A football player making a tackle during a game. (© Joe/Fotolia)
What do football superstars Tom Brady, Drew Brees, Philip Rivers, and Adrian Peterson all have in common? Last year they wore helmets that provided the poorest protection against concussions in all the NFL.
"You're only as protected as well as the worst helmet that's out there."
A Dangerous Policy
Football helmets are rated on a one-star to five-star system based on how well they do the job of protecting the player. The league has allowed players to use their favorites, regardless of the star rating.
The Oxford-trained neuroscientist Ray Colello conducted a serious analysis of just how much the protection can vary between each level of star rating. Colello and his team of graduate students sifted through two seasons of game video to identify which players were wearing what helmets. There was "a really good correlation with position, but the correlation is much more significant based on age."
"The average player in the NFL is 26.6 years old, but the average age of a player wearing a one-star helmet is 34. And for anyone who knows football, that's ancient," the brain doc says. "Then for our two-star helmet, it's 32; and for a three-star helmet it's 29." Players were sticking with the helmets they were familiar with in college, despite the fact that equipment had improved considerably in recent years.
"You're only as protected as well as the worst helmet that's out there," Colello explains. Offering an auto analogy, he says, "It's like, if you run into the back of a Pinto, even if you are in a five-star Mercedes, that gas tank may still explode and you are still going to die."
It's one thing for a player to take a risk at scrambling his own brain; it's another matter to put a teammate or opponent at needless risk. Colello published his analysis early last year and the NFL moved quickly to ban the worst performing helmets, starting next season.
Some of the 14 players using the soon-to-be-banned helmets, like Drew Brees and Philip Rivers, made the switch to a five-star helmet at the start of training camp and stayed with it. Adrian Peterson wore a one-star helmet throughout the season.
Tom Brady tried but just couldn't get comfortable with a new bonnet and, after losing a few games, switched back to his old one in the middle of the season; he says he's going to ask the league to "grandfather in" his old helmet so he can continue to use it.
As for Colello, he's only just getting started. The brain doc has a much bigger vision for the future of football safety. He wants to prevent concussions from even occurring in the first place by creating an innovative new helmet that's unlike anything the league has ever seen.
Oxford-trained neuroscientist Ray Colello is on a mission to make football safer.
(Photo credit: VCU public affairs)
"A Force Field" of Protection
His inspiration was serendipitous; he was at home watching a football game on TV when Denver Bronco's receiver Wes Welker was hit, lay flat on the field with a concussion, and was carted off. As a commercial flickered on the screen, he ambled into the kitchen for another beer. "What those guys need is a force field protecting them," he thought to himself.
Like so many households, the refrigerator door was festooned with magnets holding his kids' school work in place. And in that eureka moment the idea popped into his head: "Maybe the repulsive force of magnets can put a break on an impact before it even occurs." Colello has spent the last few years trying to turn his concept into reality.
Newton's laws of physics – mass and speed – play out graphically in a concussion. The sudden stop of a helmet-to-helmet collision can shake the brain back and forth inside the skull like beans in a maraca. Dried beans stand up to the impact, making their distinctive musical sound; living brain tissue is much softer and not nearly so percussive. The resulting damage is a concussion.
The risk of that occurring is greater than you might think. Researchers using accelerometers inside helmets have determined that a typical college football player experiences about 600 helmet-to-helmet contacts during a season of practice and games. Each hit generates a split second peak g-force of 20 to 150 within the helmet and the odds of one causing a concussion increase sharply over 100 gs of force.
By comparison, astronauts typically experience a maximum sustained 3gs during lift off and most humans will black out around 9gs, which is why fighter pilots wear special pressure suits to counter the effects.
"It stretches the time line of impact quite dramatically. In fact in most instances, it doesn't even hit."
The NFL's fastest player, Chris Johnson, can run 19.3 mph. A collision at that speed "produces 120gs worth of force," Colello explains. "But if you can extend that time of impact by just 5 milliseconds (from 12 to 17msec) you'll shift that g-force down to 84. There is a very good chance that he won't suffer a concussion."
The neuroscientist dived into learning all he could about the physics magnets. It turns out that the most powerful commercially available magnet is an alloy made of neodymium, iron, and boron. The elements can be mixed and glued together in any shape and then an electric current is run through to make it magnetic; the direction of the current establishes the north-south poles.
A 1-pound neodymium magnet can repulse 600 times its own weight, even though the magnetic field extends less than an inch. That means it can push back a magnet inside another helmet but not affect the brain.
Crash Testing the Magnets
Colello couldn't wait to see if his idea panned out. With blessing from his wife to use their credit card, he purchased some neodymium magnets and jury-rigged experiments at home.
The reinforced plastics used in football helmets don't affect the magnetic field. And the small magnets stopped weights on gym equipment that were dropped from various heights. "It stretches the time line of impact quite dramatically. In fact in most instances, it doesn't even hit," says Colello. "We are dramatically shifting the curve" of impact.
Virginia Commonwealth University stepped in with a $50,000 innovation grant to support the next research steps. The professor ordered magnets custom-designed to fit the curvature of space inside the front and sides of existing football helmets. That makes it impossible to install them the wrong way, and ensures the magnets' poles will always repel and not attract. It adds about a pound and a half to the weight of the helmet.
a) The brain in a helmet. b) Placing the magnet. c) Measuring the impact of a helmet-to-helmet collision. d) How magnets reduce the force of impact.
(Courtesy Ray Colello)
Colello rented crash test dummy heads crammed with accelerometers and found that the magnets performed equally well at slowing collisions when fixed to a pendulum in a test that approximated a helmet and head hitting a similarly equipped helmet. It impressively reduced the force of contact.
The NFL was looking for outside-the-box thinking to prevent concussions. It was intrigued by Colello's approach and two years ago invited him to submit materials for review. To be fair to all entrants, the league proposed to subject all entries to the same standard crush test to see how well each performed in lessening impact. The only trouble was, Colello's approach was designed to avoid collisions, not lessen their impact. The test wouldn't have been a valid evaluation and he withdrew from consideration.
But Colello's work caught the attention of Stefan Duma, an engineering professor at Virginia Tech who developed the five-star rating system for football helmets.
"In theory it makes sense to use [the magnets] to slow down or reduce acceleration, that's logical," says Duma. He believes current helmet technology is nearing "the end of the physics barrier; you can only absorb so much energy in so much space," so the field is ripe for new approaches to improve helmet technology.
However, one of Duma's concerns is whether magnets "are feasible from a weight standpoint." Most helmets today weigh between two and four pounds, and a sufficiently powerful magnet might add too much weight. One possibility is using an electromagnet, which potentially could be lighter and more powerful, particularly if the power supply could be carried lower in the body, say in the shoulder pads.
Colello says his lab tests are promising enough that the concept needs to be tried out on the playing field. "We need to make enough helmets for two teams to play each other in a regulation-style game and measure the impact forces that are generated on each, and see if there is a significant reduction." He is waiting to hear from the National Institutes of Health on a grant proposal to take that next step toward dramatically reducing the risk of concussions in the NFL.
Just five milliseconds could do it.
Artificial Intelligence is already helping to grow some of the food we eat.
The chef-farmers choose from among 45 types of herb and leafy-greens seeds, plop them into grow trays, and a few weeks later they pick and serve. While success is predicated on at least a small amount of these humans’ care, the systems are autonomously surveilled round-the-clock from Babylon’s base of operations. And artificial intelligence is helping to run the show.
Babylon piloted the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off.
Imagine consistently perfect greens and tomatoes and strawberries, grown hyper-locally, using less water, without chemicals or environmental contaminants. This is the hefty promise of controlled environment agriculture (CEA) — basically, indoor farms that can be hydroponic, aeroponic (plant roots are suspended and fed through misting), or aquaponic (where fish play a role in fertilizing vegetables). But whether they grow 4,160 leafy-green servings per year, like one Babylon farm, or millions of servings, like some of the large, centralized facilities starting to supply supermarkets across the U.S., they seek to minimize failure as much as possible.
Babylon’s soilless hydroponic growing system
Courtesy Babylon Micro-Farms
Here, AI is starting to play a pivotal role. CEA growers use it to help “make sense of what’s happening” to the plants in their care, says Scott Lowman, vice president of applied research at the Institute for Advanced Learning and Research (IALR) in Virginia, a state that’s investing heavily in CEA companies. And although these companies say they’re not aiming for a future with zero human employees, AI is certainly poised to take a lot of human farming intervention out of the equation — for better and worse.
Most of these companies are compiling their own data sets to identify anything that might block the success of their systems. Babylon had already integrated sensor data into its farms to measure heat and humidity, the nutrient content of water, and the amount of light plants receive. Last year, they got a National Science Foundation grant that allowed them to pilot the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off. “Will this plant be healthy tomorrow? Are there things…that the human eye can't see that the plant starts expressing?” says Amandeep Ratte, the company’s head of data science. “If our system can say, Hey, this plant is unhealthy, we can reach out to [users] preemptively about what they’re doing wrong, or is there a disease at the farm?” Ratte says. The earlier the better, to avoid crop failures.
Natural light accounts for 70 percent of Greenswell Growers’ energy use on a sunny day.
Courtesy Greenswell Growers
IALR’s Lowman says that other CEA companies are developing their AI systems to account for the different crops they grow — lettuces come in all shapes and sizes, after all, and each has different growing needs than, for example, tomatoes. The ways they run their operations differs also. Babylon is unusual in its decentralized structure. But centralized growing systems with one main location have variabilities, too. AeroFarms, which recently declared bankruptcy but will continue to run its 140,000-square foot vertical operation in Danville, Virginia, is entirely enclosed and reliant on the intense violet glow of grow lights to produce microgreens.
Different companies have different data needs. What data is essential to AeroFarms isn’t quite the same as for Greenswell Growers located in Goochland County, Virginia. Raising four kinds of lettuce in a 77,000-square-foot automated hydroponic greenhouse, the vagaries of naturally available light, which accounts for 70 percent of Greenswell’s energy use on a sunny day, affect operations. Their tech needs to account for “outside weather impacts,” says president Carl Gupton. “What adjustments do we have to make inside of the greenhouse to offset what's going on outside environmentally, to give that plant optimal conditions? When it's 85 percent humidity outside, the system needs to do X, Y and Z to get the conditions that we want inside.”
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen.
Nevertheless, every CEA system has the same core needs — consistent yield of high quality crops to keep up year-round supply to customers. Additionally, “Everybody’s got the same set of problems,” Gupton says. Pests may come into a facility with seeds. A disease called pythium, one of the most common in CEA, can damage plant roots. “Then you have root disease pressures that can also come internally — a change in [growing] substrate can change the way the plant performs,” Gupton says.
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen. So, while companies amass their own hyper-specific data sets, Lowman foresees a time within the next decade “when there will be some type of [open-source] database that has the most common types of plant stress identified” that growers will be able to tap into. Such databases will “create a community and move the science forward,” says Lowman.
In fact, IALR is working on assembling images for just such a database now. On so-called “smart tables” inside an Institute lab, a team is growing greens and subjects them to various stressors. Then, they’re administering treatments while taking images of every plant every 15 minutes, says Lowman. Some experiments generate 80,000 images; the challenge lies in analyzing and annotating the vast trove of them, marking each one to reflect outcome—for example increasing the phosphate delivery and the plant’s response to it. Eventually, they’ll be fed into AI systems to help them learn.
For all the enthusiasm surrounding this technology, it’s not without downsides. Training just one AI system can emit over 250,000 pounds of carbon dioxide, according to MIT Technology Review. AI could also be used “to enhance environmental benefit for CEA and optimize [its] energy consumption,” says Rozita Dara, a computer science professor at the University of Guelph in Canada, specializing in AI and data governance, “but we first need to collect data to measure [it].”
The chef-farmers can choose from 45 types of herb and leafy-greens seeds.
Courtesy Babylon Micro-Farms
Any system connected to the Internet of Things is also vulnerable to hacking; if CEA grows to the point where “there are many of these similar farms, and you're depending on feeding a population based on those, it would be quite scary,” Dara says. And there are privacy concerns, too, in systems where imaging is happening constantly. It’s partly for this reason, says Babylon’s Ratte, that the company’s in-farm cameras all “face down into the trays, so the only thing [visible] is pictures of plants.”
Tweaks to improve AI for CEA are happening all the time. Greenswell made its first harvest in 2022 and now has annual data points they can use to start making more intelligent choices about how to feed, water, and supply light to plants, says Gupton. Ratte says he’s confident Babylon’s system can already “get our customers reliable harvests. But in terms of how far we have to go, it's a different problem,” he says. For example, if AI could detect whether the farm is mostly empty—meaning the farm’s user hasn’t planted a new crop of greens—it can alert Babylon to check “what's going on with engagement with this user?” Ratte says. “Do they need more training? Did the main person responsible for the farm quit?”
Lowman says more automation is coming, offering greater ability for systems to identify problems and mitigate them on the spot. “We still have to develop datasets that are specific, so you can have a very clear control plan, [because] artificial intelligence is only as smart as what we tell it, and in plant science, there's so much variation,” he says. He believes AI’s next level will be “looking at those first early days of plant growth: when the seed germinates, how fast it germinates, what it looks like when it germinates.” Imaging all that and pairing it with AI, “can be a really powerful tool, for sure.”
Researchers from the University of Cambridge have laid the foundations for growing synthetic embryos that could develop a beating heart, gut and brain.
The organoid revolution
In 1981, scientists discovered how to keep stem cells alive. This was a significant breakthrough, as stem cells have notoriously rigorous demands. Nevertheless, stem cells remained a relatively niche research area, mainly because scientists didn’t know how to convince the cells to turn into other cells.
Then, in 1987, scientists embedded isolated stem cells in a gelatinous protein mixture called Matrigel, which simulated the three-dimensional environment of animal tissue. The cells thrived, but they also did something remarkable: they created breast tissue capable of producing milk proteins. This was the first organoid — a clump of cells that behave and function like a real organ. The organoid revolution had begun, and it all started with a boob in Jello.
For the next 20 years, it was rare to find a scientist who identified as an “organoid researcher,” but there were many “stem cell researchers” who wanted to figure out how to turn stem cells into other cells. Eventually, they discovered the signals (called growth factors) that stem cells require to differentiate into other types of cells.
For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells.
By the end of the 2000s, researchers began combining stem cells, Matrigel, and the newly characterized growth factors to create dozens of organoids, from liver organoids capable of producing the bile salts necessary for digesting fat to brain organoids with components that resemble eyes, the spinal cord, and arguably, the beginnings of sentience.
Synthetic embryos
Organoids possess an intrinsic flaw: they are organ-like. They share some characteristics with real organs, making them powerful tools for research. However, no one has found a way to create an organoid with all the characteristics and functions of a real organ. But Magdalena Żernicka-Goetz, a developmental biologist, might have set the foundation for that discovery.
Żernicka-Goetz hypothesized that organoids fail to develop into fully functional organs because organs develop as a collective. Organoid research often uses embryonic stem cells, which are the cells from which the developing organism is created. However, there are two other types of stem cells in an early embryo: stem cells that become the placenta and those that become the yolk sac (where the embryo grows and gets its nutrients in early development). For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells. In other words, Żernicka-Goetz suspected the best way to grow a functional organoid was to produce a synthetic embryoid.
As described in the aforementioned Nature paper, Żernicka-Goetz and her team mimicked the embryonic environment by mixing these three types of stem cells from mice. Amazingly, the stem cells self-organized into structures and progressed through the successive developmental stages until they had beating hearts and the foundations of the brain.
“Our mouse embryo model not only develops a brain, but also a beating heart [and] all the components that go on to make up the body,” said Żernicka-Goetz. “It’s just unbelievable that we’ve got this far. This has been the dream of our community for years and major focus of our work for a decade and finally we’ve done it.”
If the methods developed by Żernicka-Goetz’s team are successful with human stem cells, scientists someday could use them to guide the development of synthetic organs for patients awaiting transplants. It also opens the door to studying how embryos develop during pregnancy.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
