Dad can affect baby’s health

I saw this blog post referencing an article I wrote for the Raleigh News & Observer.

 I really just wanted to make 3 comments on it:

1) The blog post really goes where I never ever expected it to go, which is why I had to share it.

2) The author uses my first name. It appears only women are prone to this infantilizing treatment. Yay.

3) It would have been really cool to have made this one research paper part of a longer story explaining epigenetics. I think a good percentage of newspaper readers (sadly not 100…) get genetics, and epigenetics would blow many of their socks off. If they are above a certain age (not very old!) they certainly wouldn’t have learned it in high school and perhaps not even in college.

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Political Scientists

I’m going to be catching up on responding to articles I’ve had a chance to read in the last year, but haven’t had a chance to properly digest. This article by one of my instructors at the Santa Fe Science Writing Workshop, Cory Dean, is spot on:

“In a telephone interview, Dr. Ehlers, a Michigan Republican who retired this year, said he thinks a kind of “reverse snobbery” keeps researchers out of public life. “You have these professors struggling to write their $30,000 grant applications at the same time there are people they would never accept in their research groups making $100-million decisions in the National Science Foundation or the Department of Energy,” he said. He said it was “shortsighted” of the science and engineering community not to encourage “some of their best and brightest” into public life.”

All too true. And scientists have other gifts, aside from technical expertise, sorely lacking in the political sphere: patience. Scientists have the ability to see nuance in arguments – an argument is not necessarily wrong because it comes from the other side of the aisle. Further arguments are multi-faceted, and one part of an argument could be wrong, part could be right, and part live in the “we don’t have evidence” land. Speaking of evidence, scientists love finding the facts, and are patient in getting to the bottom of them. The oft-true stereotype of a political campaign: truthiness.

“Alan I. Leshner, a psychologist who heads the American Association for the Advancement of Science, agreed. He recalled learning as a young scientist in the 1960s that people who engaged in issues outside the lab “were wasting time and a sellout.” Young researchers today want their work to be “relevant, useful and used,” he said, but “they still get that message from their mentors.”

In other words, as long as your doing your fair share in the lab, do what you want! Just realize your advisor might grumble a bit. But it doesn’t mean they will ruin your career or even give you a lukewarm recommendation. If you keep up with your passions outside of research, some might even begin to admire it.

And on a similar note:

“Some researchers are concerned that if they leave the lab, even briefly, they will never be able to pick up the thread of their technical careers. But Dr. Foster said he had had no shortage of interesting job opportunities in science after his two years in Congress. And, he added, such risks were built into public service.”

A classic experiment, explained

Suppose you send a loved one on a small errand. Take out the trash. If you are concerned about his or her propensity to dawdle, it’s easy to calculate his or her average speed to acquire solid evidence. Just take the distance to the curb, multiply by 2 to account for the “to” and “from” portions of the trip. Divide this result by the total time it took and you’ll get the average speed. (If the result is comparable to the land speed of a box turtle, you are in the clear to berate your loved one.) In 1849, Armand Fizeau used this same principle to calculate the speed of light directly: He reflected light off of a mirror a fixed distance away and timed the round trip. This was the first measurement of the speed of light on Earth.

While Fizeau used the same method to calculate the speed of light, it wasn’t quite so easy. Light zooms by with a speed many times faster than a person could even hope to muster. So if you sent light back-and-forth from your house to the curb, the event would happen too quickly for you to measure with your stopwatch. One thing Fizeau realized was that in order to have any chance at measuring the speed, the distance traveled would have to be as great as possible in order to maximize the time. Fizeau chose two hilltops about 8.5 km apart, resulting in 17 km total distance. (For the Francophile reading this, they were hills near Paris, and one was Montmartre.)

However, even though this distance was large, a stopwatch still couldn’t handle the timing job. Ironically, the watchmakers of the 19th century inspired an elegant solution: it is easy to rotate a cog at a fixed rate. A cog was positioned between the light source and the mirror. The light beam was positioned to go through the gap between two teeth. If the cog is still, the beam can travel to the mirror, bounce off, come back through the same gap, and be projected onto a screen. Now imagine turning the cog at a steady rate. The beam goes through the gap. Suppose the cog rotates halfway to the next gap in the time it takes the light to travel back. The returning beam is then blocked by the tooth next to the original gap, resulting in a dark projection. Suppose you increase the turning rate so that the cog rotates fully to the next gap. Then the returning beam will sail back through untouched, and the projection will be bright.

Fizeau used this exact logic to make his measurement. He varied the rotation rate of the cog and looked at the intensity of projected light on the screen. The brightest projection meant the rotation rate was in its sweet spot: the cog had moved exactly from one gap to the next in the time it took the light to return. The rotation rate that corresponded to the brightest projection was 25.2 rotations per second, or about 0.04 seconds per rotation. The particular cog he used had 720 teeth. So the time it took the light to travel out of one gap and go back through the next was 0.04 seconds / 720 ~ 0.000055 seconds, truly incredible precision for that time! He divided the known distance by this time, and managed to get the speed of light to within 5% of today’s accepted value.

NY = LA, more or less?

This was an exercise (at least the first few paragraphs were) for the Santa Fe Workshop I attended in May.

Geoffrey West wants to let us in on a little-known tidbit. New York and Los Angeles are basically the same city. You probably think that’s preposterous. New York is the land of the subway, L.A. is the land of the freeway. New York has city lights in the nights, L.A. has sunshine-drenched days. They’re different.

West audaciously gives us an additional morsel. Their sameness can be mathematically proven. Now you’re tempted to use this paper to line the birdcage. How could something as human as a city, in all its qualitative complexity, be quantitatively characterized?

Inhabiting the halls of the prestigious Santa Fe Institute, Dr. West is truly 70 years young. He’ll elegantly answer a simple question in no less than five minutes, never pausing for a breath, his natural British charm turned up all the way. He started out as a particle physicist, but for the most recent act of his long career, decided to take on a surprising challenge. West wanted to know if we could use principles of physics to study cities. In other words, blurring our eyes to the cultural specifics, can cities be described by an equation?

West believes they can. West argues that the math describing cities shows remarkable parallels to the math describing biological organisms, a framework he also helped to develop. And while they are similar, they are also distinct in an important and tantalizing way. West hopes this picture will one day help us plan better cities, prevent crime, and achieve sustainability.

The mathematical framework to describe both systems is called scaling.
Scaling, as a scientific term, has a precise and technical meaning, though it’s quite understandable. Imagine you have a stack of $1 bills. You don’t know how much it is worth. But you know that if you have a stack twice as high, it will be worth twice as much. This is a simple example of scaling, usually called “proportional” or “linear” scaling.

There are other kinds of scaling as well. A 5 pound chicken has a daily (resting, not active) calorie requirement of about 100 calories per day. You might expect a 50 pound bulldog then to require 1000 calories per day. In actuality, the bulldog only requires about 550 calories per day. Similarly, the proverbial 500 pound gorilla will require about 3000 calories, not the 10,000 you might expect based on the chicken’s numbers. It’s not the simple linear scaling from the stack of bills, but is quite predictable. (You can take the ratio of 3000 to 550 and see it’s about the same as 550 to 100). Looking into the numbers more deeply, notice that the chicken requires about 20 calories per pound, while gorilla only requires about 6 calories per pound. The gorilla is more efficient than the chicken! West calls this an “economy of scale” in that bigger is better, using resources more conservatively. Countless biological systems follow a similar trend.

Interestingly, cities exhibit this same economy of scale. This “sublinear” scaling manifests itself in both actual jungles and urban jungles. The caveat is this only works for the impersonal elements of a city, for example the number of gas stations or square miles of pavement. As the population of a city increases, the number of gas stations also increases, but at a slower rate. In parallel to the gorilla, you need less resources per person as a city grows. Cities, compared to rural areas, are more efficient. “Cities are green,” as West likes to say.

However, when we consider things dominated by human interactions, we get “superlinear” behavior, which is the tantalizing difference between cities and nature. Back to the money stack. Imagine your stack doubles in size, but the value has actually increased by more that, someone put a few twenties in there. Examples of data exhibiting this kind of scaling include positive things like patent production per capita and average personal wealth but also include bad things like the murder rate. So cities are more productive, in good and bad ways. West remains optimistic though, “while cities are the source of many problems, they are ultimately the source of the solutions, too.”

To illustrate this superlinear scaling further we return to our cities. The New York and L.A. metro areas have roughly the same population. For the sake of argument, we’ll round both to 15 million people. The scaling argument then predicts both will have about the same per capita income. The data support this to within a few percent. By the same argument, Atlanta, 1/3 the size, will have a per capita income about ¼ as high (math details eliminated). Again, this is true to within a few percent! West and his colleagues have extended this analysis to over 300 US metropolitan areas: they all follow this same trend. The trend holds for all of the 4 variables they measured, so if you tell me the size of your city, the trend can tell you (to within a few percent), the gross production, the number of patents produced per year, the crime rate, and the per capita income.

While the data basically follow this trend, it’s a slightly fuzzy swarm of points, due to the few percent deviations mentioned before. For gross product, New York follows the trend line almost exactly. The other 3 variables also show little deviation. So strangely, New York is an “average” city. L.A. underperforms slightly for its size, having 5% less gross product than expected. West thinks these deviations, which are surprisingly constant over many decades, give a hint at the true characters of cities.

All of this analysis has revealed 5 “families” of cities that depart from the (scaling) norm in similar ways. West suggests that perhaps this taxonomy will help us in the future: similar cities may need similar solutions. Let’s return to the beginning of this article. According to West’s research, a slightly revised, more accurate statement might be: New York is just a swollen version of San Francisco, Seattle, or surprisingly, Indianapolis.