Reevaluating an approach to functional brain imaging

A new way of imaging the brain with magnetic resonance imaging (MRI) does not directly detect neural activity as originally reported, according to scientists at MIT's McGovern Institute for Brain Research.

The method, first described in 2022, generated excitement within the neuroscience community as a potentially transformative approach. But a study from the lab of MIT Professor Alan Jasanoff, reported March 27 in the journal Science Advances, demonstrates that MRI signals produced by the new method are generated in large part by the imaging process itself, not neuronal activity.

Jasanoff, a professor of biological engineering, brain and cognitive sciences, and nuclear science and engineering, as well as an associate investigator of the McGovern Institute, explains that having a noninvasive means of seeing neuronal activity in the brain is a long-sought goal for neuroscientists. The functional MRI methods that researchers currently use to monitor brain activity don't actually detect neural signaling. Instead, they use blood flow changes triggered by brain activity as a proxy. This reveals which parts of the brain are engaged during imaging, but it cannot pinpoint neural activity to precise locations, and it is too slow to truly track neurons' rapid-fire communications.

So when a team of scientists reported in 2022 a new MRI method called DIANA, for "direct imaging of neuronal activity," neuroscientists paid attention. The authors claimed that DIANA detected MRI signals in the brain that corresponded to the electrical signals of neurons, and that it acquired signals far faster than the methods now used for functional MRI.

"Everyone wants this," Jasanoff says. "If we could look at the whole brain and follow its activity with millisecond precision and know that all the signals that we're seeing have to do with cellular activity, this would be just wonderful. It could tell us all kinds of things about how the brain works and what goes wrong in disease."

Jasanoff adds that from the initial report, it was not clear what brain changes DIANA was detecting to produce such a rapid readout of neural activity. Curious, he and his team began to experiment with the method. "We wanted to reproduce it, and we wanted to understand how it worked," he says.

Recreating the MRI procedure reported by DIANA's developers, postdoc Valerie Doan Phi Van imaged the brain of a rat as an electric stimulus was delivered to one paw. Phi Van says she was excited to see an MRI signal appear in the brain's sensory cortex, exactly when and where neurons were expected to respond to the sensation on the paw. "I was able to reproduce it," she says. "I could see the signal."

With further tests of the system, however, her enthusiasm waned. To investigate the source of the signal, she disconnected the device used to stimulate the animal's paw, then repeated the imaging. Again, signals showed up in the sensory processing part of the brain. But this time, there was no reason for neurons in that area to be activated. In fact, Phi Van found, the MRI produced the same kinds of signals when the animal inside the scanner was replaced with a tube of water. It was clear DIANA's functional signals were not arising from neural activity.

Phi Van traced the source of the specious signals to the pulse program that directs DIANA's imaging process, detailing the sequence of steps the MRI scanner uses to collect data. Embedded within DIANA's pulse program was a trigger for the device that delivers sensory input to the animal inside the scanner. That synchronizes the two processes, so the stimulation occurs at a precise moment during data acquisition. That trigger appeared to be causing signals that DIANA's developers had concluded indicated neural activity.

Phi Van altered the pulse program, changing the way the stimulator was triggered. Using the updated program, the MRI scanner detected no functional signal in the brain in response to the same paw stimulation that had produced a signal before. "If you take this part of the code out, then the signal will also be gone. So that means the signal we see is an artifact of the trigger," she says.

Jasanoff and Phi Van went on to find reasons why other researchers have struggled to reproduce the results of the original DIANA report, noting that the trigger-generated signals can disappear with slight variations in the imaging process. With their postdoctoral colleague Sajal Sen, they also found evidence that cellular changes that DIANA's developers had proposed might give rise to a functional MRI signal were not related to neuronal activity.

Jasanoff and Phi Van say it was important to share their findings with the research community, particularly as efforts continue to develop new neuroimaging methods. "If people want to try to repeat any part of the study or implement any kind of approach like this, they have to avoid falling into these pits," Jasanoff says. He adds that they admire the authors of the original study for their ambition: "The community needs scientists who are willing to take risks to move the field ahead."

Source: Reevaluating an approach to functional brain imaging

Propelling atomically layered magnets toward green computers

Globally, computation is booming at an unprecedented rate, fueled by the boons of artificial intelligence. With this, the staggering energy demand of the world's computing infrastructure has become a major concern, and the development of computing devices that are far more energy-efficient is a leading challenge for the scientific community. 

Use of magnetic materials to build computing devices like memories and processors has emerged as a promising avenue for creating "beyond-CMOS" computers, which would use far less energy compared to traditional computers. Magnetization switching in magnets can be used in computation the same way that a transistor switches from open or closed to represent the 0s and 1s of binary code. 

While much of the research along this direction has focused on using bulk magnetic materials, a new class of magnetic materials — called two-dimensional van der Waals magnets — provides superior properties that can improve the scalability and energy efficiency of magnetic devices to make them commercially viable. 

Although the benefits of shifting to 2D magnetic materials are evident, their practical induction into computers has been hindered by some fundamental challenges. Until recently, 2D magnetic materials could operate only at very low temperatures, much like superconductors. So bringing their operating temperatures above room temperature has remained a primary goal. Additionally, for use in computers, it is important that they can be controlled electrically, without the need for magnetic fields. Bridging this fundamental gap, where 2D magnetic materials can be electrically switched above room temperature without any magnetic fields, could potentially catapult the translation of 2D magnets into the next generation of "green" computers.

A team of MIT researchers has now achieved this critical milestone by designing a "van der Waals atomically layered heterostructure" device where a 2D van der Waals magnet, iron gallium telluride, is interfaced with another 2D material, tungsten ditelluride. In an open-access paper published March 15 in Science Advances, the team shows that the magnet can be toggled between the 0 and 1 states simply by applying pulses of electrical current across their two-layer device. 
"Our device enables robust magnetization switching without the need for an external magnetic field, opening up unprecedented opportunities for ultra-low power and environmentally sustainable computing technology for big data and AI," says lead author Deblina Sarkar, the AT&T Career Development Assistant Professor at the MIT Media Lab and Center for Neurobiological Engineering, and head of the Nano-Cybernetic Biotrek research group. "Moreover, the atomically layered structure of our device provides unique capabilities including improved interface and possibilities of gate voltage tunability, as well as flexible and transparent spintronic technologies."

Sarkar is joined on the paper by first author Shivam Kajale, a graduate student in Sarkar's research group at the Media Lab; Thanh Nguyen, a graduate student in the Department of Nuclear Science and Engineering (NSE); Nguyen Tuan Hung, an MIT visiting scholar in NSE and an assistant professor at Tohoku University in Japan; and Mingda Li, associate professor of NSE.

Breaking the mirror symmetries 

When electric current flows through heavy metals like platinum or tantalum, the electrons get segregated in the materials based on their spin component, a phenomenon called the spin Hall effect, says Kajale. The way this segregation happens depends on the material, and particularly its symmetries.

"The conversion of electric current to spin currents in heavy metals lies at the heart of controlling magnets electrically," Kajale notes. "The microscopic structure of conventionally used materials, like platinum, have a kind of mirror symmetry, which restricts the spin currents only to in-plane spin polarization."

Kajale explains that two mirror symmetries must be broken to produce an "out-of-plane" spin component that can be transferred to a magnetic layer to induce field-free switching. "Electrical current can 'break' the mirror symmetry along one plane in platinum, but its crystal structure prevents the mirror symmetry from being broken in a second plane."

In their earlier experiments, the researchers used a small magnetic field to break the second mirror plane. To get rid of the need for a magnetic nudge, Kajale and Sarkar and colleagues looked instead for a material with a structure that could break the second mirror plane without outside help. This led them to another 2D material, tungsten ditelluride. The tungsten ditelluride that the researchers used has an orthorhombic crystal structure. The material itself has one broken mirror plane. Thus, by applying current along its low-symmetry axis (parallel to the broken mirror plane), the resulting spin current has an out-of-plane spin component that can directly induce switching in the ultra-thin magnet interfaced with the tungsten ditelluride. 

"Because it's also a 2D van der Waals material, it can also ensure that when we stack the two materials together, we get pristine interfaces and a good flow of electron spins between the materials," says Kajale. 

Becoming more energy-efficient 

Computer memory and processors built from magnetic materials use less energy than traditional silicon-based devices. And the van der Waals magnets can offer higher energy efficiency and better scalability compared to bulk magnetic material, the researchers note. 

The electrical current density used for switching the magnet translates to how much energy is dissipated during switching. A lower density means a much more energy-efficient material. "The new design has one of the lowest current densities in van der Waals magnetic materials," Kajale says. "This new design has an order of magnitude lower in terms of the switching current required in bulk materials. This translates to something like two orders of magnitude improvement in energy efficiency."

The research team is now looking at similar low-symmetry van der Waals materials to see if they can reduce current density even further. They are also hoping to collaborate with other researchers to find ways to manufacture the 2D magnetic switch devices at commercial scale. 

This work was carried out, in part, using the facilities at MIT.nano. It was funded by the Media Lab, the U.S. National Science Foundation, and the U.S. Department of Energy.

Source: Propelling atomically layered magnets toward green computers

Researchers 3D print key components for a point-of-care mass spectrometer

Mass spectrometry, a technique that can precisely identify the chemical components of a sample, could be used to monitor the health of people who suffer from chronic illnesses. For instance, a mass spectrometer can measure hormone levels in the blood of someone with hypothyroidism.

But mass spectrometers can cost several hundred thousand dollars, so these expensive machines are typically confined to laboratories where blood samples must be sent for testing. This inefficient process can make managing a chronic disease especially challenging.

"Our big vision is to make mass spectrometry local. For someone who has a chronic disease that requires constant monitoring, they could have something the size of a shoebox that they could use to do this test at home. For that to happen, the hardware has to be inexpensive," says Luis Fernando Velásquez-García, a principal research scientist in MIT's Microsystems Technology Laboratories (MTL).

He and his collaborators have taken a big step in that direction by 3D printing a low-cost ionizer — a critical component of all mass spectrometers — that performs twice as well as its state-of-the-art counterparts.

Their device, which is only a few centimeters in size, can be manufactured at scale in batches and then incorporated into a mass spectrometer using efficient, pick-and-place robotic assembly methods. Such mass production would make it cheaper than typical ionizers that often require manual labor, need expensive hardware to interface with the mass spectrometer, or must be built in a semiconductor clean room.

By 3D printing the device instead, the researchers were able to precisely control its shape and utilize special materials that helped boost its performance.

"This is a do-it-yourself approach to making an ionizer, but it is not a contraption held together with duct tape or a poor man's version of the device. At the end of the day, it works better than devices made using expensive processes and specialized instruments, and anyone can be empowered to make it," says Velásquez-García, senior author of a paper on the ionizer.

He wrote the paper with lead author Alex Kachkine, a mechanical engineering graduate student. The research is published in the Journal of the American Association for Mass Spectrometry.



Low-cost hardware

Mass spectrometers identify the contents of a sample by sorting charged particles, called ions, based on their mass-to-charge ratio. Since molecules in blood don't have an electric charge, an ionizer is used to give them a charge before they are analyzed.

Most liquid ionizers do this using electrospray, which involves applying a high voltage to a liquid sample and then firing a thin jet of charged particles into the mass spectrometer. The more ionized particles in the spray, the more accurate the measurements will be.

The MIT researchers used 3D printing, along with some clever optimizations, to produce a low-cost electrospray emitter that outperformed state-of-the-art mass spectrometry ionizer versions.

They fabricated the emitter from metal using binder jetting, a 3D printing process in which a blanket of powdered material is showered with a polymer-based glue squirted through tiny nozzles to build an object layer by layer. The finished object is heated in an oven to evaporate the glue and then consolidate the object from a bed of powder that surrounds it.

"The process sounds complicated, but it is one of the original 3D printing methods, and it is highly precise and very effective," Velásquez-García says.

Then, the printed emitters undergo an electropolishing step that sharpens it. Finally, each device is coated in zinc oxide nanowires which give the emitter a level of porosity that enables it to effectively filter and transport liquids.

Thinking outside the box

One possible problem that impacts electrospray emitters is the evaporation that can occur to the liquid sample during operation. The solvent might vaporize and clog the emitter, so engineers typically design emitters to limit evaporation.

Through modeling confirmed by experiments, the MIT team realized they could use evaporation to their advantage. They designed the emitters as externally-fed solid cones with a specific angle that leverages evaporation to strategically restrict the flow of liquid. In this way, the sample spray contains a higher ratio of charge-carrying molecules.

"We saw that evaporation can actually be a design knob that can help you optimize the performance," he says.

They also rethought the counter-electrode that applies voltage to the sample. The team optimized its size and shape, using the same binder jetting method, so the electrode prevents arcing. Arcing, which occurs when electrical current jumps a gap between two electrodes, can damage electrodes or cause overheating.

Because their electrode is not prone to arcing, they can safely increase the applied voltage, which results in more ionized molecules and better performance.

They also created a low-cost, printed circuit board with built-in digital microfluidics, which the emitter is soldered to. The addition of digital microfluidics enables the ionizer to efficiently transport droplets of liquid.

Taken together, these optimizations enabled an electrospray emitter that could operate at a voltage 24 percent higher than state-of-the-art versions. This higher voltage enabled their device to more than double the signal-to-noise ratio.

In addition, their batch processing technique could be implemented at scale, which would significantly lower the cost of each emitter and go a long way toward making a point-of-care mass spectrometer an affordable reality.

"Going back to Guttenberg, once people had the ability to print their own things, the world changed completely. In a sense, this could be more of the same. We can give people the power to create the hardware they need in their daily lives," he says.

Moving forward, the team wants to create a prototype that combines their ionizer with a 3D-printed mass filter they previously developed. The ionizer and mass filter are the key components of the device. They are also working to perfect 3D-printed vacuum pumps, which remain a major hurdle to printing an entire compact mass spectrometer.

"Miniaturization through advanced technology is slowly but surely transforming mass spectrometry, reducing manufacturing cost and increasing the range of applications. This work on fabricating electrospray sources by 3D printing also enhances signal strength, increasing sensitivity and signal-to-noise ratio and potentially opening the way to more widespread use in clinical diagnosis," says Richard Syms, professor of microsystems technology in the Department of Electrical and Electronic Engineering at Imperial College London, who was not involved with this research.

This work was supported by Empiriko Corporation.

Source: Researchers 3D print key components for a point-of-care mass spectrometer

Unlocking new science with devices that control electric power

Mo Mirvakili PhD '17 was in the middle of an experiment as a postdoc at MIT when the Covid-19 pandemic hit. Grappling with restricted access to laboratory facilities, he decided to transform his bathroom into a makeshift lab. Arranging a piece of plywood over the bathtub to support power sources and measurement devices, he conducted a study that was later published in Science Robotics, one of the top journals in the field.

The adversity made for a good story, but the truth is that it didn't take a global pandemic to force Mirvakili to build the equipment he needed to run his experiments. Even when working in some of the most well-funded labs in the world, he needed to piece together tools to bring his experiments to life.

"My journey reflects a broader truth: With determination and resourcefulness, many of us can achieve remarkable things," he says. "There are so many people who don't have access to labs yet have great ideas. We need to make it easier for them to bring their experiments to life."

That's the idea behind Seron Electronics, a company Mirvakili founded to democratize scientific experimentation. Seron develops scientific equipment that precisely sources and measures power, characterizes materials, and integrates data into a customizable software platform.

By making sophisticated experiments more accessible, Seron aims to spur a new wave of innovation across fields as diverse as microelectronics, clean energy, optics, and biomedicine.

"Our goal is to become one of the leaders in providing accurate and affordable solutions for researchers," Mirvakili says. "This vision extends beyond academia to include companies, governments, nonprofits, and even high school students. With Seron's devices, anyone can conduct high-quality experiments, regardless of their background or resources."

Feeling the need for constant power

Mirvakili earned his bachelor's and master's degrees in electrical engineering, followed by a PhD in mechanical engineering under MIT Professor Ian Hunter, which involved developing a class of high-performance thermal artificial muscles, including nylon artificial muscles. During that time, Mirvakili needed to precisely control the amount of energy that flowed through his experimental setups, but he couldn't find anything online that would solve his problem.

"I had access to all sorts of high-end equipment in our lab and the department," Mirvakili recalls. "It's all the latest, state-of-the-art stuff. But I had to bundle all these outside tools together for my work."

After completing his PhD, Mirvakili joined Institute Professor Bob Langer's lab as a postdoc, where he worked directly with Langer on a totally different problem in biomedical engineering. In Langer's famously prolific lab, he saw researchers struggling to control temperatures at the microscale for a device that was encapsulating drugs.

Mirvakili realized the researchers were ultimately struggling with the same set of problems: the need to precisely control electric current, voltage, and power. Those are also problems Mirvakili has seen in his more recent research into energy storage and solar cells. After speaking with researchers at conferences from around the world to confirm the need was widespread, he started Seron Electronics.

Seron calls the first version of its products the SE Programmable Power Platforms. The platforms allow users to source and measure precisely defined quantities of electrical voltage, current, power, and charge through a desktop application with minimal signal interference, or noise.

The equipment can be used to study things like semiconductor devices, actuators, and energy storage devices, or to precisely charge batteries without damaging their performance.

The equipment can also be used to study material performance because it can measure how materials react to precise electrical stimulation at a high resolution, and for quality control because it can test chips and flag problems.

The use cases are varied, but Seron's overarching goal is to enable more innovation faster.

"Because our system is so intuitive, you reduce the time to get results," Mirvakili says. "You can set it up in less than five minutes. It's plug-and-play. Researchers tell us it speeds things up a lot."

New frontiers

In a recent paper Mirvakili coauthored with MIT research affiliate Ehsan Haghighat, Seron's equipment provided constant power to a thermal artificial muscle that integrated machine learning to give it a sort of muscle memory. In another study Mirvakili was not involved in, a nonprofit research organization used Seron's equipment to identify a new, sustainable sensor material they are in the process of commercializing.

Many uses of the machines have come as a surprise to Seron's team, and they expect to see a new wave of applications when they release a cheaper, portable version of Seron's machines this summer. That could include the development of new bedside monitors for patients that can detect diseases, or remote sensors for field work.

Mirvakili thinks part of the beauty of Seron's devices is that people in the company don't have to dream up the experiments themselves. Instead, they can focus on providing powerful scientific tools and let the research community decide on the best ways to use them.

"Because of the size and the cost of this new device, it will really open up the possibilities for researchers," Mirvakili says. "Anyone who has a good idea should be able to turn that idea into reality with our equipment and solutions. In my mind, the applications are really unimaginable and endless."

Source: Unlocking new science with devices that control electric power

MIT researchers discover "neutronic molecules"

Neutrons are subatomic particles that have no electric charge, unlike protons and electrons. That means that while the electromagnetic force is responsible for most of the interactions between radiation and materials, neutrons are essentially immune to that force.



Instead, neutrons are held together inside an atom's nucleus solely by something called the strong force, one of the four fundamental forces of nature. As its name implies, the force is indeed very strong, but only at very close range — it drops off so rapidly as to be negligible beyond 1/10,000 the size of an atom. But now, researchers at MIT have found that neutrons can actually be made to cling to particles called quantum dots, which are made up of tens of thousands of atomic nuclei, held there just by the strong force.



The new finding may lead to useful new tools for probing the basic properties of materials at the quantum level, including those arising from the strong force, as well as exploring new kinds of quantum information processing devices. The work is reported this week in the journal ACS Nano, in a paper by MIT graduate students Hao Tang and Guoqing Wang and MIT professors Ju Li and Paola Cappellaro of the Department of Nuclear Science and Engineering.



Neutrons are widely used to probe material properties using a method called neutron scattering, in which a beam of neutrons is focused on a sample, and the neutrons that bounce off the material's atoms can be detected to reveal the material's internal structure and dynamics.



But until this new work, nobody thought that these neutrons might actually stick to the materials they were probing. "The fact that [the neutrons] can be trapped by the materials, nobody seems to know about that," says Li, who is also a professor of materials science and engineering. "We were surprised that this exists, and that nobody had talked about it before, among the experts we had checked with," he says.



The reason this new finding is so surprising, Li explains, is because neutrons don't interact with electromagnetic forces. Of the four fundamental forces, gravity and the weak force "are generally not important for materials," he says. "Pretty much everything is electromagnetic interaction, but in this case, since the neutron doesn't have a charge, the interaction here is through the strong interaction, and we know that is very short-range. It is effective at a range of 10 to the minus 15 power," or one quadrillionth, of a meter.



"It's very small, but it's very intense," he says of this force that holds the nuclei of atoms together. "But what's interesting is we've got these many thousands of nuclei in this neutronic quantum dot, and that's able to stabilize these bound states, which have much more diffuse wavefunctions at tens of nanometers [billionths of a meter].  These neutronic bound states in a quantum dot are actually quite akin to Thomson's plum pudding model of an atom, after his discovery of the electron."



It was so unexpected, Li calls it "a pretty crazy solution to a quantum mechanical problem." The team calls the newly discovered state an artificial "neutronic molecule."



These neutronic molecules are made from quantum dots, which are tiny crystalline particles, collections of atoms so small that their properties are governed more by the exact size and shape of the particles than by their composition. The discovery and controlled production of quantum dots were the subject of the 2023 Nobel Prize in Chemistry, awarded to MIT Professor Moungi Bawendi and two others.



"In conventional quantum dots, an electron is trapped by the electromagnetic potential created by a macroscopic number of atoms, thus its wavefunction extends to about 10 nanometers, much larger than a typical atomic radius," says Cappellaro. "Similarly, in these nucleonic quantum dots, a single neutron can be trapped by a nanocrystal, with a size well beyond the range of the nuclear force, and display similar quantized energies." While these energy jumps give quantum dots their colors, the neutronic quantum dots could be used for storing quantum information.





This work is based on theoretical calculations and computational simulations. "We did it analytically in two different ways, and eventually also verified it numerically," Li says. Although the effect had never been described before, he says, in principle there's no reason it couldn't have been found much sooner: "Conceptually, people should have already thought about it," he says, but as far as the team has been able to determine, nobody did.



Part of the difficulty in doing the computations is the very different scales involved: The binding energy of a neutron to the quantum dots they were attaching to is about one-trillionth that of previously known conditions where the neutron is bound to a small group of nucleons. For this work, the team used an analytical tool called Green's function to demonstrate that the strong force was sufficient to capture neutrons with a quantum dot with a minimum radius of 13 nanometers.

Then, the researchers did detailed simulations of specific cases, such as the use of a lithium hydride nanocrystal, a material being studied as a possible storage medium for hydrogen. They showed that the binding energy of the neutrons to the nanocrystal is dependent on the exact dimensions and shape of the crystal, as well as the nuclear spin polarizations of the nuclei compared to that of the neutron. They also calculated similar effects for thin films and wires of the material as opposed to particles.



But Li says that actually creating such neutronic molecules in the lab, which among other things requires specialized equipment to maintain temperatures in the range of a few thousandths of a Kelvin above absolute zero, is something that other researchers with the appropriate expertise will have to undertake.



Li notes that "artificial atoms" made up of assemblages of atoms that share properties and can behave in many ways like a single atom have been used to probe many properties of real atoms. Similarly, he says, these artificial molecules provide "an interesting model system" that might be used to study "interesting quantum mechanical problems that one can think about," such as whether these neutronic molecules will have a shell structure that mimics the electron shell structure of atoms.



"One possible application," he says, "is maybe we can precisely control the neutron state. By changing the way the quantum dot oscillates, maybe we can shoot the neutron off in a particular direction." Neutrons are powerful tools for such things as triggering both fission and fusion reactions, but so far it has been difficult to control individual neutrons. These new bound states could provide much greater degrees of control over individual neutrons, which could play a role in the development of new quantum information systems, he says.



"One idea is to use it to manipulate the neutron, and then the neutron will be able to affect other nuclear spins," Li says. In that sense, he says, the neutronic molecule could serve as a mediator between the nuclear spins of separate nuclei — and this nuclear spin is a property that is already being used as a basic storage unit, or qubit, in developing quantum computer systems.



"The nuclear spin is like a stationary qubit, and the neutron is like a flying qubit," he says. "That's one potential application." He adds that this is "quite different from electromagnetics-based quantum information processing, which is so far the dominant paradigm. So, regardless of whether it's superconducting qubits or it's trapped ions or nitrogen vacancy centers, most of these are based on electromagnetic interactions." In this new system, instead, "we have neutrons and nuclear spin. We're just starting to explore what we can do with it now."



Another possible application, he says, is for a kind of imaging, using neutral activation analysis. "Neutron imaging complements X-ray imaging because neutrons are much more strongly interacting with light elements," Li says. It can also be used for materials analysis, which can provide information not only about elemental composition but even about the different isotopes of those elements. "A lot of the chemical imaging and spectroscopy doesn't tell us about the isotopes," whereas the neutron-based method could do so, he says.



The research was supported by the U.S. Office of Naval Research.

Source: MIT researchers discover "neutronic molecules"

A new computational technique could make it easier to engineer useful proteins

To engineer proteins with useful functions, researchers usually begin with a natural protein that has a desirable function, such as emitting fluorescent light, and put it through many rounds of random mutation that eventually generate an optimized version of the protein.

This process has yielded optimized versions of many important proteins, including green fluorescent protein (GFP). However, for other proteins, it has proven difficult to generate an optimized version. MIT researchers have now developed a computational approach that makes it easier to predict mutations that will lead to better proteins, based on a relatively small amount of data.

Using this model, the researchers generated proteins with mutations that were predicted to lead to improved versions of GFP and a protein from adeno-associated virus (AAV), which is used to deliver DNA for gene therapy. They hope it could also be used to develop additional tools for neuroscience research and medical applications.

"Protein design is a hard problem because the mapping from DNA sequence to protein structure and function is really complex. There might be a great protein 10 changes away in the sequence, but each intermediate change might correspond to a totally nonfunctional protein. It's like trying to find your way to the river basin in a mountain range, when there are craggy peaks along the way that block your view. The current work tries to make the riverbed easier to find," says Ila Fiete, a professor of brain and cognitive sciences at MIT, a member of MIT's McGovern Institute for Brain Research, director of the K. Lisa Yang Integrative Computational Neuroscience Center, and one of the senior authors of the study.

Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health at MIT, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science at MIT, are also senior authors of an open-access paper on the work, which will be presented at the International Conference on Learning Representations in May. MIT graduate students Andrew Kirjner and Jason Yim are the lead authors of the study. Other authors include Shahar Bracha, an MIT postdoc, and Raman Samusevich, a graduate student at Czech Technical University.

Optimizing proteins

Many naturally occurring proteins have functions that could make them useful for research or medical applications, but they need a little extra engineering to optimize them. In this study, the researchers were originally interested in developing proteins that could be used in living cells as voltage indicators. These proteins, produced by some bacteria and algae, emit fluorescent light when an electric potential is detected. If engineered for use in mammalian cells, such proteins could allow researchers to measure neuron activity without using electrodes.

While decades of research have gone into engineering these proteins to produce a stronger fluorescent signal, on a faster timescale, they haven't become effective enough for widespread use. Bracha, who works in Edward Boyden's lab at the McGovern Institute, reached out to Fiete's lab to see if they could work together on a computational approach that might help speed up the process of optimizing the proteins.

"This work exemplifies the human serendipity that characterizes so much science discovery," Fiete says. "It grew out of the Yang Tan Collective retreat, a scientific meeting of researchers from multiple centers at MIT with distinct missions unified by the shared support of K. Lisa Yang. We learned that some of our interests and tools in modeling how brains learn and optimize could be applied in the totally different domain of protein design, as being practiced in the Boyden lab."

For any given protein that researchers might want to optimize, there is a nearly infinite number of possible sequences that could generated by swapping in different amino acids at each point within the sequence. With so many possible variants, it is impossible to test all of them experimentally, so researchers have turned to computational modeling to try to predict which ones will work best.

In this study, the researchers set out to overcome those challenges, using data from GFP to develop and test a computational model that could predict better versions of the protein.

They began by training a type of model known as a convolutional neural network (CNN) on experimental data consisting of GFP sequences and their brightness — the feature that they wanted to optimize.

The model was able to create a "fitness landscape" — a three-dimensional map that depicts the fitness of a given protein and how much it differs from the original sequence — based on a relatively small amount of experimental data (from about 1,000 variants of GFP).

These landscapes contain peaks that represent fitter proteins and valleys that represent less fit proteins. Predicting the path that a protein needs to follow to reach the peaks of fitness can be difficult, because often a protein will need to undergo a mutation that makes it less fit before it reaches a nearby peak of higher fitness. To overcome this problem, the researchers used an existing computational technique to "smooth" the fitness landscape.

Once these small bumps in the landscape were smoothed, the researchers retrained the CNN model and found that it was able to reach greater fitness peaks more easily. The model was able to predict optimized GFP sequences that had as many as seven different amino acids from the protein sequence they started with, and the best of these proteins were estimated to be about 2.5 times fitter than the original.

"Once we have this landscape that represents what the model thinks is nearby, we smooth it out and then we retrain the model on the smoother version of the landscape," Kirjner says. "Now there is a smooth path from your starting point to the top, which the model is now able to reach by iteratively making small improvements. The same is often impossible for unsmoothed landscapes." 

Proof-of-concept

The researchers also showed that this approach worked well in identifying new sequences for the viral capsid of adeno-associated virus (AAV), a viral vector that is commonly used to deliver DNA. In that case, they optimized the capsid for its ability to package a DNA payload.

"We used GFP and AAV as a proof-of-concept to show that this is a method that works on data sets that are very well-characterized, and because of that, it should be applicable to other protein engineering problems," Bracha says.

The researchers now plan to use this computational technique on data that Bracha has been generating on voltage indicator proteins.

"Dozens of labs having been working on that for two decades, and still there isn't anything better," she says. "The hope is that now with generation of a smaller data set, we could train a model in silico and make predictions that could be better than the past two decades of manual testing."

The research was funded, in part, by the U.S. National Science Foundation, the Machine Learning for Pharmaceutical Discovery and Synthesis consortium, the Abdul Latif Jameel Clinic for Machine Learning in Health, the DTRA Discovery of Medical Countermeasures Against New and Emerging threats program, the DARPA Accelerated Molecular Discovery program, the Sanofi Computational Antibody Design grant, the U.S. Office of Naval Research, the Howard Hughes Medical Institute, the National Institutes of Health, the K. Lisa Yang ICoN Center, and the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics at MIT.

Source: A new computational technique could make it easier to engineer useful proteins

Characterizing social networks

People tend to connect with others who are like them. Alumni from the same alma mater are more likely to collaborate over a research project together, or individuals with the same political beliefs are more likely to join the same political parties, attend rallies, and engage in online discussions. This sociology concept, called homophily, has been observed in many network science studies. But if like-minded individuals cluster in online and offline spaces to reinforce each other's ideas and form synergies, what does that mean for society?

Researchers at MIT wanted to investigate homophily further to understand how groups of three or more interact in complex societal settings. Prior research on understanding homophily has studied relationships between pairs of people. For example, when two members of Congress co-sponsor a bill, they are likely to be from the same political party.

However, less is known about whether group interactions between three or more people are likely to occur between similar individuals. If three members of Congress co-sponsor a bill together, are all three likely to be members of the same party, or would we expect more bipartisanship? When the researchers tried to extend traditional methods to measure homophily in these larger group interactions, they found the results can be misleading.

"We found that homophily observed in pairs, or one-to-one interactions, can make it seem like there's more homophily in larger groups than there really is," says Arnab Sarker, graduate student in the Institute for Data, Systems and Society (IDSS) and lead author of the study published in Proceedings of the National Academy of Sciences. "The previous measure didn't account for the way in which two people already know each other in friendship settings," he adds.

To address this issue, Sarker, along with co-authors Natalie Northrup '22 and Ali Jadbabaie, the JR East Professor of Engineering, head of the Department of Civil and Environmental Engineering, and core faculty member of IDSS, developed a new way of measuring homophily. Borrowing tools from algebraic topology, a subfield in mathematics typically applied in physics, they developed a new measure to understand whether homophily occurred in group interactions.

The new measure, called simplicial homophily, separates the homophily seen in one-on-one interactions from those in larger group interactions and is based on the mathematical concept of a simplicial complex. The researchers tested this new measure with real-world data from 16 different datasets and found that simplicial homophily provides more accurate insights into how similar things interact in larger groups. Interestingly, the new measure can better identify instances where there is a lack of similarity in larger group interactions, thus rectifying a weakness observed in the previous measure.

One such example of this instance was demonstrated in the dataset from the global hotel booking website, Trivago. They found that when travelers are looking at two hotels in one session, they often pick hotels that are close to one another geographically. But when they look at more than two hotels in one session, they are more likely to be searching for hotels that are farther apart from one another (for example, if they are taking a vacation with multiple stops). The new method showed "anti-homophily" — instead of similar hotels being chosen together, different hotels were chosen together.

"Our measure controls for pairwise connections and is suggesting that there's more diversity in the hotels that people are looking for as group size increases, which is an interesting economic result," says Sarker.

Additionally, they discovered that simplicial homophily can help identify when certain characteristics are important for predicting if groups will interact in the future. They found that when there's a lot of similarity or a lot of difference between individuals who already interact in groups, then knowing individual characteristics can help predict their connection to each other in the future.

Northrup was an undergraduate researcher on the project and worked with Sarker and Jadbabaie over three semesters before she graduated. The project gave her an opportunity to take some of the concepts she learned in the classroom and apply them.

"Working on this project, I really dove into building out the higher-order network model, and understanding the network, the math, and being able to implement it at a large scale," says Northrup, who was in the civil and environmental engineering systems track with a double major in economics.

The new measure opens up opportunities to study complex group interactions in a broad range of network applications, from ecology to traffic and socioeconomics. One of the areas Sarker has interest in exploring is the group dynamics of people finding jobs through social networks. "Does higher-order homophily affect how people get information about jobs?" he asks.    

Northrup adds that it could also be used to evaluate interventions or specific policies to connect people with job opportunities outside of their network. "You can even use it as a measurement to evaluate how effective that might be."

The research was supported through funding from a Vannevar Bush Fellowship from the Office of the U.S. Secretary of Defense and from the U.S. Army Research Office Multidisciplinary University Research Initiative.

Source: Characterizing social networks

Does technology help or hurt employment?

This is part 2 of a two-part MIT News feature examining new job creation in the U.S. since 1940, based on new research from Ford Professor of Economics David Autor. Part 1 is available here.



Ever since the Luddites were destroying machine looms, it has been obvious that new technologies can wipe out jobs. But technical innovations also create new jobs: Consider a computer programmer, or someone installing solar panels on a roof.



Overall, does technology replace more jobs than it creates? What is the net balance between these two things? Until now, that has not been measured. But a new research project led by MIT economist David Autor has developed an answer, at least for U.S. history since 1940.



The study uses new methods to examine how many jobs have been lost to machine automation, and how many have been generated through "augmentation," in which technology creates new tasks. On net, the study finds, and particularly since 1980, technology has replaced more U.S. jobs than it has generated.



"There does appear to be a faster rate of automation, and a slower rate of augmentation, in the last four decades, from 1980 to the present, than in the four decades prior," says Autor, co-author of a newly published paper detailing the results.



However, that finding is only one of the study's advances. The researchers have also developed an entirely new method for studying the issue, based on an analysis of tens of thousands of U.S. census job categories in relation to a comprehensive look at the text of U.S. patents over the last century. That has allowed them, for the first time, to quantify the effects of technology over both job loss and job creation.



Previously, scholars had largely just been able to quantify job losses produced by new technologies, not job gains.



"I feel like a paleontologist who was looking for dinosaur bones that we thought must have existed, but had not been able to find until now," Autor says. "I think this research breaks ground on things that we suspected were true, but we did not have direct proof of them before this study."



The paper, "New Frontiers: The Origins and Content of New Work, 1940-2018," appears in the Quarterly Journal of Economics. The co-authors are Autor, the Ford Professor of Economics; Caroline Chin, a PhD student in economics at MIT; Anna Salomons, a professor in the School of Economics at Utrecht University; and Bryan Seegmiller SM '20, PhD '22, an assistant professor at the Kellogg School of Northwestern University.



Automation versus augmentation



The study finds that overall, about 60 percent of jobs in the U.S. represent new types of work, which have been created since 1940. A century ago, that computer programmer may have been working on a farm.



To determine this, Autor and his colleagues combed through about 35,000 job categories listed in the U.S. Census Bureau reports, tracking how they emerge over time. They also used natural language processing tools to analyze the text of every U.S. patent filed since 1920. The research examined how words were "embedded" in the census and patent documents to unearth related passages of text. That allowed them to determine links between new technologies and their effects on employment.



"You can think of automation as a machine that takes a job's inputs and does it for the worker," Autor explains. "We think of augmentation as a technology that increases the variety of things that people can do, the quality of things people can do, or their productivity."



From about 1940 through 1980, for instance, jobs like elevator operator and typesetter tended to get automated. But at the same time, more workers filled roles such as shipping and receiving clerks, buyers and department heads, and civil and aeronautical engineers, where technology created a need for more employees. 



From 1980 through 2018, the ranks of cabinetmakers and machinists, among others, have been thinned by automation, while, for instance, industrial engineers, and operations and systems researchers and analysts, have enjoyed growth.



Ultimately, the research suggests that the negative effects of automation on employment were more than twice as great in the 1980-2018 period as in the 1940-1980 period. There was a more modest, and positive, change in the effect of augmentation on employment in 1980-2018, as compared to 1940-1980.



"There's no law these things have to be one-for-one balanced, although there's been no period where we haven't also created new work," Autor observes.



What will AI do?



The research also uncovers many nuances in this process, though, since automation and augmentation often occur within the same industries. It is not just that technology decimates the ranks of farmers while creating air traffic controllers. Within the same large manufacturing firm, for example, there may be fewer machinists but more systems analysts.



Relatedly, over the last 40 years, technological trends have exacerbated a gap in wages in the U.S., with highly educated professionals being more likely to work in new fields, which themselves are split between high-paying and lower-income jobs.



"The new work is bifurcated," Autor says. "As old work has been erased in the middle, new work has grown on either side."



As the research also shows, technology is not the only thing driving new work. Demographic shifts also lie behind growth in numerous sectors of the service industries. Intriguingly, the new research also suggests that large-scale consumer demand also drives technological innovation. Inventions are not just supplied by bright people thinking outside the box, but in response to clear societal needs.



The 80 years of data also suggest that future pathways for innovation, and the employment implications, are hard to forecast. Consider the possible uses of AI in workplaces.



"AI is really different," Autor says. "It may substitute some high-skill expertise but may complement decision-making tasks. I think we're in an era where we have this new tool and we don't know what's good for. New technologies have strengths and weaknesses and it takes a while to figure them out. GPS was invented for military purposes, and it took decades for it to be in smartphones."



He adds: "We're hoping our research approach gives us the ability to say more about that going forward."



As Autor recognizes, there is room for the research team's methods to be further refined. For now, he believes the research open up new ground for study.



"The missing link was documenting and quantifying how much technology augments people's jobs," Autor says. "All the prior measures just showed automation and its effects on displacing workers. We were amazed we could identify, classify, and quantify augmentation. So that itself, to me, is pretty foundational."



Support for the research was provided, in part, by The Carnegie Corporation; Google; Instituut Gak; the MIT Work of the Future Task Force; Schmidt Futures; the Smith Richardson Foundation; and the Washington Center for Equitable Growth.

Source: Does technology help or hurt employment?

Most work is new work, long-term study of U.S. census data shows

This is part 1 of a two-part MIT News feature examining new job creation in the U.S. since 1940, based on new research from Ford Professor of Economics David Autor. Part 2 is available here.



In 1900, Orville and Wilbur Wright listed their occupations as "Merchant, bicycle" on the U.S. census form. Three years later, they made their famous first airplane flight in Kitty Hawk, North Carolina. So, on the next U.S. census, in 1910, the brothers each called themselves "Inventor, aeroplane." There weren't too many of those around at the time, however, and it wasn't until 1950 that "Airplane designer" became a recognized census category.



Distinctive as their case may be, the story of the Wright brothers tells us something important about employment in the U.S. today. Most work in the U.S. is new work, as U.S. census forms reveal. That is, a majority of jobs are in occupations that have only emerged widely since 1940, according to a major new study of U.S. jobs led by MIT economist David Autor.



"We estimate that about six out of 10 jobs people are doing at present didn't exist in 1940," says Autor, co-author of a newly published paper detailing the results. "A lot of the things that we do today, no one was doing at that point. Most contemporary jobs require expertise that didn't exist back then, and was not relevant at that time."



This finding, covering the period 1940 to 2018, yields some larger implications. For one thing, many new jobs are created by technology. But not all: Some come from consumer demand, such as health care services jobs for an aging population.



On another front, the research shows a notable divide in recent new-job creation: During the first 40 years of the 1940-2018 period, many new jobs were middle-class manufacturing and clerical jobs, but in the last 40 years, new job creation often involves either highly paid professional work or lower-wage service work.



Finally, the study brings novel data to a tricky question: To what extent does technology create new jobs, and to what extent does it replace jobs?



The paper, "New Frontiers: The Origins and Content of New Work, 1940-2018," appears in the Quarterly Journal of Economics. The co-authors are Autor, the Ford Professor of Economics at MIT; Caroline Chin, a PhD student in economics at MIT; Anna Salomons, a professor in the School of Economics at Utrecht University; and Bryan Seegmiller SM '20, PhD '22, an assistant professor at the Kellogg School of Northwestern University.



"This is the hardest, most in-depth project I've ever done in my research career," Autor adds. "I feel we've made progress on things we didn't know we could make progress on."



"Technician, fingernail"



To conduct the study, the scholars dug deeply into government data about jobs and patents, using natural language processing techniques that identified related descriptions in patent and census data to link innovations and subsequent job creation. The U.S. Census Bureau tracks the emerging job descriptions that respondents provide — like the ones the Wright brothers wrote down. Each decade's jobs index lists about 35,000 occupations and 15,000 specialized variants of them.



Many new occupations are straightforwardly the result of new technologies creating new forms of work. For instance, "Engineers of computer applications" was first codified in 1970, "Circuit layout designers" in 1990, and "Solar photovoltaic electrician" made its debut in 2018.



"Many, many forms of expertise are really specific to a technology or a service," Autor says. "This is quantitatively a big deal."



He adds: "When we rebuild the electrical grid, we're going to create new occupations — not just electricians, but the solar equivalent, i.e., solar electricians. Eventually that becomes a specialty. The first objective of our study is to measure [this kind of process]; the second is to show what it responds to and how it occurs; and the third is to show what effect automation has on employment."



On the second point, however, innovations are not the only way new jobs emerge. The wants and needs of consumers also generate new vocations. As the paper notes, "Tattooers" became a U.S. census job category in 1950, "Hypnotherapists" was codified in 1980, and "Conference planners" in 1990. Also, the date of U.S. Census Bureau codification is not the first time anyone worked in those roles; it is the point at which enough people had those jobs that the bureau recognized the work as a substantial employment category. For instance, "Technician, fingernail" became a category in 2000.



"It's not just technology that creates new work, it's new demand," Autor says. An aging population of baby boomers may be creating new roles for personal health care aides that are only now emerging as plausible job categories.



All told, among "professionals," essentially specialized white-collar workers, about 74 percent of jobs in the area have been created since 1940. In the category of "health services" — the personal service side of health care, including general health aides, occupational therapy aides, and more — about 85 percent of jobs have emerged in the same time. By contrast, in the realm of manufacturing, that figure is just 46 percent.



Differences by degree



The fact that some areas of employment feature relatively more new jobs than others is one of the major features of the U.S. jobs landscape over the last 80 years. And one of the most striking things about that time period, in terms of jobs, is that it consists of two fairly distinct 40-year periods.



In the first 40 years, from 1940 to about 1980, the U.S. became a singular postwar manufacturing powerhouse, production jobs grew, and middle-income clerical and other office jobs grew up around those industries.



But in the last four decades, manufacturing started receding in the U.S., and automation started eliminating clerical work. From 1980 to the present, there have been two major tracks for new jobs: high-end and specialized professional work, and lower-paying service-sector jobs, of many types. As the authors write in the paper, the U.S. has seen an "overall polarization of occupational structure."



That corresponds with levels of education. The study finds that employees with at least some college experience are about 25 percent more likely to be working in new occupations than those who possess less than a high school diploma.



"The real concern is for whom the new work has been created," Autor says. "In the first period, from 1940 to 1980, there's a lot of work being created for people without college degrees, a lot of clerical work and production work, middle-skill work. In the latter period, it's bifurcated, with new work for college graduates being more and more in the professions, and new work for noncollege graduates being more and more in services."



Still, Autor adds, "This could change a lot. We're in a period of potentially consequential technology transition."



At the moment, it remains unclear how, and to what extent, evolving technologies such as artificial intelligence will affect the workplace. However, this is also a major issue addressed in the current research study: How much does new technology augment employment, by creating new work and viable jobs, and how much does new technology replace existing jobs, through automation? In their paper, Autor and his colleagues have produced new findings on that topic, which are outlined in part 2 of this MIT News series.



Support for the research was provided, in part, by the Carnegie Corporation; Google; Instituut Gak; the MIT Work of the Future Task Force; Schmidt Futures; the Smith Richardson Foundation; and the Washington Center for Equitable Growth.

Source: Most work is new work, long-term study of U.S. census data shows

New competition rules come with trade-offsNew competition rules come with trade-offsDirector, Economic Policy

A look at the effects of some of the changes we've made to comply with Europe's recently enacted Digital Markets Act.A look at the effects of some of the changes we've made to comply with Europe's recently enacted Digital Markets Act.

Source: New competition rules come with trade-offsNew competition rules come with trade-offsDirector, Economic Policy