Tag Archives: implants

#439042 How Scientists Used Ultrasound to Read ...

Thanks to neural implants, mind reading is no longer science fiction.

As I’m writing this sentence, a tiny chip with arrays of electrodes could sit on my brain, listening in on the crackling of my neurons firing as my hands dance across the keyboard. Sophisticated algorithms could then decode these electrical signals in real time. My brain’s inner language to plan and move my fingers could then be used to guide a robotic hand to do the same. Mind-to-machine control, voilà!

Yet as the name implies, even the most advanced neural implant has a problem: it’s an implant. For electrodes to reliably read the brain’s electrical chatter, they need to pierce through the its protective membrane and into brain tissue. Danger of infection aside, over time, damage accumulates around the electrodes, distorting their signals or even rendering them unusable.

Now, researchers from Caltech have paved a way to read the brain without any physical contact. Key to their device is a relatively new superstar in neuroscience: functional ultrasound, which uses sound waves to capture activity in the brain.

In monkeys, the technology could reliably predict their eye movement and hand gestures after just a single trial—without the usual lengthy training process needed to decode a movement. If adopted by humans, the new mind-reading tech represents a triple triumph: it requires minimal surgery and minimal learning, but yields maximal resolution for brain decoding. For people who are paralyzed, it could be a paradigm shift in how they control their prosthetics.

“We pushed the limits of ultrasound neuroimaging and were thrilled that it could predict movement,” said study author Dr. Sumner Norman.

To Dr. Krishna Shenoy at Stanford, who was not involved, the study will finally put ultrasound “on the map as a brain-machine interface technique. Adding to this toolkit is spectacular,” he said.

Breaking the Sound Barrier
Using sound to decode brain activity might seem preposterous, but ultrasound has had quite the run in medicine. You’ve probably heard of its most common use: taking photos of a fetus in pregnancy. The technique uses a transducer, which emits ultrasound pulses into the body and finds boundaries in tissue structure by analyzing the sound waves that bounce back.

Roughly a decade ago, neuroscientists realized they could adapt the tech for brain scanning. Rather than directly measuring the brain’s electrical chatter, it looks at a proxy—blood flow. When certain brain regions or circuits are active, the brain requires much more energy, which is provided by increased blood flow. In this way, functional ultrasound works similarly to functional MRI, but at a far higher resolution—roughly ten times, the authors said. Plus, people don’t have to lie very still in an expensive, claustrophobic magnet.

“A key question in this work was: If we have a technique like functional ultrasound that gives us high-resolution images of the brain’s blood flow dynamics in space and over time, is there enough information from that imaging to decode something useful about behavior?” said study author Dr. Mikhail Shapiro.

There’s plenty of reasons for doubt. As the new kid on the block, functional ultrasound has some known drawbacks. A major one: it gives a far less direct signal than electrodes. Previous studies show that, with multiple measurements, it can provide a rough picture of brain activity. But is that enough detail to guide a robotic prosthesis?

One-Trial Wonder
The new study put functional ultrasound to the ultimate test: could it reliably detect movement intention in monkeys? Because their brains are the most similar to ours, rhesus macaque monkeys are often the critical step before a brain-machine interface technology is adapted for humans.

The team first inserted small ultrasound transducers into the skulls of two rhesus monkeys. While it sounds intense, the surgery doesn’t penetrate the brain or its protective membrane; it’s only on the skull. Compared to electrodes, this means the brain itself isn’t physically harmed.

The device is linked to a computer, which controls the direction of sound waves and captures signals from the brain. For this study, the team aimed the pulses at the posterior parietal cortex, a part of the “motor” aspect of the brain, which plans movement. If right now you’re thinking about scrolling down this page, that’s the brain region already activated, before your fingers actually perform the movement.

Then came the tests. The first looked at eye movements—something pretty necessary before planning actual body movements without tripping all over the place. Here, the monkeys learned to focus on a central dot on a computer screen. A second dot, either left or right, then flashed. The monkeys’ task was to flicker their eyes to the most recent dot. It’s something that seems easy for us, but requires sophisticated brain computation.

The second task was more straightforward. Rather than just moving their eyes to the second target dot, the monkeys learned to grab and manipulate a joystick to move a cursor to that target.

Using brain imaging to decode the mind and control movement. Image Credit: S. Norman, Caltech
As the monkeys learned, so did the device. Ultrasound data capturing brain activity was fed into a sophisticated machine learning algorithm to guess the monkeys’ intentions. Here’s the kicker: once trained, using data from just a single trial, the algorithm was able to correctly predict the monkeys’ actual eye movement—whether left or right—with roughly 78 percent accuracy. The accuracy for correctly maneuvering the joystick was even higher, at nearly 90 percent.

That’s crazy accurate, and very much needed for a mind-controlled prosthetic. If you’re using a mind-controlled cursor or limb, the last thing you’d want is to have to imagine the movement multiple times before you actually click the web button, grab the door handle, or move your robotic leg.

Even more impressive is the resolution. Sound waves seem omnipresent, but with focused ultrasound, it’s possible to measure brain activity at a resolution of 100 microns—roughly 10 neurons in the brain.

A Cyborg Future?
Before you start worrying about scientists blasting your brain with sound waves to hack your mind, don’t worry. The new tech still requires skull surgery, meaning that a small chunk of skull needs to be removed. However, the brain itself is spared. This means that compared to electrodes, ultrasound could offer less damage and potentially a far longer mind reading than anything currently possible.

There are downsides. Focused ultrasound is far younger than any electrode-based neural implants, and can’t yet reliably decode 360-degree movement or fine finger movements. For now, the tech requires a wire to link the device to a computer, which is off-putting to many people and will prevent widespread adoption. Add to that the inherent downside of focused ultrasound, which lags behind electrical recordings by roughly two seconds.

All that aside, however, the tech is just tiptoeing into a future where minds and machines seamlessly connect. Ultrasound can penetrate the skull, though not yet at the resolution needed for imaging and decoding brain activity. The team is already working with human volunteers with traumatic brain injuries, who had to have a piece of their skulls removed, to see how well ultrasound works for reading their minds.

“What’s most exciting is that functional ultrasound is a young technique with huge potential. This is just our first step in bringing high performance, less invasive brain-machine interface to more people,” said Norman.

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#438001 How an Israeli Startup Is Using AI to ...

The first baby conceived using in-vitro fertilization (IVF) was born in the UK in 1978. Over 40 years later, the technique has become commonplace, but its success rate is still fairly low at around 22 to 30 percent. A female-founded Israeli startup called Embryonics is setting out to change this by using artificial intelligence to screen embryos.

IVF consists of fertilizing a woman’s egg with her partner’s or a donor’s sperm outside of her body, creating an embryo that’s then implanted in the uterus. It’s not an easy process in any sense of the word—physically, emotionally, or financially. Insurance rarely covers IVF, and the costs run anywhere from $12,000 to $25,000 per cycle (a cycle takes about a month and includes stimulating a woman’s ovaries to produce eggs, extracting the eggs, inseminating them outside the body, and implanting an embryo).

Women have to give themselves daily hormone shots to stimulate egg production, and these can cause uncomfortable side effects. After so much stress and expense, it’s disheartening to think that the odds of a successful pregnancy are, at best, one in three.

A crucial factor in whether or not an IVF cycle works—that is, whether the embryo implants in the uterus and begins to develop into a healthy fetus—is the quality of the embryo. Doctors examine embryos through a microscope to determine how many cells they contain and whether they appear healthy, and choose the one that looks most viable.

But the human eye can only see so much, even with the help of a microscope; despite embryologists’ efforts to select the “best” embryo, success rates are still relatively low. “Many decisions are based on gut feeling or personal experience,” said Embryonics founder and CEO Yael Gold-Zamir. “Even if you go to the same IVF center, two experts can give you different opinions on the same embryo.”

This is where Embryonics’ technology comes in. They used 8,789 time-lapse videos of developing embryos to train an algorithm that predicts the likelihood of successful embryo implantation. A little less than half of the embryos from the dataset were graded by embryologists, and implantation data was integrated when it was available (as a binary “successful” or “failed” metric).

The algorithm uses geometric deep learning, a technique that takes a traditional convolutional neural network—which filters input data to create maps of its features, and is most commonly used for image recognition—and applies it to more complex data like 3D objects and graphs. Within days after fertilization, the embryo is still at the blastocyst stage, essentially a microscopic clump of just 200-300 cells; the algorithm uses this deep learning technique to spot and identify patterns in embryo development that human embryologists either wouldn’t see at all, or would require massive collation of data to validate.

On top of the embryo videos, Embryonics’ team incorporated patient data and environmental data from the lab into its algorithm, with encouraging results: the company reports that using its algorithm resulted in a 12 percent increase in positive predictive value (identifying embryos that would lead to implantation and healthy pregnancy) and a 29 percent increase in negative predictive value (identifying embyros that would not result in successful pregnancy) when compared to an external panel of embryologists.

TechCrunch reported last week that in a pilot of 11 women who used Embryonics’ algorithm to select their embryos, 6 are enjoying successful pregnancies, while 5 are still awaiting results.

Embryonics wasn’t the first group to think of using AI to screen embryos; a similar algorithm developed in 2019 by researchers at Weill Cornell Medicine was able to classify the quality of a set of embryo images with 97 percent accuracy. But Embryonics will be one of the first to bring this sort of technology to market. The company is waiting to receive approval from European regulatory bodies to be able to sell the software to fertility clinics in Europe.

Its timing is ripe: as more and more women delay having kids due to lifestyle and career-related factors, demand for IVF is growing, and will likely accelerate in coming years.

The company ultimately hopes to bring its product to the US, as well as to expand its work to include using data to improve hormonal stimulation.

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#437577 A Swarm of Cyborg Cockroaches That Lives ...

Digital Nature Group at the University of Tsukuba in Japan is working towards a “post ubiquitous computing era consisting of seamless combination of computational resources and non-computational resources.” By “non-computational resources,” they mean leveraging the natural world, which for better or worse includes insects.

At small scales, the capabilities of insects far exceed the capabilities of robots. I get that. And I get that turning cockroaches into an army of insect cyborgs could be useful in a variety of ways. But what makes me fundamentally uncomfortable is the idea that “in the future, they’ll appear out of nowhere without us recognizing it, fulfilling their tasks and then hiding.” In other words, you’ll have cyborg cockroaches hiding all over your house, all the time.

Warning: This article contains video of cockroaches being modified with cybernetic implants that some people may find upsetting.

Remote controlling cockroaches isn’t a new idea, and it’s a fairly simple one. By stimulating the left or right antenna nerves of the cockroach, you can make it think that it’s running into something, and get it to turn in the opposite direction. Add wireless connectivity, some fiducial markers, an overhead camera system, and a bunch of cyborg cockroaches, and you have a resilient swarm that can collaborate on tasks. The researchers suggest that the swarm could be used as a display (by making each cockroach into a pixel), to transport objects, or to draw things. There’s also some mention of “input or haptic interfaces or an audio device,” which frankly sounds horrible.

The reason to use cockroaches is that you can take advantage of their impressive ruggedness, efficiency, high power to weight ratio, and mobility. They can also feed themselves, meaning that whenever you don’t need the swarm to perform some task for you, you can deactivate the control system and let them scurry off to find crumbs in dark places.

There are many other swarm robotic platforms that can perform what you’re seeing these cyborg roaches do, but according to the researchers, the reason to use cockroaches is that you can take advantage of their impressive ruggedness, efficiency, high power to weight ratio, and mobility. They’re a lot messier (yay biology!), but they can also feed themselves, meaning that whenever you don’t need the swarm to perform some task for you, you can deactivate the control system and let them scurry off to find crumbs in dark places. And when you need them again, turn the control system on and experience the nightmare of your cyborg cockroach swarm reassembling itself from all over your house.

While we’re on the subject of cockroach hacking, we would be doing you a disservice if we didn’t share some of project leader Yuga Tsukuda’s other projects. Here’s a cockroach-powered clock, about which the researchers note that “it is difficult to control the cockroaches when trying to control them by electrical stimulation because they move spontaneously. However, by cutting off the head and removing the brain, they do not move spontaneously and the control by the computer becomes easy.” So, zombie cockroaches. Good then.

And if that’s not enough for you, how about this:

The researchers describe this project as an “attempt to use cockroaches for makeup by sticking them on the face.” They stick electrodes into the cockroaches to make them wiggle their legs when electrical stimulation is applied. And the peacock feathers? They “make the cockroach movement bigger, and create a cosmic mystery.” Continue reading

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#437554 Ending the COVID-19 Pandemic

Photo: F.J. Jimenez/Getty Images

The approach of a new year is always a time to take stock and be hopeful. This year, though, reflection and hope are more than de rigueur—they’re rejuvenating. We’re coming off a year in which doctors, engineers, and scientists took on the most dire public threat in decades, and in the new year we’ll see the greatest results of those global efforts. COVID-19 vaccines are just months away, and biomedical testing is being revolutionized.

At IEEE Spectrum we focus on the high-tech solutions: Can artificial intelligence (AI) be used to diagnose COVID-19 using cough recordings? Can mathematical modeling determine whether preventive measures against COVID-19 work? Can big data and AI provide accurate pandemic forecasting?

Consider our story “AI Recognizes COVID-19 in the Sound of a Cough,” reported by Megan Scudellari in our Human OS blog. Using a cellphone-recorded cough, machine-learning models can now detect coronavirus with 90 percent accuracy, even in people with no symptoms. It’s a remarkable research milestone. This AI model sifts through hundreds of factors to distinguish the COVID-19 cough from those of bronchitis, whooping cough, and asthma.

But while such high-tech triumphs give us hope, the no-tech solutions are mostly what we have to work with. Soon, as our Numbers Don’t Lie columnist, Vaclav Smil, pointed out in a recent email, we will have near-instantaneous home testing, and we will have an ability to use big data to crunch every move and every outbreak. But we are nowhere near that yet. So let’s use, as he says, some old-fashioned kindergarten epidemiology, the no-tech measures, while we work to get there:

Masks: Wear them. If we all did so, we could cut transmission by two-thirds, perhaps even 80 percent.

Hands: Wash them.

Social distancing: If we could all stay home for two weeks, we could see enormous declines in COVID-19 transmission.

These are all time-tested solutions, proven effective ages ago in countless outbreaks of diseases including typhoid and cholera. They’re inexpensive and easy to prescribe, and the regimens are easy to follow.

The conflict between public health and individual rights and privacy, however, is less easy to resolve. Even during the pandemic of 1918–19, there was widespread resistance to mask wearing and social distancing. Fifty million people died—675,000 in the United States alone. Today, we are up to 240,000 deaths in the United States, and the end is not in sight. Antiflu measures were framed in 1918 as a way to protect the troops fighting in World War I, and people who refused to wear masks were called out as “dangerous slackers.” There was a world war, and yet it was still hard to convince people of the need for even such simple measures.

Personally, I have found the resistance to these easy fixes startling. I wouldn’t want maskless, gloveless doctors taking me through a surgical procedure. Or waltzing in from lunch without washing their hands. I’m sure you wouldn’t, either.

Science-based medicine has been one of the world’s greatest and most fundamental advances. In recent years, it has been turbocharged by breakthroughs in genetics technologies, advanced materials, high-tech diagnostics, and implants and other electronics-based interventions. Such leaps have already saved untold lives, but there’s much more to be done. And there will be many more pandemics ahead for humanity.

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#437145 3 Major Materials Science ...

Few recognize the vast implications of materials science.

To build today’s smartphone in the 1980s, it would cost about $110 million, require nearly 200 kilowatts of energy (compared to 2kW per year today), and the device would be 14 meters tall, according to Applied Materials CTO Omkaram Nalamasu.

That’s the power of materials advances. Materials science has democratized smartphones, bringing the technology to the pockets of over 3.5 billion people. But far beyond devices and circuitry, materials science stands at the center of innumerable breakthroughs across energy, future cities, transit, and medicine. And at the forefront of Covid-19, materials scientists are forging ahead with biomaterials, nanotechnology, and other materials research to accelerate a solution.

As the name suggests, materials science is the branch devoted to the discovery and development of new materials. It’s an outgrowth of both physics and chemistry, using the periodic table as its grocery store and the laws of physics as its cookbook.

And today, we are in the middle of a materials science revolution. In this article, we’ll unpack the most important materials advancements happening now.

Let’s dive in.

The Materials Genome Initiative
In June 2011 at Carnegie Mellon University, President Obama announced the Materials Genome Initiative, a nationwide effort to use open source methods and AI to double the pace of innovation in materials science. Obama felt this acceleration was critical to the US’s global competitiveness, and held the key to solving significant challenges in clean energy, national security, and human welfare. And it worked.

By using AI to map the hundreds of millions of different possible combinations of elements—hydrogen, boron, lithium, carbon, etc.—the initiative created an enormous database that allows scientists to play a kind of improv jazz with the periodic table.

This new map of the physical world lets scientists combine elements faster than ever before and is helping them create all sorts of novel elements. And an array of new fabrication tools are further amplifying this process, allowing us to work at altogether new scales and sizes, including the atomic scale, where we’re now building materials one atom at a time.

Biggest Materials Science Breakthroughs
These tools have helped create the metamaterials used in carbon fiber composites for lighter-weight vehicles, advanced alloys for more durable jet engines, and biomaterials to replace human joints. We’re also seeing breakthroughs in energy storage and quantum computing. In robotics, new materials are helping us create the artificial muscles needed for humanoid, soft robots—think Westworld in your world.

Let’s unpack some of the leading materials science breakthroughs of the past decade.

(1) Lithium-ion batteries

The lithium-ion battery, which today powers everything from our smartphones to our autonomous cars, was first proposed in the 1970s. It couldn’t make it to market until the 1990s, and didn’t begin to reach maturity until the past few years.

An exponential technology, these batteries have been dropping in price for three decades, plummeting 90 percent between 1990 and 2010, and 80 percent since. Concurrently, they’ve seen an eleven-fold increase in capacity.

But producing enough of them to meet demand has been an ongoing problem. Tesla has stepped up to the challenge: one of the company’s Gigafactories in Nevada churns out 20 gigawatts of energy storage per year, marking the first time we’ve seen lithium-ion batteries produced at scale.

Musk predicts 100 Gigafactories could store the energy needs of the entire globe. Other companies are moving quickly to integrate this technology as well: Renault is building a home energy storage based on their Zoe batteries, BMW’s 500 i3 battery packs are being integrated into the UK’s national energy grid, and Toyota, Nissan, and Audi have all announced pilot projects.

Lithium-ion batteries will continue to play a major role in renewable energy storage, helping bring down solar and wind energy prices to compete with those of coal and gasoline.

(2) Graphene

Derived from the same graphite found in everyday pencils, graphene is a sheet of carbon just one atom thick. It is nearly weightless, but 200 times stronger than steel. Conducting electricity and dissipating heat faster than any other known substance, this super-material has transformative applications.

Graphene enables sensors, high-performance transistors, and even gel that helps neurons communicate in the spinal cord. Many flexible device screens, drug delivery systems, 3D printers, solar panels, and protective fabric use graphene.

As manufacturing costs decrease, this material has the power to accelerate advancements of all kinds.

(3) Perovskite

Right now, the “conversion efficiency” of the average solar panel—a measure of how much captured sunlight can be turned into electricity—hovers around 16 percent, at a cost of roughly $3 per watt.

Perovskite, a light-sensitive crystal and one of our newer new materials, has the potential to get that up to 66 percent, which would double what silicon panels can muster.

Perovskite’s ingredients are widely available and inexpensive to combine. What do all these factors add up to? Affordable solar energy for everyone.

Materials of the Nano-World
Nanotechnology is the outer edge of materials science, the point where matter manipulation gets nano-small—that’s a million times smaller than an ant, 8,000 times smaller than a red blood cell, and 2.5 times smaller than a strand of DNA.

Nanobots are machines that can be directed to produce more of themselves, or more of whatever else you’d like. And because this takes place at an atomic scale, these nanobots can pull apart any kind of material—soil, water, air—atom by atom, and use these now raw materials to construct just about anything.

Progress has been surprisingly swift in the nano-world, with a bevy of nano-products now on the market. Never want to fold clothes again? Nanoscale additives to fabrics help them resist wrinkling and staining. Don’t do windows? Not a problem! Nano-films make windows self-cleaning, anti-reflective, and capable of conducting electricity. Want to add solar to your house? We’ve got nano-coatings that capture the sun’s energy.

Nanomaterials make lighter automobiles, airplanes, baseball bats, helmets, bicycles, luggage, power tools—the list goes on. Researchers at Harvard built a nanoscale 3D printer capable of producing miniature batteries less than one millimeter wide. And if you don’t like those bulky VR goggles, researchers are now using nanotech to create smart contact lenses with a resolution six times greater than that of today’s smartphones.

And even more is coming. Right now, in medicine, drug delivery nanobots are proving especially useful in fighting cancer. Computing is a stranger story, as a bioengineer at Harvard recently stored 700 terabytes of data in a single gram of DNA.

On the environmental front, scientists can take carbon dioxide from the atmosphere and convert it into super-strong carbon nanofibers for use in manufacturing. If we can do this at scale—powered by solar—a system one-tenth the size of the Sahara Desert could reduce CO2 in the atmosphere to pre-industrial levels in about a decade.

The applications are endless. And coming fast. Over the next decade, the impact of the very, very small is about to get very, very large.

Final Thoughts
With the help of artificial intelligence and quantum computing over the next decade, the discovery of new materials will accelerate exponentially.

And with these new discoveries, customized materials will grow commonplace. Future knee implants will be personalized to meet the exact needs of each body, both in terms of structure and composition.

Though invisible to the naked eye, nanoscale materials will integrate into our everyday lives, seamlessly improving medicine, energy, smartphones, and more.

Ultimately, the path to demonetization and democratization of advanced technologies starts with re-designing materials— the invisible enabler and catalyst. Our future depends on the materials we create.

(Note: This article is an excerpt from The Future Is Faster Than You Think—my new book, just released on January 28th! To get your own copy, click here!)

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If you’d like to learn more and consider joining our 2021 membership, apply here.

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This article originally appeared on diamandis.com. Read the original article here.

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