A brief Coronavirus update

A guestblogger at Watts Up With That, who himself survived the infection, has a news-packed update. Read the whole thing, but perhaps the most important paragraphs are:

Transmission route is either contact or inhalation […] The significant inhalation route is now shown by both the Diamond Princess cruise ship experiment (more below) and by the fact that ordinary surgical masks proved ineffective in the Wuhan hospital setting (JAMA, previous post).

Incubation period is 7-10 days from initial infection. The good news is that the 14-day quarantine adopted pretty much universally last week should therefore be effective […] Wuhan then makes a now well-established clinical bifurcation. In 75-80% of cases, by symptom day 10 there is a normal ‘corona cold’ recovery lasting a few days. (In my own case last week, 3 recovery days in total, days 9-12 from symptom onset.) In 20-25% of cases, by symptom day 10 Wuhan progresses to lower respiratory tract pneumonia, where death may occur with or without ICU intervention. The percentage of these deep pneumonias that are viral as opposed to a secondary bacteria infection is not known, but the NEJM clinical case report from Washington State discussed in the following paragraph strongly suggests viral (like SARS), not secondary [opportunistic] bacterial [infection] treatable with antibiotics.

The bad news is that Wuhan IS transmissible during some later part of the symptomless incubation period. […]

And here is some good news:

The new NEJM [New England Journal of Medicine] case report is so important it is summarized here because it leads to a hopeful culminating section below. The Seattle Wuhan case evidenced x-ray diagnosed lower respiratory tract pneumonia from days 9-11 from symptom onset. Supplemental oxygen was started day 9. IV antibiotics were started day 10 to no effect, so discontinued after one day. Importantly (more below), experimental antiviral remdesivir started day 11 by IV under a compassionate use exception, and the deep viral pneumonia fully resolved (per x-ray diagnosis) within 24 hours!

Remdesivir was developed by Gilead Scientific as an antiviral for Ebola and Marburg viruses, but was subsequently found to be active against other single-stranded RNA viruses.

Based on this, China has announced a full-scale random double blind placebo controlled trial in 761 patients. As of this writing China reports successful synthesis of sufficient remdesivir active, so human testing begins today.

PS: A friendly writer sent me this rather more worrying analysis arriving at a comparatively high basic reproduction number R0 of the virus: https://www.medrxiv.org/content/10.1101/2020.02.07.20021154v1

The "perfect Aryan poster baby" was actually Jewish

[repost from my Facebook writer page]

The cover of the January 1935 issue of “Sonne ins Haus” (“sun in the house”, a Nazi magazine for mothers) featured the winner of the “most beautiful Aryan baby” photo contest.

Cover of “Sonne ins Haus”, January 1935

There was only one problem with the undeniably beautiful baby Hessy Levinsons: she was Jewish.
When her mother Pauline had taken Hessy to Hans Ballin’s photography studio for a baby portrait, the photographer had asked her if he could enter said portrait for the “most beautiful Aryan baby” contest. Pauline, flustered, felt obliged to inform the photographer that both parents were non-Aryan. The photographer’s answer: “I know. I want to make the Nazis look lächerlich” [ridiculous].

Recounting the story 80 years later, Hessy Levinsons Taft, now a chemistry professor emeritus at St. John’s University in New York, says she can laugh about it now, but realizes she might not have been alive today if the Nazis had known.

As it happens, following her father arrest and brief imprisonment in 1938, the Levinsons got the message and fled to France. After the Nazi invasion, they made it to Nice in the unoccupied zone (a.k.a., “Vichy France”). In 1941 the husband was able to bribe a Cuban consular official for visas, and with that visa they were presumably able to get a transit visa to Portugal, as they traveled to Lisbon shortly after. In 1942, they were finally able to make it to Havana, where Hessy and her sisetr Naomi attended a British school .
Come 1949, the family relocated one last time to New York City, where Hessy attended a more sciences-oriented high school and immediately was hooked on chemistry. She studied the subject at Columbia and stayed on for her doctorate, during which she met her husband, a mathematics instructor and future professor Earl J. Taft, as in “Taft-Hopf algebra”.)

The exigencies of raising small children made her leave the lab for a while, but she did continue working in science, just on the educational rather than the research side: she oversaw the development of the AP Chemistry test at Educational Testing Services in Princeton, NJ. Later she did return to research, now focusing on water treatment and sustainable water supply. Here is a very recent review article that she co-authored on the subject: http://doi.org/10.1021/acssuschemeng.8b05859

Would an editor chide me for putting this “unrealistic” story in a novel? Possibly, since unlike history, fiction has to make sense. She isn’t the only “Aryan poster boy/girl” used by the Nazis who was Jewish in whole or in part, BTW: Werner Goldberg, the “Ideal Wehrmacht Soldier” whose picture was used for recruiting posters, had a Jewish father. [I will cover his story in a future post.]

Let’s raise a glass wishing Hessy many more healthy and fulfilling years. Ad meah ve’esrim!

[For further reading: http://www.bild.de/regional/berlin/adolf-hitler/berliner-juedin-hessy-taft-war-hitlers-propaganda-baby-36611794.bild.html (in German) and https://cen.acs.org/articles/92/i36/Hessy-Taft.html (in English) If you do not read German fluently, check out the amazing Deep Learning-based machine translator DeepL]

UPDATE: Welcome, Instapundit readers! An alert commenter there points out that “Sonne ins Haus” predates the Third Reich and wasn’t originally a Nazi periodical — merely Gleichgeschaltet [literally: “switched in line”, idiomatically: “made to conform”] after the National Socialist takeover.

I was unable to find any online pre-1933 issues, but it seems the owner of the publishing house was a Leipzig-based entrepreneur named Kurt Herrmann (German wikipedia page). Summarizing in translation, Herrmann was a close friend of Hermann Göring [y”sh] and even acted as a witness at Göring’s remarriage to Emmy Sonneman. Already wealthy, he leveraged his pull with the Nazi top to enrich himself enough through forced “Aryanizations” — the forced sale of Jewish-owned firms to new “Aryan” owners for a tiny fraction of their value[*] — that he became the richest man in Leipzig. Near the end of the war, her fled to Liechtenstein. His firm was expropriated after the war by the Communist East German regime and Gleichgeschaltet for the second time. Herrmann himself got off lightly in his denazification trial, being classified only as category 4: Mitlaufer (fellow traveler).

[*] One of these plundered firms was the venerable sheet music publisher C. F. Peters, then owned by Henri_Hinrichsen (Hamburg 1868—Auschwitz 1942). After the war, the Communist East German government expropriated the Leipzig firm again and ran it as a state enterprise; Hinrichsen’s sons Max and Walter, who had set up the London and New York branches of the company, recreated the private company in Frankfurt. After German reunification, the company was reunited as well.

Book of note: “The Personalized Diet” by Eran Segal and Eran Elinav

I have blogged earlier about the book by neuroscientist Sandra Aamodt and have discussed there in passing the pioneering work by Weizmann Institute scientists Eran Segal and Eran Elinav on the individual microbiome (our “gut bacteria population”) and how it affects blood sugar levels. Now the duo has teamed up with editor Eve Adamson, and together they have put out a popularized book:

I am familiar with some of the original papers in top scientific journals—the book is of course much more readable, and the authors and editors have done a good job of presenting their work in lay language while preserving the broad strokes of their work.

The bottom line of their research is this: each of us carries a whole ecosystem of bacteria in our intestines, which help us digest and absorb food. The specific mix of bacteria varies between individuals, and hence so do our responses to different foods. While weight gain/loss is best seen as an outcome—one aspect of overall health—glycemic response, the changes in blood sugar levels after a meal (“postprandial glucose response”) are sufficiently rapid that they can be monitored in real time (e.g. with a continuous glucose monitor) and correlated with what the person ate (logged in a smartphone app). Doing this for thousands of people is a big-data project par excellence, and this is how computer scientist Segal teamed up with gastroenterologist Elinav.

But this isn’t where it ends. Gut bacteria populations can of course be obtained from stool samples, and subjected to analysis—another aspect of the massive big-data puzzle. Moreover, some of what they infer from the data can be checked in an animal model—for instance, certain gut bacteria can be administered to sterile mice and their weight gain (or lack thereof) in response to certain food mixtures tested on a much shorter time scale than would be possible in slow, relatively large, and long-lived mammals like us.

The duo brought different, complementary perspectives to the problem, not just scientifically but personally. Elinav always loved to take machines apart and see how they fit together (fittingly, he did his military service aboard a submarine), then became fascinated with living organisms. He ended up studying medicine, then specializing in internal medicine. During his residency, he was exposed to the human suffering caused by “metabolic syndrome” (the term given to the combination of severe obesity, adult-onset diabetes, fatty liver, hyperlipidemia, and the complications thereof). He realized that they spent all their time as doctors dealing with the consequences and complications rather than with the root cause.

Segal, on the other hand, was an avid long-distance runner in his spare time. He started experimenting with different nutritional approaches to improve his endurance as a runner, assisted in this pursuit by his wife, a clinical dietitian. As he dove deeper into this and observed diets of fellow runners, it became increasingly clear to him that there was no one-size-fits-all, and that recommendations that were held to be gospel truth (or Torah from Sinai, in our case) were, in fact, counterproductive for some. Why do some runners who eat dates before a run become energized and others exhausted? Who do some do best with carb-loading, and indeed thrive on high-carb diets, while others quickly pack on the pounds and suffer from low energy?

Segal was already involved in the computational study of the human genome at the time and then started reading about the emergent field of study of the microbiome. One thing led to another, a mutual acquaintance put Segal and Elinav in touch with each other, and together they embarked on the collaboration that eventually morphed into the personalized nutrition project.

One factor that facilitated their research was that rapid, reliable, and minimally-invasive blood glucose monitoring technology has become relatively inexpensive. And here some of their first surprises came. Anybody who has followed a Gary Taubes-type diet, or who is trying to manage diabetes, is aware of the ‘glycemic index’ (GI) of foods—the increase in blood sugar levels caused by eating a given amount of the food, compared to the same amount of pure glucose (for which GI=100 by definition). But how uniform are these values really?

Segal and Elinav found that the GI for some foods (e.g., bananas) differed very little between their test subjects (say, 60-65), while others (e.g., apples) were all over the place (40-90). Moreover, the variation was not random but correlated with the person.

One would expect glycemic response to go up more or less linearly with the amount of the food consumed was a given. They found that this is indeed true for smaller amounts, but at some point saturation sets in as the body manufactures more insulin, and the glucose response levels off. (This, of course, does not mean you can just eat ten times as much: the insulin will cause the excess energy to be stored as fat!)

More surprising, however, was that higher fat content in the meal on average caused a minor decrease in glycemic response. For a nontrivial number of their participants, eating toast with butter or olive oil actually did less glycemic harm than eating the toast on its own.

Now trying to keep blood sugar levels on a more even keel has two major benefits. In the short term, yo-yoing blood sugar levels lead to a reduction in energy, a feeling of exhaustion as the body pumps out insulin in response to a sugar spike and blood sugar dips. As for the long term: Segal and Elinav found across their sample that glycemic response after habitual meals is strongly correlated with BMI. Keeping blood sugar levels on a more even keel turns out to be a win-win on all counts.

And here’s the catch—”thanks” to our microbiome, glycemic response is highly individual. Segal himself ‘spikes’ after eating rice, while Elinav does not. One person spikes after ice cream, while another does not—and the same person who spikes after an evening snack of ice cream can safely have chocolate instead, go figure.

This addresses a seeming paradox. It’s not that diets don’t work—in fact, many do for some people, though long-term compliance can be an issue—it’s that there is no diet that will work for everyone, or even for most people.

So the next step, then, was to have a computer analyze the data for some of the participants in depth, and have it plan out a personalized diet that would keep blood sugar levels as steady as possible for that patient. Guess what? Yup, you guessed it.

Now some people might be discouraged by the idea of carrying around a blood sugar monitor for two weeks and carefully logging every meal (and physical activity). But once a large enough dataset has been established, and correlated to analyses of the gut flora composition in all the test persons, it becomes possible to predict glycemic responses to different foods with reasonable accuracy based on a bacterial population analysis of stool samples. A startup company named DayTwo is offering to do exactly that. [Full disclosure: I have no financial interest in DayTwo or in any of Drs. Segal and Elinav’s ventures.]

We are at the dawn of a major revolution in healthcare—a shift away from a paradigm of statistical averages to one of detailed monitoring of individual patients. Call it ‘personalized medicine’ or any other buzzword: it does seem poised to radically change healthcare and individual health outcomes for the better.


On dieting, weight, and reductionist fallacies


Sandra Aamodt, the former editor of Nature Neuroscience, presents a TED talk where she explains something counterintuitive: not only do most diets fail to achieve permanent weight loss, but in some cases the rebound actually overshoots, and the diet actually causes a weight gain in the long run.

As she describes it: the hypothalamus of the brain acts as a kind of ‘weight thermostat’ (that would be a barostat? :)) that tries to adjust body weight to within about 10-15 lb of a set weight by sending chemical signals that up- or down-regulate appetite, that speed up or slow down metabolism, etc. If weight drops “too” far below the set point, signals to increase food intake are sent out, and if no food intake ensues (because no food is available, or because the person is dieting), then metabolism is slowed down to reduce the base metabolic rate (i.e., the number of calories your body needs to keep basic functions going at rest). Unfortunately, the “set point” can be ratcheted up but not trivially ratcheted down.

People who think it is all about the pounds (or about the BMI) will find this a depressing message. But this is a classic example of the “reductionist fallacy”: weight or BMI are but. one metric of health among many. There are many others that matter, such as percentage muscle mass, blood sugar at rest, blood pressure, cholesterol, blood oxygen levels,… A person who is technically overweight (i.e., BMI between 25 and 30) but eats healthily, exercises at least 3 times a week, does not smoke, and only drinks in moderation actually has a better health prognosis than somebody who has an “ideal” weight (BMI around 20) but smokes and drinks heavily and never does any exercise.

To be sure, she shows that among people who do not have any of these four healthy habits, an obese person (BMI=30 or higher) has seven times the mortality risk of somebody with an ideal BMI=20.oo. However, for those who do observe all four healthy habits, the mortality risks with normal, overweight, and obese patient differ only by statistical uncertainty.

Does that mean that a morbidly obese person who cannot fit in an airplane seat does not need to go on a diet? Of course, it doesn’t — that is a straw man, and “set point” normally don’t go that high unless pushed there by unhealthy habits or regular binge eating.

But somebody who, well, has a naturally zaftig built is probably better off making a fixed habit of exercise, and to eat ‘smart’, than to go on some extreme low-carb diet. (Full disclosure: I do restrict my carbohydrate intake, but not all the way down to “ketogenic”.)

There is an additional factor here: in recent years we are increasingly aware of the role the microbiome (“gut bacteria”) plays in food absorption, and particularly in sugar absorption. For instance, in this very recent paper: http://dx.doi.org/10.1016/j.cmet.2017.05.002

ABSTRACT: Bread is consumed daily by billions of people, yet evidence regarding its clinical effects is contradicting. Here, we performed a randomized crossover trial of two 1-week-long dietary interventions comprising consumption of either traditionally made sourdough- leavened whole-grain bread or industrially made white bread. We found no significant differential effects of bread type on multiple clinical parameters. The gut microbiota composition remained person specific throughout this trial and was generally resilient to the intervention. We demonstrate statistically significant interpersonal variability in the glycemic response to different bread types, suggesting that the lack of phenotypic difference between the bread types stems from a person-specific effect. We further show that the type of bread that induces the lower glycemic response in each person can be predicted based solely on microbiome data prior to the intervention. Together, we present marked personalization in both bread metabolism and the gut microbiome, suggesting that understanding dietary effects requires integration of person-specific factors.


We are only beginning to understand how human digestion, food absorption, metabolism, and the microbiome interact. Eventually, genome analysis combined with microbiomics will bring us into the personalized nutrition era.


UPDATE: from the same team, a 2014 paper showing that artificial sweeteners induce glucose intolerance by altering the microbiome.  NATURE’s editorial summary in lay language:

We have been using non-caloric artificial sweeteners for more than a century. Today the food industry is using them in ever-greater quantities in ‘diet’ foodstuffs and they are recommended for weight loss and for individuals with glucose intolerance and type 2 diabetes mellitus. Eran Elinav and colleagues show that consumption of the three most commonly used non-caloric artificial sweeteners saccharin, sucralose and aspartame directly induces a propensity for obesity and glucose intolerance in mice. These effects are mediated by changes in the composition and function of the intestinal microbiota; deleterious metabolic effects can be transferred to germ-free mice by faecal transplantation and can be abrogated by antibiotic treatment. The authors demonstrate that artificial sweeteners can induce dysbiosis and glucose intolerance in healthy human subjects, and suggest that it may be necessary to develop new nutritional strategies tailored to the individual and to variations in the gut microbiota.

Of light and banishing SAD

In honor of the holiday (Christmas if you’re a Western Communion Christian, Isaac Newton Day for everyone else), our Beautiful but Evil Space Mistress has a post up about “living in the light”. She mentions some of the more tasteful and tacky Christmas decorations in her neighborhood, but particularly the abundance of light. (Note that all major winter festivals involve light — be it the pagan Julfest, Christian Christmas, or the Jewish Chanukah/Festival of Lights.)

Our BbESM grew up outside Porto, Portugal, with a single 60W incandescent bulb hanging off the ceiling of her room, plus a 30W lampshade — and even that was a luxury by historical standards. In fact, her editor notes that, adjusted for inflation, a given amount of luminosity has gotten a whopping 500,000 times cheaper in the past few centuries. Just in the past few decades alone, we’ve gone from 60W incandescent to 8 W LED for the same luminosity.

Sarah also notes that she suffered from mild SAD (seasonal affective disorder) and hence appreciated the light. Now actually, while incandescents (with their very “reddish” light — not to mention most of their energy output being infrared, i.e., heat) are probably still better than darkness, they do not help a whole lot with SAD except at very high luminosities. Why?

We actually have three types of photoreceptors: rod cells, cone cells in three colors, and ipRGCs (intrinsically photosensitive retinal ganglion cells). The absorption maxima of rod cells (night vision) and cone cells (daytime color vision) are illustrated below:


(Fish and birds have a fourth “color” of cones in the near-ultraviolet region, with an absorption maximum around 370 nm.)

The ipRGC’s task, on the other hand, is not vision per se but the regulation of circadian rhythm. Their pigment, melanopsin, has an absorption maximum around 480nm, in the bluish region. (Mutations in the gene that expresses melanopsin are one cause for SAD.) SAD is a major issue in arctic countries (close to 10% of the population in Finland, for example). The traditional treatment (review article here) involves full-spectrum lamps at high intensity (10,000 lux and more). However, it was recently found that blue-enriched light sources at more modest luminosities of 750 lux — or even narrow-band blue light at just 100 lux — yield equally good results, as they selectively stimulate the ipRGCs.

Merry Christmas, happy belated Chanukah/Festival of Lights, or happy Isaac Newton Day, as applicable!


The “Magical Mystery Chord” finally revealed?

The classic Beatles song, “A Hard Day’s Night”, opens with a complex ringing chord that has had songbooks (and musicians) arguing among themselves for decades. Complicating the answer is that even Paul McCartney can’t exactly remember what was done.

Full disclosure: I relate to the Beatles much the way I relate to Mozart: I recognize their musical genius but much of their most popular music does not ‘move’ me either intellectually or emotionally. But I love a good musical puzzle as much as can be.

In principle, given modern computer technology, the problem of transcribing a piece of music should be simple: digitize the audio, carry out a Fourier analysis, and convert the resulting frequencies to note names. Right?

Well… Feed in unaccompanied flute and this will work fine. (As anybody who’s owned an analog synth knows, a triangle wave is a pretty decent starting point for a flute sounds — and while a triangle does have some harmonics, the fundamental is very strong and there are only odd harmonics so you can tell apart the fundamentals pretty easily from the rest in the Fourier spectrum.) Feed in a Hammond organ with just a single drawbar open: ditto. Feed in a more complex sound but with restricted harmony (e.g., a violin playing only single notes), no problem. Feed in a complex chord played by multiple instruments on top of each other, and things get hairier. Have some of the multiple instruments not be quite in tune, or let some be in equal temperament and others in just intonation, and things gets even worse.

An applied mathematician at Dalhousie University did a Fourier analysis on the opening chord some time ago and turned that into a paper.  Does this sound like an academic with too much time on his hands, “partially supported by a grant from the Natural Sciences and Engineering Research Council of Canada,” no less? Well, to me it sounds like a good “torture test” for the robustness of a musical transcription code. And where it comes to science popularization, this definitely hits the spot with the musically minded: only yesterday I saw another popular article about the now a decade old analysis being linked on Instapundit.

Just retaining all frequencies with relative amplitudes above 0.02 still gave him 48 frequencies, from which he squeezed a solution that looks good in theory but just doesn’t sound “quite right”.

A musical transcription site run by somebody with the delightful pseudonym “Waynus of Uranus” points out a fly in the ointment that people who grew up with digital recording wouldn’t even have thought of. Back in the day, loud bass tones meant pushing against the limitations of vinyl singles and lo-fi audio equipment alike, so the deep end of the bass (about 80 Hz and lower) was routinely rolled off with an equalizer or a highpass filter during mixing or mastering. What this means, for example: if Paul were to strike an open D string on his bass guitar (or an A string at the fifth fret) his fundamental would be below the filter cutoff, and the Fourier spectrum would instead have the second harmonic much stronger — leading to claims like “Paul played a D3 and a soft D2 at the same time”. I know bass players like Geddy Lee or Rush or Steve Harris of Iron Maiden play lots of double-stops, but this really is a progressive rock or metal thing to do, not a pop thing.

Applied mathematician Kevin Houston takes it from there and digs further in a very geekish way. While the original record was mono, it turns out there is a stereo mix made for the movie—and in the early days of stereo, it was not unusual for recording engineers to just put some instruments all left and others all right, with the vocal in the center. (This is, pretty much, how I used to jam along with Deep Purple records: Jon Lord’s organ and Ritchie Blackmore’s guitar were usually at opposite end of the stereo image, so you could single out their parts by listening to one stereo channel at a time.) In the stereo

In the stereo mix of AHDN, Paul (bass) and George (12-string guitar) are off to one side, and John (acoustic guitar) off to the other, together with producer George Martin on piano. Better still: after subtracting the left channel from the right (i.e., “phase-inverting”), it becomes clear that the acoustic is playing an Fadd9 chord. (That means: an F major chord with an added ninth, a.k.a. a “Steely Dan chord“. It differs from a major ninth chord F9 in that the seventh is omitted.)

To cut a very long story short (some mathematicians can get quite verbose ;)), this is the solution (which relies on a good dose of Occam’s razor/the Law of Parsimony as well):

  • Paul just plays a low D2, but because of EQing off the deep end, the D3 overtone/second harmonic comes through louder than the fundamental, hence the acoustic illusion that the bass note played is D3
  • John plays F2 A2 F3 A3 C4 G4 (in standard tuning, frets 1-0-3-2-1-3)
  • George plays the same chord, but on a 12-string in standard tuning—where the bottom four “courses” have the second string one octave higher. Hence aside from the slight tuning discrepancy with John, he adds F4 A4 as new pitches
  • Finally, George Martin on the piano, with the sustain pedal down, plays D2 G2 D3 G3 C4, which one could call a Gsus4/D chord. Sympathetic resonance from the undamped piano strings adds the wash of low-level extra pitches that befuddles the Fourier analysis.

Not only does this not require attributing instrumental acrobatics to the Beatles that are out of character for them, but actually playing those notes on the respective instruments does produce a sound quite like the record. (Listen at 7:17 in the video below.)

Kevin and his collaborators could not readily find an electric 12-string, so they simulated that by layering two six-string electric chords: once fretted 1-0-3-2-1-3, the second time 13-12-15-14-1-2 with an extra hand. “Fake Nashville Tuning“, if you like.)

If this isn’t  the solution, it sounds much closer than anything else I’ve heard. Enjoy the above video!