This week we learned about a cloaking device that helps medicine sneak up on cancer cells, a tiny brain for a tiny drone, and “smart outlets” that can learn the difference between harmless power surges and dangerous ones. What a trip!
What is it? At Penn State, scientists have designed a “biomimetic nanosystem” to fight cancer — it mimics a crucial component of cancer cells in order to deliver tumor-fighting treatments.
Why does it matter? “Blood is a hostile environment for drug delivery,” explains Penn State in a press release, precisely because it’s so good at flushing out what it perceives to be impurities in its midst. But some cells, including cancer cells, come with what are called extracellular vesicles — particles that send a friend-not-foe message to the immune system, convincing it to leave well enough alone. As reported in the Journal of the American Chemical Society, researchers figured out a way to wrap cancer-fighting proteins in these extracellular vesicles, getting them past the body’s defenses to where they need to go to attack disease.
How does it work? It all starts in the Himalayan mountains — that’s where researchers source a plant that produces the protein toxin that attacks the tumor. They then wrap the toxin in “self-assembled metal-organic framework (MOF) nanoparticles” and finally coat them in the extracellular vesicles. Once the nanoparticles have hooked onto a cancer cell, the cell’s acidity dissolves the metal-organic framework, releasing the tumor-fighting toxin within.
What is it? Drone engineers at MIT have a new bee in their bonnet: an even smaller, more efficient chip than the one they introduced last year for navigating tiny flying robots.
Why does it matter? It takes a ton of power to fly a drone, let alone operate surveillance equipment attached to it. The team’s new chip measures a mere 20 square millimeters “about the size of a LEGO minifigure’s footprint,” according to MIT — and consumes just 24 milliwatts of power. “I can imagine applying this chip to low-energy robotics, like flapping-wing vehicles the size of your fingernail, or lighter-than-air vehicles like weather balloons, that have to go for months on one battery,” says team co-leader Sertac Karaman. “Or imagine medical devices like a little pill you swallow, that can navigate in an intelligent way on very little battery so it doesn’t overheat in your body. The chips we are building can help with all of these.”
How does it work? Despite its wee dimensions, the chip can process images at up to 171 frames per second in real time, plus “inertial measurements” to tell the drone — or any small robot — where it is in space. That’s because the design limits how much image data is stored on the chip by compressing it. It also cuts down on power- and memory-hungry “extraneous operations, such as the computation of zeroes.”
What is it? A new “smart outlet” can distinguish between dangerous electrical spikes and the more routine ones caused by using household appliances — the annoying losses of power referred to as “nuisance trips,” situations when the circuit breaker cuts off power for some mundane reason.
Why does it matter? You’ve probably had this happen: You plug the blender into the same outlet as the microwave — and suddenly neither has juice. That’s because outlets are designed with detectors that look for “arc-faults” — power discharges that could cause sparks and even fire — and interrupt the circuit when they think they’ve found one. Problem is, the detectors often err too much on the side of caution, interpreting routine and harmless surges — such as that blender-microwave combo — as a hazard. Designed by a team of engineers at MIT, the new smart power outlet collects data over time that helps it learn the difference between common electricity surges and truly dangerous ones.
How does it work? Via a combination of data collection and software analytics: The outlet tracks the way electricity is used in real time, then transfers the information to software that learns to recognize the devices that are usually plugged in and get familiar with how much power they require. Research scientist Joshua Siegel, who co-authored a paper in the journal Engineering Applications of Artificial Intelligence announcing the technology, said the achievement had big implications for homes outfitted with internet of things (IoT) connectivity: “By making IoT capable of learning, you’re able to constantly update the system, so that your vacuum cleaner may trigger the circuit breaker once or twice the first week, but it’ll get smarter over time. By the time that you have 1,000 or 10,000 users contributing to the model, very few people will experience these nuisance trips because there’s so much data aggregated from so many different houses.”
What is it? A 90-pound robot called Cheetah 3 that can “leap and gallop across rough terrain, climb a staircase littered with debris, and quickly recover its balance when suddenly yanked or shoved” — according to MIT, where the machine was created. What distinguishes Cheetah 3 from other next-gen robots is that it’s able to do all this without the benefit of vision.
Why does it matter? A robot that doesn’t need to see is appealing on a number of levels: Think of the need to respond to rescue situations in dangerous environments with low visibility. The machine could also have use in industrial functions, such as for inspecting power plants.
How does it work? Cheetah 3 makes its way around with the help of two algorithms: a “contact detection algorithm” and a “model-predictive control algorithm.” The former helps it learn how to put one foot in front of the other by constantly crunching data the machine receives from its joints, gyroscopes and accelerometers — what happens when it steps on a branch versus a rock versus a hole in the ground, for instance. As the robot’s designer, MIT mechanical engineering professor Sangbae Kim, said, “This algorithm is really about, ‘When is a safe time to commit my footstep?’” And the model-predictive control algorithm helps the robot judge how much pressure it should apply to each step. Kim said, “Say someone kicks the robot sideways. When the foot is already on the ground, the algorithm decides, ‘How should I specify the forces on the foot? Because I have an undesirable velocity on the left, so I want to apply a force in the opposite direction to kill that velocity. If I apply 100 newtons in this opposite direction, what will happen a half second later?’”
What is it? Scientists at the University of Buffalo have pinpointed a single gene that initiates a hallmark of aging: senescence, the process by which cells stop dividing.
Why does it matter? You may remember from biology class that human cells grow and divide a finite number of times. When they can no longer divide (after about 50 cell-population doublings) they enter senescence, a state in which they can’t replicate but remain metabolically active. The problem with senescent cells is that they stick around, contributing to a host of age-related problems, including arthritis, cataracts and heart disease. On the flip side, a lack of senescence occurs in cancer, where cell division is unregulated. Figuring out how to control this process could lead to drugs that expand our lifespans and limit disease. “Senescence seems to have implications for old age and cancer, so understanding it is very important,” said Omer Gokcumen, an assistant professor of biological sciences at the university.
How does it work? The scientists first observed heightened CD36 gene activity in older, senescent skin and lung cells. They discovered they could induce senescence in young, healthy cells by engineering them to overproduce CD36. But the fun didn’t stop there: When the altered cells were placed in a petri dish, the surrounding normal cells stopped replicating. “What we found was very surprising,“ said Ekin Atilla-Gokcumen, an assistant professor of chemistry at the university. “Senescence is a very complex process, and we didn’t expect that altering expression of one gene could spark it, or cause the same effect in surrounding cells.” The team’s study appears in the journal Molecular Omics.