The new method, for which the team has already applied for patent protection, is described in the journal Physical Review X, in a paper by graduate student Guoqing Wang, professor of nuclear science and engineering and of physics Paola Cappellaro, and four others at MIT and Lincoln Laboratory.

Quantum sensors can take many forms; they’re essentially systems in which some particles are in such a delicately balanced state that they are affected by even tiny variations in the fields they are exposed to. These can take the form of neutral atoms, trapped ions, and solid-state spins, and research using such sensors has grown rapidly. For example, physicists use them to investigate exotic states of matter, including so-called time crystals and topological phases, while other researchers use them to characterize practical devices such as experimental quantum memory or computation devices. But many other phenomena of interest span a much broader frequency range than today’s quantum sensors can detect.

The new system the team devised, which they call a quantum mixer, injects a second frequency into the detector using a beam of microwaves. This converts the frequency of the field being studied into a different frequency — the difference between the original frequency and that of the added signal — which is tuned to the specific frequency that the detector is most sensitive to. This simple process enables the detector to home in on any desired frequency at all, with no loss in the nanoscale spatial resolution of the sensor.

In their experiments, the team used a specific device based on an array of nitrogen-vacancy centers in diamond, a widely used quantum sensing system, and successfully demonstrated detection of a signal with a frequency of 150 megahertz, using a qubit detector with frequency of 2.2 gigahertz — a detection that would be impossible without the quantum multiplexer. They then did detailed analyses of the process by deriving a theoretical framework, based on Floquet theory, and testing the numerical predictions of that theory in a series of experiments.

While their tests used this specific system, Wang says, “the same principle can be also applied to any kind of sensors or quantum devices.” The system would be self-contained, with the detector and the source of the second frequency all packaged in a single device.

Wang says that this system could be used, for example, to characterize in detail the performance of a microwave antenna. “It can characterize the distribution of the field [generated by the antenna] with nanoscale resolution, so it’s very promising in that direction,” he says.

There are other ways of altering the frequency sensitivity of some quantum sensors, but these require the use of large devices and strong magnetic fields that blur out the fine details and make it impossible to achieve the very high resolution that the new system offers. In such systems today, Wang says, “you need to use a strong magnetic field to tune the sensor, but that magnetic field can potentially break the quantum material properties, which can influence the phenomena that you want to measure.”

The system may open up new applications in biomedical fields, according to Cappellaro, because it can make accessible a range of frequencies of electrical or magnetic activity at the level of a single cell. It would be very difficult to get useful resolution of such signals using current quantum sensing systems, she says. It may be possible using this system to detect output signals from a single neuron in response to some stimulus, for example, which typically include a great deal of noise, making such signals hard to isolate.

The system could also be used to characterize in detail the behavior of exotic materials such as 2D materials that are being intensely studied for their electromagnetic, optical, and physical properties.

In ongoing work, the team is exploring the possibility of finding ways to expand the system to be able to probe a range of frequencies at once, rather than the present system’s single frequency targeting. They will also be continuing to define the system’s capabilities using more powerful quantum sensing devices at Lincoln Laboratory, where some members of the research team are based.

The team included Yi-Xiang Liu at MIT and Jennifer Schloss, Scott Alsid and Danielle Braje at Lincoln Laboratory. The work was supported by the Defense Advanced Research Projects Agency (DARPA) and Q-Diamond.

The study acquainted 49 bats with a series of ringtones that attracted their attention, and trained them to associate flying toward just one of the tones with a reward: a baitfish snack.

Between one and four years later, eight of those bats were recaptured and exposed again to the food-related ringtone. All of them flew toward the sound, and six flew all the way to the speaker and grabbed the food reward, meaning they expected to find food. Control bats without previous training on the sounds were comparatively unmoved by the exposure to the unfamiliar tones.

“I was surprised — I went into this thinking that at least a year would be a reasonable time for them to remember, given all the other things they need to know and given that long-term memory does have real costs. Four years strikes me as a long time to hold on to a sound that you might never hear again,” said lead author May Dixon, a postdoctoral scholar in evolution, ecology and organismal biology at The Ohio State University.

Dixon led this study at the Smithsonian Tropical Research Institute in Panama while she was a graduate student at the University of Texas at Austin.

“The environment that previous generations experienced can be extremely different from the environment an animal is born into — and it may also change throughout an animal’s life,” she said. “Trying to figure out how animals use learning and memory is one way to figure out how they’re going to make it in a life full of change in the modern world.”

The study is published today (June 20, 2022) in Current Biology.

In the first phase, individual frog-eating bats captured for a series of cognition tests were exposed to a highly attractive sound in the lab: the mating call of the male túngara frog, one of this species of bats’ preferred prey. Flying to that sound was rewarded with a piece of baitfish placed on mesh above the speaker.

Over time, the sound was mingled with and gradually replaced by a ringtone, but the reward was the same. Researchers then introduced three other ringtones, none of which was connected to a food reward. Bats were trained to discern the differences and eventually no longer flew toward the unrewarded sounds. Each bat secured at least 40 snacks by flying to the trained ringtone over 11 to 27 days. All bats were microchipped and returned to the wild.

Beginning a year later and for three additional years, Dixon captured bats and identified eight from the initial trial by their microchips. In a follow-up test of their response to the original rewarded ringtone, all eight trained bats quickly flew to the sound and were able to tell the difference between that ringtone and a new, steady tone, though many of the bats did fly to an unrewarded sound from the initial training.

When 17 untrained bats were exposed to these sounds, they mostly twitched their ears in response to the sounds, but didn’t fly toward them.

“The study taught us a lot because there are relatively few studies of long-term memory in wild animals and we don’t have systematic understanding of long-term memories in nature yet,” Dixon said. “If we can collect additional data on different species of bats, we could pick this apart and see what life histories select for long memories.”

The paper lists 39 previous studies that have documented memory in species ranging from fish, birds and bats to goats and primates. Some of the longest of those experiments — documenting memory in sea lions for 10 years, tortoises for nine years and dolphins for 20 years — were all conducted on animals that lived in captivity the entire time.

“Being able to study memory in the wild is important,” said study co-author Gerald Carter, assistant professor of evolution, ecology and organismal biology at Ohio State. “You can’t necessarily extrapolate from the wealth of data we have on animals in the lab to what they’re facing in the wild, where there are many more things they have to remember. The environment is different and the brain is different in the wild versus captivity.”

Despite the human tendency to assume a long memory gives our species the intelligence advantage, nature shows us that memory flexibility — also called adaptive forgetting — may be important for survival.

“It’s not always true that being the smartest or having the longest memory is actually advantageous. Research has shown that fruitflies selected for improved memories can’t compete as well against other fruitflies,” Dixon said. “Just because it’s useful for humans to be so smart and have such good memories doesn’t necessarily mean it’s going to be the best thing for other animals.

“That’s why we want to figure out when these skills are actually going to help animals and when they could be a liability.”

This work was supported by the National Science Foundation and the Smithsonian Tropical Research Institute (STRI). Co-authors include Patricia Jones of Bowdoin College, Michael Ryan of STRI and UT Austin, and Rachel Page of STRI.

As light passes through the slide, an image of Australia can be seen, but when you flip the slide and look again, an image of the Sydney Opera House is visible. The pair of images created is just one example of an untapped number of possibilities.

The ability to produce two distinctly different images is possible thanks to the ANU scientists’ ability to control the direction in which light can and can’t travel at the nanoscale. The development could pave the way for new light-based devices that could lead to faster, cheaper and more reliable Internet. It could also serve as the foundation for many of the technologies of tomorrow.

Developed in collaboration with colleagues from China, Germany and Singapore, the new technology uses nanoparticles, so small that about 12,000 of them can fit within a cross-section of a human hair. These tiny particles are arranged into unique patterns on the slides.

“The particles control the flow of light like road signs control traffic on a busy road by manipulating the direction in which light can, or can’t, travel,” project leader Dr Sergey Kruk said.

“Some particles allow light to flow from left to right only, others from right to left or the pathway might be blocked in either direction.”

Dr Lei Wang, from Southeast University in China said: “While the purpose of these images is mainly artistic, they demonstrate the potential for this new technology.

“In real-world applications these nanoparticles can be assembled into complex systems that would control the flow of light in a useful manner — such as in next-generation communications infrastructure.”

According to Dr Kruk, the ability to control the flow of light at the nanoscale ensures light “goes where it’s supposed to go and doesn’t go where it’s not supposed to.”

“We exchange enormous amounts of information with the help of light. When you make a video call, say, from Australia to Europe, your voice and image get converted into short pulses of light that travel thousands of kilometres through an optical fibre over the continents and oceans,” Dr Kruk, from the ANU Nonlinear Physics Centre, said.

“Unfortunately, when we use current light-based technologies to exchange information a lot of parasitic effects might occur. Light might get scattered or reflected, which compromises your communication.

“By ensuring light flows exactly where it needs to flow, we would resolve many issues with current technologies.”

According to Dr Kruk, the development of many technologies of tomorrow will rely heavily on our ability to control light at a tiny scale.

“A wide deployment of tiny components that can control the flow of light could potentially bring technological and social changes similar to transformations brought about in the past by the development of tiny components that control the flow of electricity, which are known as diodes and transistors,” he said.

“Control over the flow of electricity at the nanoscale is what ultimately brought us modern computers and smartphones. It is therefore exciting to envision the potential of our emerging technology for controlling flow of light.”

This research was a collaboration between the Nonlinear Physics Centre at the ANU Research School of Physics, Paderborn University in Germany, Southeast University in China and A*STAR Singapore.

Distinguishing these harmful driver mutations from the neutral passengers could help researchers identify better drug targets. To boost those efforts, an MIT-led team has built a new computer model that can rapidly scan the entire genome of cancer cells and identify mutations that occur more frequently than expected, suggesting that they are driving tumor growth. This type of prediction has been challenging because some genomic regions have an extremely high frequency of passenger mutations, drowning out the signal of actual drivers

“We created a probabilistic, deep-learning method that allowed us to get a really accurate model of the number of passenger mutations that should exist anywhere in the genome,” says Maxwell Sherman, an MIT graduate student. “Then we can look all across the genome for regions where you have an unexpected accumulation of mutations, which suggests that those are driver mutations.”

In their new study, the researchers found additional mutations across the genome that appear to contribute to tumor growth in 5 to 10 percent of cancer patients. The findings could help doctors to identify drugs that would have greater chance of successfully treating those patients, the researchers say. Currently, at least 30 percent of cancer patients have no detectable driver mutation that can be used to guide treatment.

Sherman, MIT graduate student Adam Yaari, and former MIT research assistant Oliver Priebe are the lead authors of the study, which appears today in Nature Biotechnology. Bonnie Berger, the Simons Professor of Mathematics at MIT and head of the Computation and Biology group at the Computer Science and Artificial Intelligence Laboratory (CSAIL), is a senior author of the study, along with Po-Ru Loh, an assistant professor at Harvard Medical School and associate member of the Broad Institute of MIT and Harvard. Felix Dietlein, an associate professor at Harvard Medical School and Boston Children’s Hospital, is also an author of the paper.

A new tool

Since the human genome was sequenced two decades ago, researchers have been scouring the genome to try to find mutations that contribute to cancer by causing cells to grow uncontrollably or evade the immune system. This has successfully yielded targets such as epidermal growth factor receptor (EGFR), which is commonly mutated in lung tumors, and BRAF, a common driver of melanoma. Both of these mutations can now be targeted by specific drugs.

While those targets have proven useful, protein-coding genes make up only about 2 percent of the genome. The other 98 percent also contains mutations that can occur in cancer cells, but it has been much more difficult to figure out if any of those mutations contribute to cancer development.

“There has really been a lack of computational tools that allow us to search for these driver mutations outside of protein-coding regions,” Berger says. “That’s what we were trying to do here: design a computational method to let us look at not only the 2 percent of the genome that codes for proteins, but 100 percent of it.”

To do that, the researchers trained a type of computational model known as a deep neural network to search cancer genomes for mutations that occur more frequently than expected. As a first step, they trained the model on genomic data from 37 different types of cancer, which allowed the model to determine the background mutation rates for each of those types.

“The really nice thing about our model is that you train it once for a given cancer type, and it learns the mutation rate everywhere across the genome simultaneously for that particular type of cancer,” Sherman says. “Then you can query the mutations that you see in a patient cohort against the number of mutations you should expect to see.”

The data used to train the models came from the Roadmap Epigenomics Project and an international collection of data called the Pan-Cancer Analysis of Whole Genomes (PCAWG). The model’s analysis of this data gave the researchers a map of the expected passenger mutation rate across the genome, such that the expected rate in any set of regions (down to the single base pair) can be compared to the observed mutation count anywhere across the genome.

Changing the landscape

Using this model, the MIT team was able to add to the known landscape of mutations that can drive cancer. Currently, when cancer patients’ tumors are screened for cancer-causing mutations, a known driver will turn up about two-thirds of the time. The new results of the MIT study offer possible driver mutations for an additional 5 to 10 percent of the pool of patients.

One type of noncoding mutation the researchers focused on is called “cryptic splice mutations.” Most genes consist of sequences of exons, which encode protein-building instructions, and introns, which are spacer elements that usually get trimmed out of messenger RNA before it is translated into protein. Cryptic splice mutations are found in introns, where they can confuse the cellular machinery that splices them out. This results in introns being included when they shouldn’t be.

Using their model, the researchers found that many cryptic splice mutations appear to disrupt tumor suppressor genes. When these mutations are present, the tumor suppressors are spliced incorrectly and stop working, and the cell loses one of its defenses against cancer. The number of cryptic splice sites that the researchers found in this study accounts for about 5 percent of the driver mutations found in tumor suppressor genes.

Targeting these mutations could offer a new way to potentially treat those patients, the researchers say. One possible approach that is still in development uses short strands of RNA called antisense oligonucleotides (ASOs) to patch over a mutated piece of DNA with the correct sequence.

“If you could make the mutation disappear in a way, then you solve the problem. Those tumor suppressor genes could keep operating and perhaps combat the cancer,” Yaari says. “The ASO technology is actively being developed, and this could be a very good application for it.”

Another region where the researchers found a high concentration of noncoding driver mutations is in the untranslated regions of some tumor suppressor genes. The tumor suppressor gene TP53, which is defective in many types of cancer, was already known to accumulate many deletions in these sequences, known as 5′ untranslated regions. The MIT team found the same pattern in a tumor suppressor called ELF3.

The researchers also used their model to investigate whether common mutations that were already known might also be driving different types of cancers. As one example, the researchers found that BRAF, previously linked to melanoma, also contributes to cancer progression in smaller percentages of other types of cancers, including pancreatic, liver, and gastroesophageal.

“That says that there’s actually a lot of overlap between the landscape of common drivers and the landscape of rare drivers. That provides opportunity for therapeutic repurposing,” Sherman says. “These results could help guide the clinical trials that we should be setting up to expand these drugs from just being approved in one cancer, to being approved in many cancers and being able to help more patients.”

The research was funded, in part, by the National Institutes of Health and the National Cancer Institute.