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Narendra Bharathi
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Mysterious wave patterns in biology - demystified

The mathematician Gheorghe Craciun recently visited U.C. Riverside, and he told me something amazing. All the proteins in your body are made of peptides - strings of amino acids. Biologists have made big databases of peptides. If you graph how many peptides there are as a function of mass, you get the curves shown here.

The number of peptides generally grows as a function of mass, since there are more ways to string together a lot of amino acids than just a few. But the number also waves up and down! The waves are almost the same in human peptides, mouse peptides and yeast peptides.

What causes these waves?

Gheorghe Craciun and his colleague Shane Hubler figured it out. It turns out to be a math puzzle with a beautiful answer. Read the whole story at my blog:

This is the puzzle: how many ways can you write the mass of a peptide as a sum of the masses of the 20 amino acids found in nature? (In this puzzle you have to keep track of the order of the masses you're adding up.)

This puzzle, while solved, leads to some other math puzzles that seem even more exciting to me. One of them - in the comments to my blog article - asks if the wavelength of these waves is a robust purely mathematical phenomenon. Does it depend a lot on the precise masses of the amino acids in nature, or there something pretty general going on here?

In other words: to what extent are the wave patterns in peptide masses built into the structure of pure math?


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Biology as Information Dynamics

I'm giving a talk at the Stanford Complexity Group this Thursday afternoon, April 20th. If you're around - like in Silicon Valley - please drop by! It will be in Clark S361 at 4:20 pm.

Here's the idea. Everyone likes to say that biology is all about information. There's something true about this - just think about DNA. But what does this insight actually do for us? To figure it out, we need to do some work.

Biology is also about things that make copies of themselves. So it makes sense to figure out how information theory is connected to the 'replicator equation' — a simple model of population dynamics for self-replicating entities.

To see the connection, we need to use relative information: the information of one probability distribution relative to another, also known as the Kullback–Leibler divergence. Then everything pops into sharp focus.

It turns out that free energy — energy in forms that can actually be used, not just waste heat — is a special case of relative information Since the decrease of free energy is what drives chemical reactions, biochemistry is founded on relative information.

But there's a lot more to it than this! Using relative information we can also see evolution as a learning process, fix the problems with Fisher's fundamental theorem of natural selection, and more.

So this what I'll talk about! You can see slides of an old version here:

but my Stanford talk will be videotaped and it'll eventually be here:

You can already see lots of cool talks at this location!


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The search for dark matter

In South Dakota, in a town named Lead, there was a gold mine.  Now it's abandoned.   But at the bottom of this mine, more than a mile underground, there sits a one-meter-tall, 12-sided container.  It contains 370 kilograms of a noble gas chilled to liquid form.  Liquid xenon!  

It's called the Large Underground Xenon experiment, or LUX.  It's been looking for particles that could explain dark matter.   If such a particle interacts with a xenon atom, LUX can detect it. 

Of course, we also need to distinguish these particles from other things. Xenon, a gas at room temperatures, chilled to liquid form, is a great choice here.  For one thing, it's self-shielding!  Xenon is so dense that most gamma rays and neutrons don't get through more than a few centimeters of the stuff.  But it's perfectly transparent to ordinary light... so if a dark matter particle hits an atom of xenon in the middle of the tank, LUX will see a flash of light.  It can also detect electrons that shoot out from the collision.

Four other experiments had reported hints of dark matter particles about 5 times heavier than a proton.  But LUX is much more sensitive!

The LUX team, with over a hundred scientists, has been looking for dark matter since 2014.  Ten days ago they announced their results: no dark matter particles seen.

This "non-discovery" is actually an important discovery.  The most popular theory of dark matter - that it consists of weakly interacting massive particles - has taken a serious hit. 

We now know that if these hypothetical particles, affectionately called WIMPs, are responsible for most of the dark matter and have a mass between 1/5 and 1000 times the mass of a proton, they must be very, very unwilling to interact with ordinary matter. 

There's no rule saying particles have to interact with ordinary matter.  So, we can't rule out such WIMPs, but they're looking less plausible.  People are getting more interested in other theories:

1) theories with very light WIMPs, such as axions or new kinds of neutrinos

2) theories with very heavy WIMPs, jokingly called WIMPzillas

3) theories where dark matter consists of large objects such as black holes.

In case you're wondering whether dark matter really exists: there's so much evidence for this that very few scientists question it anymore.

Theory 3) is getting a lot of attention, because the gravitational wave detector called LIGO is now able to detect black hole collisions!  It's seen two collisions so far, and the first one involved black holes that seem quite strange, not like the ones we know.  They might be primordial black holes, left over from the early Universe.   Perhaps dark matter consists of primordial black holes!

More on that later.  For now, try these.  The new announcement from the LUX team is here:

For how the LUX detector works, read this nice article:

For a nice intro to the LUX results by Ethan Siegel, on a website that requires you to look at ads, try this:

For primordial black holes as dark matter, try this:

The picture is from this article:


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What does one name an English language parsing model, built with an open-source neural network framework implemented in #TensorFlow that provides a foundation for Natural Language Understanding systems? Parsey McParseface, of course!

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A climate hero

This is Alberto Behar in Greenland with the robotic boat he designed.  How fast is Greenland melting due to global warming?  Where does the water go?  Some people sit around and argue.  Others go and find out.

It was very warm in Greenland from July 11th to 13th, 2012.  Scientists from NASA traveled by helicopter to study the melting ice.  They mapped rivers and streams over 5400 square kilometers of Greenland.   They found 523 separate drainage systems - small streams joining to form larger streams and rivers.

The water in every one of these flowed into a moulin!  A moulin is a circular, vertical shaft.  Water pours down the moulin and goes deep below the surface - sometimes forming a layer between ice and the underlying rock.  This layer can help glaciers slide down toward the ocean.  And this water reaches the ocean fast. 

In the area they studied, a total of between 0.13 and 0.15 cubic kilometers of water were flowing into moulins each day.  That's a lot!  That would be enough to drain 2.5 centimeters of water off the surface each day. 

To study the flow of water, Alberto Behar designed two kinds of remotely controlled boats.  One was a drone boat that measured the depth of the water and how much light it reflected, allowing the researchers to calibrate the depth of the surface water from satellite images. They used this boat on lakes and slow-flowing rivers.  But for dangerous, swift-flowing rivers, Behar developed disposable robotic drifters that measured the water's velocity, depth and temperature as they swept downstream.

Just a few days ago, Alberto Behar died in a plane crash.  The plane he was flying crashed shortly after he took off from a small airport near NASA’s Jet Propulsion Laboratory in Pasadena, California. 

So, his coauthors dedicated their paper on this research to him.  Here is is:

• Laurence C. Smith et al, Efficient meltwater drainage through supraglacial streams and rivers on the southwest Greenland ice sheet, Proc. Nat. Acad. Sci.,

Check out the cool images and maps.

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