I have put up a few posts warning readers to be careful of confusing PCA plots with real genetic variation. PCA plots are just ways to capture variation in large data sets and extract out the independent dimensions. Its great at detecting population substructure because the largest components of variation often track between population differences, which consist of sets of correlated allele frequencies. Remeber that PCA plots usually are constructed from the two largest dimensions of variation, so they will be drawn from just these correlated allele frequency differences between populations which emerge from historical separation and evolutionary events. Observe that African Americans are distributed along an axis between Europeans and West Africans. Since we know that these are the two parental populations this makes total sense; the between population differences (e.g., SLC24A5 and Duffy) are the raw material from which independent dimensions can pop out. But on a finer scale one has to be cautious because the distribution of elements on the plot as a function of principal components is sensitive to the variation you input to generate the dimensions in the first place.

I can give you a concrete example: me. I showed you my 23andMe ancestry painting yesterday. I didn’t show you my position on the HGDP data set because I’ve shared genes with others and I don’t want to take the step of displaying other peoples’ genetic data, even if at a remove. But, I have reedited some “demo” screenshots and placed where I am on the plot to illustrate what I’m talking about above. The first shot is my position on the two-dimensional plot of first and second principal components of genetic variation from the HGDP data set.

No surprise that I’m in the Central/South Asian cluster. But what may surprise you is that I’m not in the South Asian cluster, I’m in the Central Asian cluster. In the Central Asian cluster are Uyghurs and Hazaras. These are two hybrid populations, a mixture of West and East Eurasian elements. The Uyghurs are likely the outcome of a process of admixture between the Iranian and Tocharian Indo-European populations of the cities of the Tarim basin, and later Turkic speaking settlers who arrived in the wake of the expansion and later collapse of the first Uyghur Empire (the historical connection between the current Uyghurs and ancient Uyghurs is tenuous at best, and complicated). The Hazaras are a more recent population, likely emerging as the product of intermarriages between Mongol soldiers who arrived in the 13th century, and indigenous women, Persians, Turks, and assorted Indo-Iranian groups between the Zagros and Khyber Pass. It is somewhat ironic that I’m on the edge of the Hazara cluster since they are almost certainly in part descended from Genghis Khan’s family, and my own surname is Khan. But I know that my Y chromosomal lineage is R1a1, very common across Central and Southern Eurasia, and not a Mongolian one at all.

Zoom! Now we’ve constrained the input data set to the Central/South Asian groups. First, look at the Kalash. They’re strange, which is no surprise, they’re an inbred mountain group in Pakistan who have not adopted Islam. The Pakistani Taliban looks to be ending them as we speak. I really would prefer that they were just thrown out of the data set for this zoom view, because on this fine grained scale I don’t think they add much at all. They’re just an example of what long term endogamy can do to your allele frequencies. The bigger picture is the axis between the populations of Pakistan, and those of Central Asia. Observe that I’ve changed position. Whereas when taking world wide genetic variation into account I clustered with Central Asians, now I’m 2/3 of the way to the South Asian cluster. I will tell you that I’ve shared “genes” with around 50 South Asians now, from various parts of the subcontinent, and in the 23andMe plot they overlay the South Asians nearly perfectly. I’ve put labels at the approximate ethno-linguistic position. I’m an outlier. 23andMe tells me that I’m 43% “East Asian.” The typical South Asian is in the 10-30% range. My first assumption was that I have a lot of ancient South Indian, which just shows up as East Asian in their algorithm. With this in mind I tried sharing with a lot of South and East Indians, and found out two interesting points. First, South Indians seem no higher than 30-35% East Asian. Bengalis on the other hand are more East Asian, with Bangladeshis more East Asian than West Bengalis. My sample size for Bengalis is small, so take that with caution. Second, the PCA plots put the South Indians firmly in the South Asian cluster, but the Bengalis trail out toward my own position. This indicates again that different methods are telling you slightly different things. The PCA is only a thin slice of variation, but it’s highly informative of between population differences. A Bengali and a South Indian with the same “East Asian” fraction in the ancestry painting nevertheless have consistently different positions on the PCA, with Bengalis closer to the East Asians. Additionally, there’s an ethnic Persian in this zoom plot that I’m describing, and they are positioned near the Balochi. But on the world wide plot they’re on the margins of the European cluster. Another illustration that position of an element is sensitive to the input data because of how the dimensions are generated.

Blaine Bettinger, who inspired me to post this, told a story with his ancestry painting which was plausible. What can I say? First, I have less than 1% African ancestry. This could be noise. But, I do observe that the South Asians with Muslim names are enriched in the set of those who I’ve shared genes with and who have less than 1%, but not 0%, African ancestry. Just as Muslim South Asians have non-trivial West Asian ancestry, I suspect that many of us have Sub-Saharan African ancestry through the same dynamic. Sub-Saharan African soldiers were prominent across South Asia with the arrival of Muslims. Bengal even has a period of rule by Abyssinian rulers. But the bigger issue for me is the East Asian component. Here is a figure from a paper published 4 years ago:

The figure is showing Fst value comparing Indian Americans with Europeans and East Asians. Fst measures between population differences in allele frequency, in this case the alleles being 207 indels. Take a look at the Bengalis. These are West Bengalis, who I believe have a lesser East Asian component, but even there the allele frequency difference to East Asians is near that of Europeans. The Assamese, who speak a language very close to Bengali, are similar. Assam was ruled by a Tibeto-Burman people for nearly 600 years. The Oriya speakers, from the southwest of Bengal, are more distant from East Asians. As one goes south and east, and west and north, the distance from East Asians increases. This shouldn’t be that surprising, but nice to confirm. The fact that the genetic distance increases as one goes south means that for northeast South Asia you need to complexify the model from a two-way admixture with “ancient North Indians” and “ancient South Indians.” Set next to these two is an East Asian element, which is also clear in the Indo-Aryan peoples of Nepal.

Of course anyone who knows Bengalis won’t be totally surprised by an East Asian component to their ancestry. To the left are head shots of the two women who have dominated Bangladeshi politics for the past two decades, Khaleda Zia and Sheik Hasina. They’re both Bengalis, but they do look different, and I know many people who look like one or the other (or a combination). My family is from one of most easternmost districts of Bengali, next to Tripura. In fact my late maternal grandmother lived in Tripura for some of her childhood (she was almost trampled to death by the Maharani of Tripura’s insane elephant as a young girl!). When I was a young child I once saw a black and white photo from my father’s college days, and I was curious who the Asiatic looking young man in the middle of the photograph was. Turns out it was my father! Sometimes our expectations affect how we perceive people. I have never perceived my father to have an Asian cast to his features as a more mature man, but others have told me that he does still exhibit them.

There is still the question of how Bengalis came to have this particular admixture. I think the most plausible scenario probably synthesizes conventional village-to-village intermarriage and isolation-by-distance, along with some component of migrationism. Tribes such as the Chakma have left Burma in historical time. The Chakma of Bangladesh now speak a dialect of Bengali, not their ancestral Sino-Tibetan tongue. I believe that a non-trivial portion of Bengalis have ancestors who were tribal people who shifted their religious identity to that of Hinduism or Islam (from Theravada Buddhism in the case of the Chakma, or animism in the case of the Garos before their Christianization). But eastern South Asia is adjacent to mainland Southeast Asia, and it stands to reason that continuous gene flow would over time would also have introduced East Asian alleles into the Bengali gene pool.

Image Credit: TopNews.in

That was not a typo. My friend/part-time stalker Ashley Paramore interviewed me at TAM 8 during the "Skeptics in the Tub" after-hours get-together. So yeah, we’re in swimsuits, and she interviews me. It was my first time being interviewed in a hot tub, so that all by itself is rather exciting to me. For you, seeing me partially naked may curb that somewhat. YMMV.

The background audio is a bit loud, but it’s not too hard to hear us. The red color is due to low light conditions plus the weird lighting outside at the casino.

As usual when someone interviews me, I’m a smartass for the first few minutes, but then we get down to business: my tattoo, Bad Universe, Universe Today, Pamela Gay, and skepticism. I listened to the interview, and I have to say I pontificate an awful lot for a guy standing around in swim trunks. Still, I’m glad I got a chance to say some of that stuff. The last three minutes or so are actually rather important… they’re important issues to me, but also, I think, to the skeptic community at large. And I’ll have more to say on that subject hopefully soon.

Back in May, I gave a talk at the annual Phenomenology Symposium at UW Madison, showing some of the first physics results from the CMS experiment. At that point we had a data sample of proton-proton collisions corresponding to 1 inverse nanobarn.

This past weekend the LHC crossed a major threshold: 1 inverse picobarn delivered to the experiments – a factor of a thousand more collisions. By late next year we are all hoping to have recorded another factor of a thousand, for a total of 1 inverse femtobarn.

In an earlier post I explained these funny units, inverse whatever-barns. The point here, though, is that as we record exponentially greater numbers of collision events, with the proton beam energy 3.5 times greater than that at the Tevatron at Fermilab we will begin to really probe an unexplored mass scale in the search for new particles. What lies there is completely unknown.

So far the LHC experiments CMS and ATLAS have presented results on about a quarter of the data sample recorded so far, at the biennial International Conference on High Energy Physics, held this year in Paris. To sum it up in a sentence, both experiments have rediscovered our familiar standard model friends, among which the W and Z bosons and the top quark are the most massive.

The W and Z are both produced in proton-proton collisions by the collision of quarks with antiquarks. You should visualize the incoming beam protons as being composed not just of two “up” quarks (charge +2/3) and one “down quark (charge -1/3), but as sort of seething, roiling mass of quarks, antiquarks, and gluons. When the protons collide any two of these constituents, if they have enough energy, can annihilate to form a a W or Z boson.

The W and Z are the “carriers” of the weak force in the standard model. For commonplace processes like nuclear decay (like cesium-137, for example) it’s the weak force which allows it to happen. We describe the process as involving a “virtual” W boson which exists fleetingly, by grace of the uncertainty principle, with a mass thousands of times less than its true mass of 80 GeV. It’s this virtuality that makes the weak force weak, in fact, for nuclear processes.

But at the energies of the incoming proton constituents, there is plenty to make real W bosons, and also Z bosons. (We don’t ordinarily see the effects of Z bosons in nuclear processes, because Z’s can only couple a particle to its own antiparticle…) Now, if you have a real W or Z boson sitting there, it will decay in about 10^-23 seconds to a quark and an antiquark or to leptons. In the case of the W, which has electric charge +1 or -1, it decays to a charged lepton and its associated neutrino about 33% of the time, and the rest of the time to quark-antiquark pairs. A Z boson will decay to a charged lepton (e, mu, or tau) and antilepton about 10% of the time, or to a pair of neutrinos about 20% of the time, and the rest of the time to a quark-antiquark pair.

The LHC experiments cannot really see the quark-antiquark decays of the W and Z – there is just too much background from quark-quark, quark-gluon, and gluon-gluon scattering giving two outgoing quarks or gluons. When a quark or gluon emerges sideways from one of these collisions it sort of shatters into a collimated spray of high energy particles that we call a jet. This all is governed by the strong force, which, being stronger than the weak force, has much higher rate than the W- and Z-producing processes.

But, ah, the leptonic decays of the W and Z! The sweetest is the lepton-antilepton decay of the Z. About 7% of the time a Z will decay to an electron-positron pair or a muon-antimuon pair. These particles come screaming out of the collision region into the detector carrying about half the Z’s total mass-energy of 91.2 GeV. This makes them readily identifiable and reconstructible. High energy electrons and muons leave a very straight track in our charged particle tracking system. Electrons then lose all their energy in the dense calorimeter surrounding the tracker. Muons, being 200 times more massive, tend to sail on through the calorimeter and magnet coil out to the muon tracking system that forms the true bulk of the CMS experiment. Here is a cool display of one of the first such events recorded in CMS:

In fact, the astute reader who knows the size of an atomic nucleus will conclude that the muons in the picture above must have traveled straight through quite a number of nuclei to reach the outer parts of the detector! This is because muons interact only via the weak and electromagnetic forces with nuclear matter, and, those forces are quite weak compared with the strong force.

With two muons, one can calculate the mass of the parent particle from which they came, using relativistic formulae. And by ICHEP the CMS experiment had recorded enough muon pair events to make the following beautiful graph showing the spectrum of masses from which opposite sign muon pairs arose. In the plot, at the far right end, the peak from the Z boson at 91.2 GeV is clear as a bell:

At lower masses one can see the peaks from the upsilon (Y), which is a bound state of a bottom and an antibottom quark, the J/psi which is a bound state of charm-anticharm, and lighter resonances. The broad smear of “continuum” muon pair production comes from virtual photons – the electromagnetic interaction.

These data, and similar data from eletron-positron pairs, is extremely important for calibrating the experiment. By measuring the position of the Z peak we can see whether we have properly calibrated our charge particle momentum scale, and then use that to calibrate the calorimeters via the Z to ee signal. The Z is our standard candle here, but as the saying goes, in high energy physics yesterday’s sensation is today’s calibration (and tomorrow’s background).

All these results and more are there for the world to see at the ICHEP web site. There are plenty more results, including the first glimpse of top-antitop events, and the results of some searches for new phenomena.

Nothing startling has come out yet, and we are eagerly awaiting the exponentially growing samples to analyze, with which we will push past the Tevatron sensitivity in a number of areas. But don’t count the Tevatron out just yet! The CDF and Dzero experiments have recorded thousands of times more collisions and results are still pouring out. And, oops, it’s time for me to go to that CDF analysis meeting now…

Listen, people of Earth: Everything’s going to be fine. All we have to do is survive another century or two without self-destructing as a species. Then we’ll get off this rock, spread throughout space, and everything will be all right.

If this is not your idea of “optimism,” then you are not Stephen Hawking. The esteemed physicist garnered headlines, and some eye-rolls, after telling Big Think last week that humanity needs to leave the Earth in the future or face extinction.

He’s not knocking climate scientists’ attempts to figure things out on Earth–he’s just thinking long term. “There have been a number of times in the past when our survival has been touch-and-go,” explains Hawking at Big Think, mentioning the Cuban Missile Crisis, and “the frequency of such occasions is likely to increase in the future…. Our population and our use of the finite resources of the planet earth are growing exponentially along with our technical ability to change the environment for good or ill,” while “our genetic code still caries our selfish and aggressive instincts” [The Atlantic].

Combined with Hawking’s statement earlier this year that it might be dangerous to contact aliens because they could come and wipe us out, the physicist’s latest warning makes it feel like he’s increasingly a member of the gloom-and-doom crowd. But not so. He’s just the kind of person who thinks on the long, long term.

Let’s jump back to another publicly engaged scientist: Carl Sagan’s message in Cosmos that the stars await… if we don’t destroy ourselves.

Sagan was pushing urgency and vigilance, not gloominess. The same, I think, is true of Hawking—it’s why he calls himself an “optimist” despite his dire warnings of treacherous times ahead. Indeed, he says, if humanity can just get past the next 200 years without driving itself to extinction, then we’re good to go. Once we spread to different locations in space, no event contained to a single world—even a catastrophic one like all-out nuclear war or a massive asteroid strike—could do in the species by itself.

Hawking concludes the Big Think message about the necessity of a human future in space by saying, “That is why I’m in favor of manned, or should I say ‘personed’, space flight.” That is: Putting people back on the moon or take them to Mars wouldn’t be just a vainglorious gesture. The next phase of humanity demands it.

He’s far from the only one thinking far into the future. Take DISCOVER blogger Phil Plait, who, in his book Death from the Skies!, discusses audacious plans for our descendants to take way, way down the line to survive the slow dying and then death of the sun. (For a culture so plugged into now, it seems laughable to consider something billions of years down the line. But where Hawking may be proven wrong in his 100-200 years statement, he is clearly correct about the options for humanity’s long-term future: We’ll either leave the Earth or die before we get the chance.)

Or, if you want to go all the way to the far end of the optimism spectrum, take another future-obsessed theoretical physicist: Michio Kaku, whom I interviewed about his TV show Sci-Fi Science for the September issue of DISCOVER, on newsstands now. The outline of a Type I, or global, civilization is now emerging on the Earth, he says, with the Internet and even type I sports—like the FIFA World Cup. And whether or not you agree humans are doomed if they don’t leave the Earth for points beyond, he believes our future is out there.

“It’s not guaranteed we’ll [even] hit Type I,” he says. “But I’m optimistic.”

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Image: NASA

The bigger the fossilized feces the more ancient rain. A team of paleontologists has uncovered this apparent correlation during a study of chinchilla scat at nine sites in South America’s Atacama Desert.

Claudio Latorre Hidalgo of Pontificia Universidad Católica de Chile in Santiago presented his findings on this rainfall metric at a talk held yesterday during the ongoing American Geophysical Union’s Meeting of the Americas. Science News, where we found the story, reports that Latorre Hidaglo looked at fossilized feces from middens–shared rodent poop piles that contain “fecal pellets cemented together by crystallized urine.”

Latorre Hidaglo’s team carbon dated organic bits from the largest twenty percent of the chinchilla pellets (so as to exclude pellets from rodent youth). Given information on rainfall from other sources, they correlated the larger feces with periods of greater rainfall. According to Science News, Latorre Hidaglo suggests that the more rain, the better the environment to support bigger chinchillas; the bigger the chinchillas, the bigger the chinchilla poop. The poop test, the researchers say, may provide a way to estimate past rainfall when other tests aren’t available.

The American Geophysical Union talk announcement advises researchers to keep digging into the middens for more information:

A correlation between the size of rodent droppings and rainfall quantities is enabling researchers to establish a new paleoclimate record. Plus, a study of the contents of middens accumulated long ago by rodents offers further insights into the Atacama’s past.

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Image: wikimedia / Rumpelstiltzkin

P is not equal to NP. Seems simple enough. But if it’s true, it could be the answer to a problem computer scientists have wrestled for decades.

Vinay Deolalikar, who is with Hewlett-Packard Labs, has sent to peers copies of a proof he did stating that P is not equal to NP. Mathematicians are reviewing his work now—a task that could go on for a long time. If he’s correct, Deolalikar will have figured out one of the Clay Mathematics Institute’s seven Millennium Prize Problems, for which they give $1 million prizes. (Grigory Perelman won one of the seven for solving the Poincaré conjecture, but turned down the money last month.)

What’s all the hubbub? First, an explainer:

The P versus NP question concerns the speed at which a computer can accomplish a task such as factorising a number. Some tasks can be completed reasonably quickly – in technical terms, the running time is proportional to a polynomial function of the input size – and these tasks are in class P. If the answer to a task can be checked quickly then it is in class NP [New Scientist].

That definition is pretty abstract, so here’s a more concrete example:

Clay imagines a college housing scenario wherein 400 students have applied for rooms at a college that can only accommodate 100 of them. A selection of 100 students must be paired together in rooms, but the dean of students has a list of pairings of certain students who cannot room together. The total possible number of pairings is ridiculously large — more than the total number of atoms in the universe — but the solutions, i.e. the list of pairings finally provided to the dean, is easy to check for errors: If one of the dean’s prohibited pairs is on the list, that’s an error [AOL News].

Thus, if P were equal to NP, it would mean that problems that are easy to check—like this roommate match-up—must also be easy to solve. But if Deolalikar is correct and in fact P is not equal to NP, as many mathematicians already believed, then that ain’t necessarily so. And that would have practical meaning, according to Michael Sipser of MIT.

Sipser … says that the P-versus-NP problem is important for deepening our understanding of computational complexity. “A major application is in the cryptography area,” Sipser says, where the security of cryptographic codes is often ensured by the complexity of a computational task. The RSA cryptographic scheme, which is commonly used for secure Internet transactions — and was invented at MIT — “is really an outgrowth of the study of the complexity of doing certain number-theoretic computations,” Sipser says [MIT News].

Deolalikar’s proof is now available to read online. New Scientist and Network World report that he pulled together tactics from different disciplines to show that an NP problem—whether a list of statements can be simultaneously correct or contradict one another—is not a P problem, because it can be easily checked but no computer can figure it out quickly from scratch.

In the days since the proof began to spread across the Internet, however, some math bloggers like Scott Aaronson have responded to the proof by saying yes, it’s lovely, but no, it probably isn’t going to stand.

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Image: HP Labs

How can some sleepers doze through anything from the rattle of a jackhammer to the blast of a jet engine? According to a new study, an extra helping of brain activity in the thalamus–a region tied to the senses–may give some people a better chance at blocking sleep-disturbing sounds.

“I hear complaints a lot as a sleep doctor that noises are interrupting people’s sleep all the time,’’ said Dr. Jeffrey M. Ellenbogen, chief of the division of sleep medicine at Harvard Medical School [and co-author of the study]. “What is it in the brain that makes it have less response to noise at night, and how can we enhance that natural occurring brain-based process to help people sleep?” he said. [The New York Times]

Researchers at the Harvard Medical School asked twelve healthy volunteers to spend three nights in a sleep lab. The first night the researchers let them sleep soundly, but monitored their brain activity. The following two nights, they used four speakers aimed at the sleepers’ heads to play sounds of air and car traffic, ringing telephones, and “hospital-based mechanical sounds,” among other things. They found that those people whose thalami produced more high-frequency signals called “sleep spindles” lasted the longest when barraged with noises: the more sleep spindles, apparently, the better the sleep. The study appears today in Current Biology.

The correlation between sleep spindles–so called because the brain wave pattern looks like spindles of thread–and deeper sleep doesn’t necessarily mean causation, but the team suggests that the mechanism that produces the spindles in the thalamus could be “colliding” with the incoming sounds. This would prevent the sensory information from being passed on to the rest of the cortex, and could allow sleepers to get their shut-eye despite a noisy background. The New York Times reports that older people produce fewer sleep spindles, and notes that people often become lighter sleepers as they age. The researchers wonder if the number of spindles may serve as a good prediction for deep sleep capabilities:

In the meantime, testing a person’s spindle activity may help predict an individual’s tolerance to noise, Ellenbogen added. This could help with life decisions, he said, such as: “Should I take the job that puts me in the city, where I’m [in] urban chaos?” [National Geographic]

The researchers also question if this line of research will change how leading sleep medications are manufactured, since sedating the brain (as many current sleep aids do) means sedating the thalamus, the sleep spindle-maker.

“Although our computer vernacular uses ’sleep’ to refer to a process of temporary shut-down, that’s not the way our brain works,” Ellenbogen wrote in an email to Wired.com. “During sleep, our neurons are busy doing very complicated processing, including, this study shows, generating sleep spindles to protect us from being awoken from noises in the environment.” [Wired]

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Image: flickr / davitydave