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Liver Stress Hormone Talks to the Brain to Reduce Alcohol and Sugar Preference

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Endocrinology researchers already knew that a stress hormone secreted in the liver—fibroblast growth factor 21, or FGF21—helps regulate metabolism in humans and mice. Now, a new study published by researchers at UT Southwestern Medical Center in Cell Metabolism is the first to discover that FGF21 communicates directly with the brain via the brain’s reward pathway to control preferences for, and amounts of, sugar and alcohol consumption in mice—and potentially humans. This could lead to new drugs to treat diabetes, alcoholism, and other forms of addiction. 

Though the new study was conducted on mice, co-senior author Steven Kliewer, a professor of molecular biology and pharmacology at UT Southwestern, tells mental_floss: “Our springboard for this study was human studies. One of the nice things about this is that we already have evidence of human relevance, not just a rodent phenomenon.” 

Kliewer runs a joint laboratory with David Mangelsdorf, with whom he has done four total studies on FGF21. Two studies published in Nature Medicine in 2013 showed FGF21’s ability to regulate metabolism, circadian behavior, and female reproduction. In 2014, their study published in Cell Metabolism showed that FGF21 can cause weight loss. 

Kliewer and Mangelsdorf knew the liver releases FGF21 in response to a variety of stresses, such as marked changes in metabolic and environmental stresses that accompany starvation or exposure to extreme cold, but, Kliewer says, “We hadn’t anticipated that there would be this endocrine loop where the liver communicates with the brain to regulate nutrient preference.”

FGF21 sends the message “too much” to the brain when it is consuming sugar or alcohol, “but obviously it’s not enough to stop overconsumption in the long run,” Kliewer says. At least, not yet. He believes that the FGF21 pathway “could be very powerful to exploit in terms of developing drugs to treat addiction.”

The researchers demonstrated that mice with elevated levels of FGF21 showed a reduced preference for either sweetener- or ethanol-laced water. The mice were given “free access” to food and a choice between two water bottles in their cages. In the first experiment, one of the bottles contained only water and the other contained sweetened water. They repeated the experiment with two bottles of water and one with concentrations of ethanol. Then they measured how much the mice drank from each bottle.  

They were surprised to find that the FGF21 mice showed reduced interest in either the sweetened or the ethanol water, and preferred plain water. Furthermore, they showed that FGF21 was responsible for the decreased preference for sweet and alcohol in the brain, accompanied by a decrease in dopamine levels. “We found that FGF21 affects neurotransmitter dopamine levels, which is important for lots of reward behaviors, it’s a global reward regulator,” Kleiwer says. 

FGF21 requires a co-receptor, β-Klotho, to function. To confirm that FGF21 acts along the brain’s reward pathway, they increased its levels in mice that had been genetically modified to be unable to produce β-Klotho and found the taste preference effect disappeared. 

From here they hope to understand the molecular pathways of FGF21 better for its drug potential in the treatment of addiction, which will require more study due to its known side effects. “We already know that it causes some bone loss when it’s taken long term at high levels,” says Kliewer. “And any time you start messing around with reward behaviors, you have to worry about depression.” 

Kliewer says that the questions driving the next phase of research will include: “What is the reason the liver does this [secretes FGF21 along the brain’s reward pathway]? Under what conditions naturally? And can the levels of FGF21 be increased in humans?”

He cautions that it's a long process to bring research findings into clinical settings. “This is exciting biology and has promise, but … people have to take this [finding] with a grain of salt.”

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iStock // Ekaterina Minaeva
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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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What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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Nick Briggs/Comic Relief

Fans of the romantic-comedy Love Actually recently got a bonus reunion in the form of Red Nose Day Actually, a short charity special that gave audiences a peek at where their favorite characters ended up almost 15 years later.

One of the most improbable pairings from the original film was between Jamie (Colin Firth) and Aurelia (Lúcia Moniz), who fell in love despite almost no shared vocabulary. Jamie is English, and Aurelia is Portuguese, and they know just enough of each other’s native tongues for Jamie to propose and Aurelia to accept.

A decade and a half on, they have both improved their knowledge of each other’s languages—if not perfectly, in Jamie’s case. But apparently, their love is much stronger than his grasp on Portuguese grammar, because they’ve got three bilingual kids and another on the way. (And still enjoy having important romantic moments in the car.)

In 2015, Love Actually script editor Emma Freud revealed via Twitter what happened between Karen and Harry (Emma Thompson and Alan Rickman, who passed away last year). Most of the other couples get happy endings in the short—even if Hugh Grant's character hasn't gotten any better at dancing.

[h/t TV Guide]

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