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The Stem-Cell Breakthroughs That Won the Nobel Prize

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Two scientists who each made a major discovery — four decades apart — share the science world's prestigious award.


On Monday, the Nobel Prize in Medicine was awarded to two biologists for their breakthroughs in the field of stem-cell research — two discoveries that happened 44 years apart. The honors go to Britain's Sir John B. Gurdon and Japan's Shinya Yamanaka for their pioneering work with the life-shaping cells, which can be reprogrammed to create any kind of tissue in the body. Here, a concise guide to Gurdon and Yamanaka's contributions to the field of medicine:

What were they awarded the prize for?

Both discoveries "concern the manipulation of living cells," says Nicholas Wade at The New York Times, which lies "at the heart of the techniques for cloning animals" and curing a wide variety of diseases, including Parkinson's and Alzheimer's. The "primitive cells" are incredibly malleable, and can be programmed to mature into other tissues, including skin, vital organs, and more.

Where do stem cells typically come from?

Embryonic stem cells are usually taken from early-stage human embryos, with the embryos being destroyed in the process. That's why stem-cell research is fraught with religious and moral issues, with critics often arguing that scientists are overstepping their boundaries by manipulating stem cells. The next generation of researchers, building upon the body of work started by Gurdon and Yamanaka, are looking into new techniques that sidestep ethical considerations by taking stem cells from other sources.

Specifically, what kind of work did Dr. Gurdon do?

In 1962, the year Yamanaka was born, Gurdon demonstrated that the DNA in frog tissue could be used to generate a fresh batch of tadpoles, says Karl Ritterlouise Nordstrom of The Associated Press. Gurdon's technique involved extracting the frog's chromosomes from an adult intestinal cell and injecting it into an empty frog egg, which was able to "reprogram" the new nucleus to switch its directive over to tadpole-making. At first his work was "greeted with skepticism," says the Times' Wade, because it "contradicted the textbook dogma" that mature cells are irrevocably set in their specific functions. The process itself was little understood, and it wasn't until more than four decades later in Dr. Yamanaka's labs that the reason behind this reprogramming was finally revealed.

And what did Dr. Yamanaka find?

In 2006, Dr. Yamanaka's research showed that four specific genes control the agents in the egg. Using mice, Yamanaka discovered that mature skin cells could be reprogrammed to become any other kind of cell, which he called inducted pluripotent stem cells (iPS) — basically the equivalent of embryonic stem cells. iPS cells can be taken from adult nerve, heart, or liver cells, and unlike their embryonic cousins, could be taken without destroying human embryos.

What do the scientists get for their discoveries?

Gurdon, 79, and Yamanaka, 50, will share the $1.2 million prize for their work, which the Nobel committee says has "revolutionized our understanding of how cells and organisms develop." In an interview, Dr. Yamanaka said, "My goal, all my life, is to bring this technology... to the bedside, to patients, to clinics." When asked if he planned to celebrate, Dr. Gurdon said he was invited to drinks at 6 o'clock. "I intend to attend those drinks," he dryly told the AP.

Every so often, we'll reprint something from our sister publication, The Week. This is one of those times.

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iStock // Ekaterina Minaeva
Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Name the Author Based on the Character
May 23, 2017
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