New Host!

pair

gmilburn.ca is now on Pair Networks… bear with me as I move everything over.

Things might look a little weird (ie missing thumbnails) for a little while, but don’t worry, I’m working on it Edit: Thumbnails should be taken care of now…

If you posted a comment in the last 12 hours or so it may have been lost – my apologies. There also appear to be a few comments out of order, but I’m working on it.

If you see any horrible errors that I should probably know about, please leave a comment!

Chimpanzees and Neoteny

One of the biggest “human” questions is “where did we come from?”. While the mechanisms of evolution are well established, the route humanity took to get to its present state is not as well detemined. It’s the difference between knowing the rules of chess and being able to figure out the personality and play style of a grandmaster from a few snapshots of a very long game in progress.

One proposed mechanism for the evolution of humans from primates is neoteny, where juvenile traits are retained and adult adaptations lost. This has been observed in foxes subject to behavioural selection. For instance, look at this young chimpanzee.

naef_fig4_baby

This picture is from a 1926 study by the German anthropologist Adolf Naef. He describes it as “the the most human-like picture of an animal, of any that is known to me.” The little guy does seem to have a rather regal and refined air about him, but we can’t just wave our hands and call it case closed at this point. Can we look at the development of a chimpanzee and see if there are any quantifiable parallels?

Bone structure is a great place to start. Chimpanzees, like humans, have a skeleton that changes shape and size as the organism matures.

chimp_human_compare

The two skulls on the far left are those of an infant chimpanzee (top) and an infant human (bottom). Bone structure and shape are very similar, with the classic huge head and tiny cute face we seem programmed to love. The two skulls in the middle are of a adolescent chimpanzee (top) and an adult human (bottom). We can see the jaw start to lengthen in both, and their overall similarity. The final picture on the top right is of an adult chimpanzee, who has a significantly larger and more powerful bite than any adult human.

So what does this show us? Well, humans and chimpanzees appear to have very similar development in terms of bone structure as they grow up, except that humans just seem to… stop at a certain point. There are a multitude of theories as to why this happens, but they all seem to follow the pattern of certain behaviours being selected for which affect the balance of hormones in the body that control the development of adult features. This is called neoteny.

Now neoteny doesn’t mean that every single part of the entire animal becomes more juvenile, or that the animal becomes less complex overall. It’s a selective reduction in complexity – traits that appear later in the animals development (ie adolescence) become less likely to appear.

So how did humans get their unique features? It’s very difficult to select for traits like a bigger brain or hairlessness when those traits don’t appear in the wild in any real frequency to begin with. Viewing human evolution through this lens seems to indicate that change would be very slow, and very hard to do.

chimp_dental

But what if instead of selecting for a simple trait, we (or the species as a whole) selects for a behaviour? The neat thing about selecting for this is that hormones have a strong influence on behaviour. So we are partly selecting for certain hormone levels or actions. These hormones also share logical relationships with other hormones, and act in many different parts of the body, not just the parts of the brain influencing behaviour.

If we put significant selection pressure on a species, we are effectively increasing the mutation rate (ie “mutant” creatures tend to be selected more). Increases in mutation rates would be more likely to affect more logically complex proteins arising later in life involved in the development of adolescent features (due to more references to more parts of the mutating DNA) rather than less logically complex proteins that would be involved in juvenile features.

As a result, we now have a mechanism for how these bizarre traits that we simply don’t see in the wild can become so common, so quickly, and also a predicted side effect – neoteny.

But how could this end up as an advantage? It seems that mutations are destroying those adult adaptations that made the organism successful in the first place. But what if the world changes simply because you and others like you live in it? We like to think of physical strength as the be all and end all of “dominance”, but I think this is only true if you’re “one chimp against the world”. A chimp who can more accurately figure out social structure and how to manipulate his place in it could be far more successful in breeding than a chimp who is simply stronger than average.

A chimpanzee’s ability to learn is drastically reduced upon reaching maturity. But baby chimps…

babymimic

Baby chimps will eagerly mimic a human caretaker – sticking out their tongues, opening their mouth wide, or making their best effort at a kissy face. Not only is the basic mechanism of learning there (imitation), it appears to be very focused on social relationship. And this ability decreases with age! It seems that the retention of juvenile traits is not the burden it appears at first.

So the origin of humanity? Well, it’s still up in the air. But I think it’s incredibly likely that we literally changed ourselves – that living together created environmental pressures (namely social ones) that selected for behaviour in an incredibly complex manner, where the ability to learn and social skills were valued and led to reproductive success. All too often we look for outside pressures in evolution, when some of the most magnificent examples (like the plumage and mating rituals of birds of paradise) are simply a result of everyone agreeing to play an elaborate game.

Clever as a Fox

Sometimes we see things so often that we simply forget to ask “why are they like that?” For instance, let’s take a closer look at domestic animals. Dogs, cats, horses, cows, pigs – animals that we live with, and who couldn’t live without us.

Common Traits

What do all these domestic animals have in common?

pb_pup pb_cat pb_dog
pb_cow pb_horse pb_pig

Now this isn’t a particularly subtle example, but that’s kind of the point. You can see that all of these domestic animals have large white patches – they’ve lost pigment in their coats in some areas. Why do we care? Well, this is something that is extremely common among domesticated animals, but very rare among wild animals. I hear you saying “but what about zebras, or any other wild animal with white patches?”. What we’re referring to here is slightly different. A zebra will always have that patterning, whereas what we’re looking at here is depigmentation – the loss of color in certain areas in an animal that is “normally” colored.

What else is common among domestic animals but rare in the wild? Well, things like dwarf and giant varieties, floppy ears, and non-seasonal mating. Charles Darwin, in Chapter One of Origin of the Species noted that “not a single domestic animal can be named which has not in some country drooping ears”. A very significant observation when you consider that there is only a single wild animal with drooping ears – the elephant.

So perhaps something weird is going on here. Why do animals as different as cats and dogs have these common traits? It seems to arise simply from being around humans!

The Hypothesis

belyaev

The Russian geneticist Dmitri Belyaev provided a very interesting potential explanation. Genetics at the time was preoccupied with easily measurable traits that could be passed on – if you bred dogs, you could pick the biggest puppies, breed them, and they would produce bigger dogs on average. Fine. But that is selection of a single simple trait, something that likely did not require that many genes to “switch” in order for the puppies to be bigger.

But what if you were selecting for something more complicated? What if, instead of selecting for a simple trait like size or eye color, you selected for something more vague like behaviour – in this case, the very behaviour that made these animals more likely to be around humans. We can call it tamability, or lack of aggressiveness, or whatever – the point is, we are selecting for those animals who will behave in a manner we want around us. A wolf who does not display aggressive behaviour might be able to grab a few scraps of food from the garbage pile of a early human settlement, rather than being driven off.

And if we were selecting a complicated behaviour, rather than a simple trait, it seems likely that it will require more change in the animals genetic code. And since the genetic code is a tangled web where a small bit of DNA can be referenced in many areas of the body – perhaps selecting for a common behaviour would also cause other common traits to arise in animals that are otherwise different.

It’s like giving your car a paint job versus trying to make it go faster – the paint job is easy, but trying to make it faster could lead to your car exhibiting other traits you didn’t directly request, like consuming more gas during regular driving. This could be common across all your project cars. One is a low level trait (the paint, the size of puppy) that can be encompassed in a tiny bit of information (color, size), the other is a high level trait (speed, tamability) that must involve a wide variety of sub-systems changing as well.

The Experiment

Now if you were a Soviet scientist in the late 1950s, you probably worked on something awesome like a giant robot that shot nuclear missles, or a flying submarine. Not Dmitri Belyaev. No, he lost his job as head of the Department of Fur Animal Breeding at the Central Research Laboratory of Fur Breeding in Moscow in 1948 because he was committed to the theories of classical genetics rather than the very fashionable (and totally wrong) theories of Lysenkoism.

So instead, he started breeding foxes. Well, it was technically an experiment to study animal physiology, but that was more of a ruse to get his Lysenkoism-loving bosses off his back while he could study genetics and his theories of selecting for behaviour.

fox_1

He started out with 130 silver foxes. Like foxes in the wild, their ears are erect, the tail is low slung, and the fur is silver-black with a white tip on the tail. Tameness was selected for rigorously – only about 5% of males and 20% of females were allowed to breed each generation.

fox_2

At first, all foxes bred were classified as Class III foxes. They are tamer than the calmest farm-bred foxes, but flee from humans and will bite if stroked or handled.

fox_3

The next generation of foxes were deemed Class II foxes. Class II foxes will allow humans to pet them and pick them up, but do not show any emotionally friendly response to people. If you are a cat owner, you would call the experiment a success at this point.

fox_4

Later generations produced Class I foxes. They are eager to establish human contact, and will wag their tails and whine. Domesticated features were noted to occur with increasing frequency.

fox_5

Forty years after the start of the experiment, 70 to 80 percent of the foxes are now Class IE – the “domesticated elite”. When raised with humans, they are affectionate devoted animals, capable of forming strong bonds with their owner.

These “elite” foxes also exhibit domestic features such as depigmentation (1,646% increase in frequency), floppy ears (35% increase in frequency), short tails (6,900% increase in frequency), and other traits also seen frequently in domesticated animals.

The Results

Belyaevn passed away in 1985, but he was able to witness the early success of his hypothesis, that selecting for behaviour can cause cascading changes throughout the entire organism. For instance, the current explanation for the loss of pigment is that melanin (a compound that acts to color the coat of the animal) shares a common pathway with adrenaline (a compound that increases the “fight or flight” instinct of an animal). Reduction of adrenaline (by selecting for tame animals) inadvertently reduces melanin (causing the observed depigmentation effects).

So if Belyaevn is right, genetics is not just a low slow process that works on tiny incremental tweaks. Complicated environmental pressures can result in complicated genetic results, in a stunningly quick period of time. Where do I think we’re going with this?

Well, designer pets for one. Following the collapse of the Soviet Union, the project ran into serious financial trouble in the late 1990s. They had to cut down the amount of foxes drastically, and the project survived primarily on funding obtained from selling the tame foxes as exotic pets. Imagine a menagerie of dwarf exotic animals, who crave human attention and form bonds with people. It would be obscenely profitable.

And the out there thought for the day? We’re doing this to ourselves. We don’t encourage people to act aggressively all day to everyone they meet. We reward certain behaviours more than other behaviours. My unprovable conjecture? Humanity is selecting itself for certain behaviours, and the traits we think of as fundamentally human (loss of hair, retention of juvenile characteristics relative to primates) are a side effect of this self-selection.

Videos

Here are some great videos with footage of the tame foxes.

From NOVA – Dogs and More Dogs (starts at about 17:30)

“Suddenly, it all started to make sense. As Belyaev bred his foxes for tameness, over the generations their bodies began producing different levels of a whole range of hormones. These hormones, in turn, set off a cascade of changes that somehow triggered a surprising degree of genetic variation.

Just the simple act of selecting for tameness destabilized the genetic make up of these animals in such a way that all sorts of stuff that you would never normally see in a wild population suddenly appeared.” (Full transcript)

Great Moments in Bat Science

Space Shuttle Discovery was launched this Sunday. If you look at a picture of liftoff, there appears to be something on the side of the external fuel tank.

shuttle_bat_wide

Upon closer inspection, we find the most adventurous bat in the history of bats.

shuttle_bat

All photo evidence so far indicates that he clung on during liftoff and the ascent above the Kennedy Space Center. I like to think he sacrificed himself for the advancement of bat science.

Koide’s Formula

Finding a beautiful and simple equation for something in the natural world is fascinating to me – it’s like picking at a corner of loose wallpaper in your room and suddenly seeing the scrolling green text of the Matrix on the wall behind it. Often these relations lead to a deeper understanding, but sometimes an indisputably true and simple relation will remain maddeningly confounding.

In 1981 Yoshio Koide was researching leptons, a family of fundamental particles that includes the familiar electron. There are three leptons which are “charge carriers” (they have mass) – the electron, the muon, and the tauon.

Koide was wondering if there was a way to relate the masses of these three particles with one another. He developed the following equation (related to the eigenvectors of the democratic matrix, here’s a review paper if you want more detail):

koide_1

Nothing too wild mathematically here. If we assume our three lepton masses are positive (pretty reasonable) then the value of Q can range from 1/3 (all the masses are the same) to 1 (the masses vary wildly from each other). So what is the value of Q? Well, when Koide first proposed this equation, the masses of the leptons were thought to be as follows:

  • Electron: 0.511 MeV/c2
  • Muon: 105.658 MeV/c2
  • Tauon: 1,784.2 MeV/c2

If we plug these values into Koide’s equation, we get a value of 0.667074 – incredibly close to 2/3, which would be precisely halfway between our upper (1) and lower (1/3) bounds we figured out before! This seems like a ridiculous coincidence.

Things like this make you wonder… well, is it exactly 2/3? Or is it just “kind of” close? The mass of the electron and the muon had been measured to a rather high level of accuracy, but the accuracy of the tauon measurements had been lagging behind due to the higher energies required. Perhaps the measurement of the tauon was wrong! It’s a hell of a hunch – but let’s go with it. Assume that the tauon mass has been measured incorrectly, we can set Q = 2/3, input the masses of the electron and muon, and see what the tauon mass “should” be. It turns out that Koide’s equation says the mass of the tauon “should” be 1777 MeV/c2.

Well that’s wonderful, but nature doesn’t seem to care how you think it “should” behave. The only test was to wait for more accurate measurements of the tauon mass and see if this was a neat coincidence based on measurement error or whether there may be something more interesting going on. The mass of the tauon was later revised with better measurements, and… drumroll…

Old Measurement Koide’s Prediction New Measurement
1,784.2 MeV/c2 1,777 MeV/c2 1,776.9 MeV/c2

Whoa. Our simple little equation, using nothing more than grade school arithmetic, has accurately predicted the mass of a fundamental physical particle years in advance of having this measurement confirmed by the best research labs on earth.

And now the question becomes why – why does this work at all? We have three seemingly random lepton masses, measurements of the most complicated physical system we know – our universe. We then input them into a ridiculously simple equation, and the most ridiculously simple answer pops out.

We can gain a tiny bit of insight by figuring out what exactly this equation is telling us.

koide_cone

Basically, we can calculate Q for a given set of three lepton masses. This Q will tell us where a three-dimensional vector specified by the square roots of our three lepton masses will end up.

Q = 1/3 The set of all vectors that form an angle of zero with the unit vector (multiples of the unit vector).
Q = 2/3 The cone seen above which fits perfectly into the “corner” created by our three axes. The set of all vectors that form an angle of pi/4 with the unit vector.
Q = 1 The set of vectors that form an angle of zero with our basis vectors. These vectors lie along one of our three axes.

So it appears that our lepton masses have been chosen in some magical manner as to fall perfectly in the middle of these two extremes. The concept appeals to our perception of the universe as a finely tuned apparatus, but gets us nowhere closer to an interpretation based in physical reality.

It’s a maddening equation. Beautiful. Simple. True. And no one knows what the hell to do with it.

High Definition Science

I’ve found that the content that really shows off the HDTV format is that of the natural world. While sitcoms might be a bit more clear, the format really shines in situations where the extra detail is actually relevant, like in documentaries such as Planet Earth.

Here’s some of the best free high-definition content I’ve found on the web, if you know of any more please let me know!

Gravitas

Gravitas is a project by John Dubinski of the Department of Astronomy & Astrophysics at the University of Toronto. He works on visualization of galaxy dynamics, and his goal is to “use supercomputer simulations of realistic model galaxies to illustrate these slow and majestic dynamical processes on an accessible timescale and so breathe life into the iconic images of galaxies created by the world’s great telescopes”. He succeeds brilliantly, and has produced a set of captivating animations, some in HD.

Download Future Sky (Quicktime 720p) and Spiral Metamorphosis (Quicktime 1080p).

Fractal Zooms

Eric Bigas has a great website with several fractal animations, including a few in HD.

Cherry Blossom Hexagons is a zoom into a Barnsley fractal, available in 720p XviD or 720p H.264.

19th Hole Terraces is a zoom into a Mandlebrot set, available in 720p XviD or 720p H.264.

Copperplate Chevrons is available in 720p XviD.

Hubble Space Telescope

The European Homepage for the NASA/ESA Hubble Space Telescope has tons of great HD content. Dr. Joe Liske at the European Organization for Astronomical Research in the Southern Hemisphere hosts a video podcast which you can subscribe to in 720p or full HD 1080p. They also have a HD video archive of broadcast quality footage, like this flythrough of the Hubble Ultra Deep Field.

For other HD space videos, NASA has a HD video archive with a section dedicated to Hubble. The Jet Propulsion Laboratory has an HD archive of their own, click “HD” at the bottom to browse.

The Trees of Mars

Science is a far more dynamic process than many realize. The constant upheaval of new measurements and new data forces us to constantly reassess our theories and our very view of how well we know the world.

In 2001, a very interesting image began circulating around the internet. It was of a narrow strip of Mars, captured by the Mars Global Surveyor’s MOC (Mars Orbital Camera, great originality there). It was stored in a large database open to the public, but this image had sat unnoticed next to thousands of others until now.

moc-m0804688-crop

What did we see? These dark blobs were almost a kilometer across. Well, no one really wanted to say. It kind of looked like lichen:

lichen

Or a bacterial colony:

bacteria_plate

Sir Arthur C. Clarke even suggested that they were some sort of Martian banyan tree. “I’m quite serious when I say have a really good look at these new Mars images,” he said. “Something is actually moving and changing with the seasons that suggests, at least, vegetation.”

There’s only one problem with that, and any schoolchild can point it out to you – Mars is supposed to be a dead planet. No life has been found there, at least not that the unwashed masses have been made aware of. The conspiracy theories soon flew fast and thick – that this was one of many images NASA had suppressed that indicated life on Mars.

In short, we were being manipulated by some sort of New World Order that kept knowledge of Martian life silent in order to… well, no one was really clear on that point. Thankfully, the file clerk of this powerful cabal was so incompetent as to leave these blockbuster images on a public server.

But what could it be if it wasn’t life? There are other processes that can produce similar structures:

dla

such as diffusion limited aggregation. So perhaps imminent takeover of the world via suppressed satellite images wasn’t the first thing we should worry about. Maybe there was a less elaborate explanation.

If all we had to base our assumptions on was that single picture, the debate could rage on for a while. Thankfully a new satellite, the Mars Reconissance Orbiter (MRO), entered the skies of Mars in 2006. On the MRO was one crucial piece of equipment – the High Resolution Imaging Science Experiment (HiRISE) camera. This camera was the largest camera ever carried on a deep space mission – to give you an idea of its capabilities, it could see a beachball on the surface of Mars from orbit.

So what did the new pictures look like? Well…

hirise

they certainly weren’t lichen, or bacteria, or banyan trees. These long tortured cracks hundreds of meters long were like nothing ever seen on Earth. What could have caused them? Conventional geologic processes on Earth simply didn’t do things like this.

So what was the alternative? Were we back to thinking it may be life again? Well, perhaps there was an unconventional geologic explanation.

Hypothesis: The [carbon dioxide] seasonal ice in the cryptic terrain is translucent, allowing sunlight to penetrate through the ice to the surface below. The ice then sublimates from the bottom of the slab, eroding channels in the surface below. (H. Kieffer, 2000)

Here’s the idea. Mars is cold. So cold in fact, that in it’s “winter” carbon dioxide will actually freeze into transparent sheets over certain regions of the planet. The key thing here is that the ice is transparent, like black ice on asphalt.

Now, think what happens when bright sun shines on black ice. Where does it start melting from? Well, it doesn’t start from the top like you’d think. The sun shines through the clear ice, heats up the asphalt, and the asphalt melts the ice from the bottom. It might even make tiny river-like channels of water between the ice and asphalt, as the liquid water needs somewhere to go.

But what if you ice isn’t made of water, and instead is made of a gas like carbon dioxide? Suppose the sun shines through the ice, heats up the Martian soil, and starts melting the ice from the bottom. Enormous amounts of gas are produced – but where can it go? Well, first it might start to make little channels under the ice like we thought of before to escape. But if there’s no where to go to, eventually, something has to give.

fracture_jets

And so, screaming with pressure, the ice fractures. Gas rushes out of these many cracks, carrying dust and soil with it. So what do we end up with? A giant circular region with fractures, darkened relative to the rest of Mars by freshly spit up soil and dust.

So a deep dark conspiracy theory? Perhaps not. It may not be Martian trees, but it is an amazing geologic process that has never been observed on Earth.

Elementary Cellular Automata

Simple rules can often give rise to very complex behaviour.

applet

Applet built using Processing, Wolfram CA code example used as base. Source code available upon request. If this applet fails to load or screws up in any other way, please leave a comment with your browser version and operating system – thanks!

What is this?

Imagine we have a line running left to right made up of squares, and these squares can be either white or black. We then want to draw another line directly below this – but how do we do it? What rules should we use?

Well, we could use a very simple rule and just copy it directly.

eca-duplicate

But that gets pretty boring. Maybe we could use a slightly more complicated rule and say if a cell used to be black, it’s now white, and vice versa.

eca-invert

Well, it’s a bit more interesting, but not by much. What is a simple set of rules we can use that will produce more interesting behaviour? Perhaps instead of having a square rely just on the previous one, let’s have the rules depend on the previous square and its neighbors.

eca-concept

This means we’re looking at three squares that can either be black or white. There are eight total possible combinations, shown below.

eca-generic_rules

And we can decide what we want to happen when any of these situations occur. Lets try this set of rules:

eca-example_ruleset

And see what happens when we start with a single black cell.

eca-example

Could be interesting! It’d be a lot easier if we had more lines and squares for us to see a bigger picture of the structure produced by these rules though…

So that’s exactly what the application at the top of the post does. You can select whatever rules you want for the eight possible states, and toggle between a single point and random data to start by clicking on the circle in the top right.

Some Interesting Rule Sets

Since there are eight possible combinations that we can choose to either result in a black or a white square, there are a total of 28 = 256 possible rule combinations. A lot of these rules end up producing patterns that become very similar, and it was found that there are 88 unique rules, depending on your definition of unique. Here are a few interesting ones.

Rule 110

random initial conditions
rule-1
How complex can the behaviour be from these simple rules? Well, you can use this ruleset to simulate a computer. You’ve got to set up the initial line of black and white squares very carefully, and it’s equally hard to read the results, but the logic ends up being exactly the same. If you had a lot of time, you could do some interesting things…

Rule 90

single point initial condition
rule-2
This ruleset produces a fractal – the Sierpinski Triangle – when a single point is used to start.

Rule 30

single point initial condition
rule-3
If you’re wondering if these things have any real world application, this rule set displays chaotic properties and is used as the random number generator for large integers in Mathematica, used by millions worldwide.

Rule 169

random initial conditions
rule-5
I couldn’t find any information online about this, but it certainly stands out visually.

Rule 184

random initial conditions
rule-6
This rule set can be used to model traffic flow, or deposition of particles onto an irregular surface.

TED Talks – Mushrooms Can Save the World

I enjoy TED greatly. The multidisciplinary approach is a perfect way to introduce yourself to new ways of thinking – and thinking in unconventional ways is something Paul Stamets has spent a lifetime doing. Paul loves mushrooms – or more correctly, he loves fungi.

You can also download the MP4, or add the video to iTunes. I’m not going to repeat the content of the video (because you should watch it!) but I am going to highlight a few amazing thing I learned.

Paul Stamets

We are intimately related to fungi. Animals and fungi are part of a larger group called Opisthokonta, that is, we share a common ancestor. The same pathogens that attack fungi attack us – and some of the most promising and effective antibiotics come from fungi. Unlike plants (and like us) fungi inhale oxygen and produce carbon dioxide.

Prototaxite Landscape

Fungi used to rule the earth. There’s a common misconception that first there was life in the oceans, then plants grew around the oceans, and eventually basic animals wandered out somehow. Not true. Fungi were the first organisms to arrive on land, and plants followed several hundred million years later.

Why? Fungi can produce oxalic acid along with many other acids and enzymes in order to grab minerals they need to grow. Where do they get these minerals? Well, as they moved out of the ocean they obtained them from rocks. This slow process of calcium oxalate formation causes rocks to slowly crumble, and is the first step in producing the soil conditions necessary for plant growth.

So what, you may say – just more slime around a very old pond. Well, not true! This is the absolutely mindblowing part, and I was surprised that I’ve never even heard of it before. There are organisms called prototaxites which could reach sizes of up to 1 m (3 feet) across and 8 m (24 feet) high – all during a time when the largest plants were 2 feet tall.

The landscape of this early Earth must have been breathtaking.

Remediation Experiment

The industrial potential of fungi has not yet been realized. Not even close. I think in this part of the talk he starts to run out of time, but what he manages to state is stunning.

Paul was involved in an experiment to gauge the effectiveness of various methods to remove petroleum waste. Four piles of dirt were saturated with hydrocarbons. One was left alone, one was treated with bacteria, the other with enzymes, and Paul’s used fungi (of course). Fast forward six weeks – three of the piles remain “dead, dark, and stinky”, while the fungi-treated pile was covered in hundreds of pounds of oyster mushrooms. Fast forward to eight weeks, polycyclic aromatic hydrocarbon levels (a measure of the level of contamination of the soil) went from 10,000 parts per million to 200. Not to mention that by the end of the experiment the fungi treated pile was the only one covered in grass…

There are more great examples in the talk itself. I strongly recommend giving it a listen or three – it’s about as far from my line of work as you could get, and I found myself absolutely fascinated.