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© Takashi Tsuji, RIKEN

Scientists Grow Hairy Skin in the Lab

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© Takashi Tsuji, RIKEN

Anybody can make skin cells—in fact, you’re doing it right now. But large, transplantable sheets of skin? That’s a bit harder. Still, it’s not impossible: Japanese scientists have successfully grown sophisticated artificial skin, including hair follicles and sweat glands. Their results were published last week in the journal Science Advances

These are heady days for lab-grown tissue. In the last decade, scientists have managed to create petri-dish tear ducts, kidneys, rat legs, muscle and bone, and even beef. All of this is good news, as transplantable organs and other body parts are in high demand, but some have been harder to cook up than others. Skin, for example. 

Your hide may look simple, but there’s actually a lot going on: there are three distinct layers (the epidermis, dermis, and hypodermis, which is composed of fat and connective tissue), sweat glands, and hair follicles. For skin to be skin, it’s got to be functional, lead author Takashi Tsuji said in a press statement. "Up until now, artificial skin development has been hampered by the fact that the skin lacked the important organs, such as hair follicles and exocrine glands, which allow the skin to play its important role in regulation.”

Tsuji and his colleagues decided to start at the very beginning, before skin cells are even skin cells. They used induced pluripotent stem cells (iPS), which are adult cells that have been reprogrammed with the stem-cell-like power to grow into anything.

The researchers used chemicals to activate a gene in the cells called Wnt10b, which is known for its role in skin growth. The iPS were cultured until they grew into little clumps called embryoid bodies (EBs), then transplanted into mice. Once inside the mice, the EBs continued to develop into skin tissue. To test the tissue’s medical potential, the researchers removed the new skin from the mice and transplanted it into other mice. They found that the artificial skin settled in and continued to develop normally as three-layered skin complete with glands and follicles. They also noticed that the new skin was able to connect with nearby nerves and muscles—an essential element in any transplanted tissue. 

Bioengineered skin with hair follicles. Image credit: © Takashi Tsuji, RIKEN

The researchers say their lab-grown skin is a step toward both important medical advances and a reduction in the use of lab animals in research (despite the study's reliance on mice subjects).

“With this new technique, we have successfully grown skin that replicates the function of normal tissue,” Tsuji said. “We are coming ever closer to the dream of being able to recreate actual organs in the lab for transplantation, and also believe that tissue grown through this method could be used as an alternative to animal testing of chemicals."

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iStock // Ekaterina Minaeva
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Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
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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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iStock
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Why Your iPhone Doesn't Always Show You the 'Decline Call' Button
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iStock

When you get an incoming call to your iPhone, the options that light up your screen aren't always the same. Sometimes you have the option to decline a call, and sometimes you only see a slider that allows you to answer, without an option to send the caller straight to voicemail. Why the difference?

A while back, Business Insider tracked down the answer to this conundrum of modern communication, and the answer turns out to be fairly simple.

If you get a call while your phone is locked, you’ll see the "slide to answer" button. In order to decline the call, you have to double-tap the power button on the top of the phone.

If your phone is unlocked, however, the screen that appears during an incoming call is different. You’ll see the two buttons, "accept" or "decline."

Either way, you get the options to set a reminder to call that person back or to immediately send them a text message. ("Dad, stop calling me at work, it’s 9 a.m.!")

[h/t Business Insider]

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