn’t working out, see here.) In 2007 Physics Today published his String theory in the era of the Large Hadron Collider (discussed here), which complained about how “weblogs” had it wrong that string theory had no relation to experiment. That piece claimed that
  

  A few years ago, there seemed little hope that string theory could make definitive statements about the physics of the LHC. The development of the landscape has radically altered that situation.

  and that

  The Large Hadron Collider will either make a spectacular discovery or rule out supersymmetry entirely.

  Confronted by Brian with the issue of LHC results, Dine looks rather uncomfortable, but claims that there still is hope for string theory and the landscape, that now big data and machine learning can be applied to the problem (for commentary on this, see here). He doesn’t though expect to see success in his lifetime.

• Andy Strominger doesn’t discuss supersymmetry in particular, but about the larger superstring theory unification idea, tries to make the case that it hasn’t been a failure at all, but a success way beyond what was expected. The argument is basically that the search for a unified string theory was like Columbus’s search for a new sea route to China. He didn’t find it, but found something much more exciting, the New World. In this analogy, instead of finding some tedious reductionist new layer of reality as hoped, string theorists have found some revolutionary new insight about the emergent nature of gravity:
  

  I think that the idea that people were excited about back in 1985 was really a small thing, you know, to kind of complete that table that you put down in the beginning of the spectrum of particles…

  We didn’t do that, we didn’t predict new things that were going to be measured at the Large Hadron Collider, but what has happened is so much more exciting than our original vision… we’re getting little hints of a radical new view of the nature of space and time, in which it really just is an approximate concept, emergent from something deeper. That is really, really more exciting, I mean it’s as exciting as quantum mechanics or general relativity, probably even more so.

  The lesson Strominger seems to have learned from the failure of the 1985 hopes is that when you’ve lost your bet on one piece of hype, the thing to do is double down, go for twice the hype…

Update: The Breakthrough Prize campaign to explain why supergravity is important despite having no known relation to reality has led to various nonsense making its way to the public, as reporters desperately try to make sense of the misleading information they have been fed. For instance, you can read (maybe after first reading this comment) here that

Witten showed in 1981 that the theory could be used to simplify the proof for general relativity, initiating the integration of the theory into string theory.

You could learn here that

When the theory of supersymmetry was developed in 1973, it solved some key problems in particle physics, such as unifying three forces of nature (electromagnetism, the weak nuclear force, and the strong nuclear force)

Update: On the idea that machine learning will solve the problems of string theory, see this yesterday from the Northeastern press office, which explains that the goal is to “unify string theory with experimental findings”:

Using data science to learn more about the large set of possibilities in string theory could ultimately help scientists better understand how theoretical physics fits into findings from experimental physics. Halverson says one of the ongoing questions in the field is how to unify string theory with experimental findings from particle physics and cosmology…

Update: Physics World has a story about this that emphasizes the sort of criticism I’ve been making here.

As mentioned in the comments, I took a closer look at the citation for the prize. The section on supersymmetry is really outrageous, using “supersymmetry stabilizes the weak scale” as an argument for SUSY, despite the fact that this has been falsified by LHC results.

Update: Jim Baggott writes about this story and post-empirical science here.

Noah Smith here gets the most remarkable aspect of this right. String theory has always had the feature that the strings were not supposed to be visible at accessible energies, so not directly testable. Supersymmetry is quite different: it has always been advertised as a directly testable idea, with superpartners supposed to appear at the electroweak scale and be seen at the latest at the LHC. Giving a huge prize to a theoretical idea that has just been conclusively shown to not work is something both new and outrageous.

Update: Tommaso Dorigo’s take is here, which I’d characterize as basically “any publicity is good publicity, but it’s pretty annoying the cash is going to theorists for failed theories instead of experimentalists”(he does say he wanted to entitle the piece “Billionaire Awards Prizes To Failed Theories”):

[Rant mode on] An exception to the above is, of course, the effect that this not insignificant influx of cash and 23rd-hour recognition has on theoretical physicists. For they seem to be the preferred recipients of the breakthrough prize as of late, not unsurprisingly. Apparently, building detectors and developing new methods to study subnuclear reactions, which are our only way to directly fathom the unknown properties of elementary particles, is not considered enough of a breakthrough by Milner’s jury as it is to concoct elegant, albeit wrong, theories of nature. [Rant mode off]

Going back to the effect on laypersons: this is of course positive. Already the sheer idea that you may earn enough cash to buy a Ferrari and a villa in Malibu beach in one shot by writing smart formulas on a sheet of paper is suggestive, in a world dominated by the equation “is paid very well, so it is important”. But even more important is the echo that he prize – somewhere by now dubbed “the Oscar of Physics” – is having on the media. Whatever works to bring science to the fore is welcome in my book.

Sensory neurons have recently emerged as critical mediators of immunity. Cohen et al. (2019) demonstrate that peripheral neurons utilize reflex arcs in order to rapidly condition the immune response in skin adjacent to the site of infection. This nerve reflex arc generates anticipatory immunity for more effective elimination of the pathogen if later exposed.

Blip glitches are short noise transients present in data from ground-based gravitational-wave observatories. These glitches resemble the gravitational-wave signature of massive binary black hole mergers. Hence, the sensitivity of transient gravitational-wave searches to such high-mass systems and other potential short duration sources is degraded by the presence of blip glitches. The origin and rate of occurrence of this type of glitch have been largely unknown. In this paper we explore the population of blip glitches in Advanced LIGO during its first and second observing runs. On average, we find that Advanced LIGO data contains approximately two blip glitches per hour of data. We identify four subsets of blip glitches correlated with detector auxiliary or environmental sensor channels, however the physical causes of the majority of blips remain unclear.

The neuron is the fundamental unit of computation in the nervous system, and different neuron types produce different temporal patterns of voltage fluctuations in response to input currents. Understanding the mechanism of single neuron firing patterns requires accurate knowledge of the spatial densities of diverse ion channels along the membrane. However, direct measurements of these microscopic variables are difficult to obtain experimentally. Alternatively, one can attempt to infer those microscopic variables from the membrane potential (a mesoscopic variable), or features thereof, which are more experimentally tractable. One approach in this direction is to infer the ionic densities as parameters of a neuronal model. Traditionally this is done using a Multi-Objective Optimization (MOO) method to minimize the differences between features extracted from a simulated neuron's membrane potential and the same features extracted from target data. Here, we use Convolutional Neural Networks (CNNs) to directly regress generative parameters (e.g., ionic conductances, membrane resistance, etc.,) from simulated time-varying membrane potentials in response to an input stimulus. We simulated diverse neuron models of increasing complexity (Izikivich: 4 parameters; Hodgkin-Huxley: 7 parameters; Mainen-Sejnowski: 10 parameters) with a large range of variation in the underlying parameter values. We show that hyperparameter optimized CNNs can accurately infer the values of generative variables for these neuron models, and that these results far surpass the previous state-of-the-art method (MOO). We discuss the benefits of optimizing the CNN architecture, improvements in accuracy with additional training data, and some observed limitations. Based on these results, we propose that CNNs may be able to infer the spatial distribution of diverse ionic densities from spatially resolved measurements of neuronal membrane potentials (e.g. voltage imaging).

The idea that brain activity can impact the fate of the progeny goes against a central tenet of biology. Posner et al. describe an RNA-based mechanism for how neuronal responses to environmental cues can be translated into heritable information that affects the behavior of progeny.

Ethan Steinberg points to a new article by Saul Justin Newman with the wonderfully descriptive title, “Supercentenarians and the oldest-old are concentrated into regions with no birth certificates and short lifespans,” which begins:

The observation of individuals attaining remarkable ages, and their concentration into geographic sub-regions or ‘blue zones’, has generated considerable scientific interest. Proposed drivers of remarkable longevity include high vegetable intake, strong social connections, and genetic markers. Here, we reveal new predictors of remarkable longevity and ‘supercentenarian’ status. In the United States, supercentenarian status is predicted by the absence of vital registration. The state-specific introduction of birth certificates is associated with a 69-82% fall in the number of supercentenarian records. In Italy, which has more uniform vital registration, remarkable longevity is instead predicted by low per capita incomes and a short life expectancy. Finally, the designated ‘blue zones’ of Sardinia, Okinawa, and Ikaria corresponded to regions with low incomes, low literacy, high crime rate and short life expectancy relative to their national average.

In summary:

As such, relative poverty and short lifespan constitute unexpected predictors of centenarian and supercentenarian status, and support a primary role of fraud and error in generating remarkable human age records.

Supercentenarians are defined as “individuals attaining 110 years of age.”

I’ve skimmed the article but not examined the data or the analysis—we can leave that to the experts—but, if what Newman did is correct, it’s a great story about the importance of measurement in learning about the world.

Many large proteins suffer from slow or inefficient folding in vitro. Here, we provide evidence that this problem can be alleviated in vivo if proteins start folding co-translationally. Using an all-atom simulation-based algorithm, we compute the folding properties of various large protein domains as a function of nascent chain length, and find that for certain proteins, there exists a narrow window of lengths that confers both thermodynamic stability and fast folding kinetics. Beyond these lengths, folding is drastically slowed by non-native interactions involving C-terminal residues. Thus, co-translational folding is predicted to be beneficial because it allows proteins to take advantage of this optimal window of lengths and thus avoid kinetic traps. Interestingly, many of these proteins' sequences contain conserved rare codons that may slow down synthesis at this optimal window, suggesting that synthesis rates may be evolutionarily tuned to optimize folding. Using kinetic modelling, we show that under certain conditions, such a slowdown indeed improves co-translational folding efficiency by giving these nascent chains more time to fold. In contrast, other proteins are predicted not to benefit from co-translational folding due to a lack of significant non-native interactions, and indeed these proteins' sequences lack conserved C-terminal rare codons. Together, these results shed light on the factors that promote proper protein folding in the cell, and how biomolecular self-assembly may be optimized evolutionarily.

A current challenge of neurotechnologies is the development of speech brain-computer interfaces to restore communication in people unable to speak. To achieve a proof of concept of such system, neural activity can be investigated in patients implanted for clinical reasons while they speak. Using such simultaneously recorded audio and neural data, decoders can be built to predict speech features using features extracted from brain signals. A typical neural feature is the spectral power of field potentials in the high-gamma frequency band (between 70 and 200 Hz), a range that happen to overlap with the fundamental frequency of speech. Here, we analyzed human electrocorticographic (ECoG) and intracortical recordings during speech production and perception as well as rat microelectrocorticographic (micro-ECoG) recordings during sound perception. We observed that electrophysiological recordings, obtained with different recording setups, often contain spectrotemporal features of the sound, especially within the high-gamma band. Further analysis and in vitro replication suggest that these correlations are caused by a microphonic effect, transforming sound vibrations into an undesired electrical noise that contaminates the biopotential measurements. This study does not question the existence of relevant physiological neural information underlying speech production or sound perception in the high-gamma frequency band, but alerts on the fact that care should be taken to evaluate and eliminate any possible acoustic contamination of neural signals to investigate cortical dynamics underlying speech production and auditory perception.

Many problems in biology and medicine have a control component. Often, the goal might be to modify intracellular networks, such as gene regulatory networks or signaling networks, in order for cells to achieve a certain phenotype, such as happens in cancer. If the network is represented by a mathematical model for which mathematical control approaches are available, such as systems of ordinary differential equations, then this problem might be solved systematically. Such approaches are available for some other model types, such as Boolean networks, where structure-based approaches have been developed, as well as stable motif techniques. However, increasingly many published discrete models are mixed-state or multistate, that is, some or all variables have more than two states, and thus the development of control strategies for multistate networks is needed. This paper presents a control approach broadly applicable to general multistate models based on encoding them as polynomial dynamical systems over a finite algebraic state set, and using computational algebra for finding appropriate intervention strategies. To demonstrate the feasibility and applicability of this method, we apply it to a recently developed multistate intracellular model of E2F-mediated bladder cancerous growth, and to a model linking intracellular iron metabolism and oncogenic pathways. The control strategies identified for these published models are novel in some cases and represent new hypotheses, or are supported by the literature in others as potential drug targets.