A child who learns not to drop a glass on the floor, or a tennis player predicting the course of an incoming ball to hit it accurately are examples of how the brain incorporates the physical laws of gravity to optimally function on Earth. Astronauts who go to space reside in a weightless environment, where the brain’s rules about gravity are no longer applicable. A new study on brain function in cosmonauts has revealed how the brain’s organization is changed after a six-month mission to the International Space Station (ISS), demonstrating the adaptation that is required to live in weightlessness.

The University of Antwerp has been leading this BRAIN-DTI scientific project through the European Space Agency. Magnetic resonance imaging (MRI) data were taken from 14 astronaut brains before and several times after their mission to space. Using a special MRI technique, the researchers collected the astronauts’ brain data in a resting condition, hence without having them engage in a specific task. This resting-state functional MRI technique enabled the researchers to investigate the brain’s default state and to find out whether this changes or not after long-duration spaceflight.

Learning effect

In collaboration with the University of Liège, recent analyses of the brain’s activity at rest revealed how functional connectivity, a marker of how activity in some brain areas is correlated with the activity in others, changes in specific regions.

“We found that connectivity was altered after spaceflight in regions which support the integration of different types of information, rather than dealing with only one type each time, such as visual, auditory, or movement information’, say Steven Jillings and Floris Wuyts (University of Antwerp). “Moreover, we found that some of these altered communication patterns were retained throughout 8 months of being back on Earth. At the same time, some brain changes returned to the level of how the areas were functioning before the space mission.”

Both scenarios of changes are plausible: retained changes in brain communication may indicate a learning effect, while transient changes may indicate more acute adaptation to changed gravity levels.

“This dataset is so special as their participants themselves. Back in 2016, we were historically the first to show how spaceflight may affect brain function on a single cosmonaut. Some years later we are now in a unique position to investigate the brains of more astronauts, several times. Therefore, we are deciphering the potential of the human brain all the more in confidence,” says Dr. Athena Demertzi (GIGA Institute, University of Liège), co-supervisor of this this work.

New generation of astronauts

“Understanding physiological and behavioral changes triggered by weightlessness is key to plan human space exploration. Therefore, mapping changes of brain function using neuroimaging techniques as done in this work is an important step to prepare the new generation of astronauts for longer missions,” comments Raphaël Liégeois, Doctor of Engineering Science (ULiège) with a Thesis in the field of Neuroscience, future ESA Astronaut.

The researchers are excited with the results, though they know it is only the first step in pursuing our understanding of brain communication changes after space travel. For example, we still need to investigate what the exact behavioural consequence is for these brain communication changes, we need to understand whether longer time spent in outer space might influence these observations, and whether brain characteristics may be helpful in selecting future astronauts or monitoring them during and after space travel.

The observations were carried out in an international project led by Dr Nicola Schneider at the University of Cologne and Prof Alexander Tielens at the University of Maryland as part of the FEEDBACK programme on board the flying observatory SOFIA (Stratospheric Observatory for Infrared Astronomy). The new findings modify previous perceptions that this specific process of star formation is quasi-static and quite slow. The dynamic formation process now observed would also explain the formation of particularly massive stars.

By comparing the distribution of ionized carbon, molecular carbon monoxide and atomic hydrogen, the team found that the shells of interstellar gas clouds are made of hydrogen and collide with each other at speeds of up to twenty kilometres per second. “This high speed compresses the gas into denser molecular regions where new, mainly massive stars form. We needed the CII observations to detect this otherwise ‘dark’ gas,” said Dr Schneider. The observations show for the first time the faint CII radiation from the periphery of the clouds, which could not be observed before. Only SOFIA and its sensitive instruments were capable of detecting this radiation.

SOFIA was operated by NASA and the German Aerospace Center (DLR) until September 2022. The observatory consisted of a converted Boeing 747 with a built-in 2.7-metre telescope. It was coordinated by the German SOFIA Institute (DSI) and the Universities Space Research Association (USRA). SOFIA observed the sky from the stratosphere (above 13 kilometres) and covered the infrared region of the electromagnetic spectrum, just beyond what humans can see. The Boeing thus flew above most of the water vapour in the Earth’s atmosphere, which otherwise blocks out infrared light. This allowed the scientists to observe a wavelength range that is not accessible from Earth. For the current results, the team used the upGREAT receiver installed on SOFIA in 2015 by the Max Planck Institute for Radio Astronomy in Bonn and the University of Cologne.

Even though SOFIA is no longer in operation, the data collected so far are essential for basic astronomical research because there is no longer an instrument that extensively maps the sky in this wavelength range (typically 60 to 200 micrometres). The now active James Webb Space Telescope observes in the infrared at shorter wavelengths and focuses on spatially small areas. Therefore, the analysis of the data collected by SOFIA is ongoing and continues to provide important insights – also regarding other star-forming regions: “In the list of FEEDBACK sources, there are other gas clouds in different stages of evolution, where we are now looking for the weak CII radiation at the peripheries of the clouds to detect similar interactions as in the Cygnus X region,” Schneider concluded.

Elison Matioli of the Power and Wide-band-gap Electronics Research Lab (POWERlab) in EPFL’s School of Engineering explains that further miniaturization is therefore not a viable solution to better electronics performance. “New papers come out describing smaller and smaller devices, but in the case of materials made from gallium nitride, the best devices in terms of frequency were already published a few years back,” he says. “After that, there is really nothing better, because as device size is reduced, we face fundamental limitations. This is true regardless of the material used.”

In response to this challenge, Matioli and PhD student Mohammad Samizadeh Nikoo came up with a new approach to electronics that could overcome these limitations and enable a new class of terahertz devices. Instead of shrinking their device, they rearranged it, notably by etching patterned contacts called metastructures at sub-wavelength distances onto a semiconductor made of gallium nitride and indium gallium nitride. These metastructures allow the electrical fields inside the device to be controlled, yielding extraordinary properties that do not occur in nature.

Crucially, the device can operate at electromagnetic frequencies in the terahertz range (between 0.3-30 THz) — significantly faster than the gigahertz waves used in today’s electronics. They can therefore carry much greater quantities of information for a given signal or period, giving them great potential for applications in 6G communications and beyond.

“We found that manipulating radiofrequency fields at microscopic scales can significantly boost the performance of electronic devices, without relying on aggressive downscaling,” explains Samizadeh Nikoo, who is the first author of an article on the breakthrough recently published in the journal Nature.

Record high frequencies, record low resistance

Because terahertz frequencies are too fast for current electronics to manage, and too slow for optics applications, this range is often referred to as the ‘terahertz gap’. Using sub-wavelength metastructures to modulate terahertz waves is a technique that comes from the world of optics. But the POWERlab’s method allows for an unprecedented degree of electronic control, unlike the optics approach of shining an external beam of light onto an existing pattern.

“In our electronics-based approach, the ability to control induced radiofrequencies comes from the combination of the sub-wavelength patterned contacts, plus the control of the electronic channel with applied voltage. This means that we can change the collective effect inside the metadevice by inducing electrons (or not),” says Matioli.

While the most advanced devices on the market today can achieve frequencies of up to 2 THz, the POWERlab’s metadevices can reach 20 THz. Similarly, today’s devices operating near the terahertz range tend to break down at voltages below 2 volts, while the metadevices can support over 20 volts. This enables the transmission and modulation of terahertz signals with much greater power and frequency than is currently possible.

Integrated solutions

As Samizadeh Nikoo explains, modulating terahertz waves is crucial for the future of telecommunications, as the increasing data requirements of technologies like autonomous vehicles and 6G mobile communications are fast reaching the limits of today’s devices. The electronic metadevices developed in the POWERlab could form the basis for integrated terahertz electronics by producing compact, high-frequency chips that can already be used with smartphones, for example.

“This new technology could change the future of ultra-high-speed communications, as it is compatible with existing processes in semiconductor manufacturing. We have demonstrated data transmission of up to 100 gigabits per second at terahertz frequencies, which is already 10 times higher than what we have today with 5G,” Samizadeh Nikoo says.

To fully realize the potential of the approach, Matioli says the next step is to develop other electronics components ready for integration into terahertz circuits.

“Integrated terahertz electronics are the next frontier for a connected future. But our electronic metadevices are just one component. We need to develop other integrated terahertz components to fully realize the potential of this technology. That is our vision and goal.”

Graphene, an atom-thick sheet of carbon arranged in a honeycomb pattern, is an especially pure electrical conductor, making it an ideal material to study electron flow with very low resistance. Here, researchers intentionally added impurities at known distances and found that electron flow changes from gas-like to fluid-like as temperatures rise.

“All conductive materials contain impurities and imperfections that block electron flow, which causes resistance. Historically, people have taken a low-resolution approach to identifying where resistance comes from,” says Zach Krebs, a physics graduate student at UW-Madison and co-lead author of the study. “In this study, we image how charge flows around an impurity and actually see how that impurity blocks current and causes resistance, which is something that hasn’t been done before to distinguish gas-like and fluid-like electron flow.

The researchers intentionally introduced obstacles in the graphene, spaced at controlled distances and then applied a current across the sheet. Using a technique called scanning tunneling potentiomentry (STP), they measured the voltage with nanometer resolution at all points on the graphene, producing a 2D map of the electron flow pattern.

No matter the obstacle spacing, the drop in voltage through the channel was much lower at higher temp (77 kelvins) vs lower temp (4 K), indicating lower resistance with more electrons passing through.

At temperatures near absolute zero, electrons in graphene behave like a gas: they diffuse in all directions and are more likely to hit obstacles than they are to interact with each other. Resistance is higher, and electron flow is relatively inefficient. At higher temperatures — 77 K, or minus 196 C — the fluid-like behavior of electron flow means they are interacting with each other more than they are hitting obstacles, flowing like water between two rocks in the middle of a stream. It is as if the electrons are communicating information about the obstacle to each other and diverting around the rocks.

“We did a quantitative analysis [of the voltage map] and found that at the higher temperature, the resistance is much lower in the channel. The electrons were flowing more freely and fluid-like,” Krebs says. “Graphene is so clean that we’re forcing the electrons to interact with each other before they interact with anything else, and that is crucial in getting them to behave like a fluid.”

Former UW-Madison graduate student Wyatt Behn is a co-first author on this study conducted in physics professor Victor Brar’s group. Funding was provided by the U.S. Department of Energy (DE-SC00020313), the Office of Naval Research (N00014-20-1-2356) and the National Science Foundation (DMR-1653661).

A research team, led by Professor Hideshi Ihara from the Osaka Metropolitan University Graduate School of Science, was the first to discover 2-oxo-imidazole-containing dipeptides (2-oxo-IDPs) — which have one more oxygen atom than normal IDPs — and found that they are the most common variety of IDPs derivatives in the body. The researchers also found that they have remarkably high antioxidant activity.

In their study, the researchers established a method for selective and highly sensitive detection of five types of 2-oxo-IDPs using mass spectrometry, which enables quantitative detection of trace 2-oxo-IDPs in living organisms. Using this method, they revealed for the first time that beef, pork, chicken, and other meats contain antioxidants, not only IDPs but a variety of different 2-oxo-IDPs. Their findings were published in Antioxidants.

“We hope that this research method, which enables advanced analysis of 2-oxo-IDPs, will be applied not only to basic biology but also to medicine, agriculture, and pharmacy, where it will help improve peoples’ health and prevent diseases,” concluded Professor Ihara.

Say you notice a sudden drop in temperature on both your patio and kitchen thermometers. At first, you think it’s because of a cold snap, so you crank up the heat in your home. Then you realize that while the outside has indeed become colder, inside, someone left the refrigerator door open.

Initially, you thought the temperature drops were correlated. Later, you saw that they weren’t.

Recognizing when readings are correlated is important not only for your home heating bill but for all of science. It’s especially challenging when measuring properties of atoms.

Now scientists have developed a method, reported in Science, that enables them to see whether magnetic fields detected by a pair of atom-scale quantum sensors are correlated or not.

“As far as I know, this is something people hadn’t tried to do, and that’s why we see these correlations where nobody else was able to. You really win from that.” — Shimon Kolkowitz, University of Wisconsin-Madison

The research was supported in part by Q-NEXT, a U.S. Department of Energy (DOE) National Quantum Information Science Research Center led by DOE’s Argonne National Laboratory.

The ability to distinguish between standalone and correlated environments at the atomic scale could have enormous impacts in medicine, navigation and discovery science.

What happened

A team of scientists at Princeton University and the University of Wisconsin-Madison developed and demonstrated a new technique for teasing out whether magnetic fields picked up by multiple quantum sensors are correlated with each other or independent.

The team focused on a type of diamond-based sensor called a nitrogen-vacancy center, or NV center, which consists of a nitrogen atom next to an atom-sized hole in the crystal of carbon atoms that make up diamond.

Typically, scientists measure the magnetic field strength at a single NV center by averaging multiple readings. Or they might take an average reading of many NV centers at once.

While helpful, average values provide only so much information. Knowing that the average temperature in Wisconsin will be 42 degrees Fahrenheit tomorrow tells you little about how much colder it will be at night or in the northern part of the state.

“If you want to learn not just the value of the magnetic field at one location or at one point in time, but whether there’s a relationship between the magnetic field at one location and the magnetic field at another nearby — there wasn’t really a good way to do that with these NV centers,” said paper co-author Shimon Kolkowitz, associate professor at the University of Wisconsin-Madison and Q-NEXT collaborator.

The team’s new method uses multiple simultaneous readings of two NV centers. Using sophisticated computation and signal-processing techniques, they obtained information about the relationship between the magnetic fields at both points and could say whether the two readings resulted from the same source.

“Were they seeing the same magnetic field? Were they seeing a different magnetic field? That’s what we can get from these measurements,” Kolkowitz said. ?”It’s useful information that no one had access to before. We can tell the difference between the global field that both sensors were seeing and those that were local.”

Why it matters

Quantum sensors harness the quantum properties of atoms or atom-like systems to pick up tiny signals — such as the magnetic fields arising from the motion of single electrons. These fields are puny: 100,000 times weaker than that of a fridge magnet. Only ultrasensitive tools such as quantum sensors can make measurements at nature’s smallest scales.

Quantum sensors are expected to be powerful. NV centers, for example, can distinguish features separated by a mere one ten thousandth of the width of a human hair. With that kind of hyperzoom capability, NV centers could be placed in living cells for an inside, up-close look at how they function. Scientists could even use them to pinpoint the causes of disease.

“What make NVs special is their spatial resolution,” Kolkowitz said. ?”That’s useful for imaging the magnetic fields from an exotic material or seeing the structure of individual proteins.”

With the Kolkowitz team’s new method for sensing magnetic field strengths at multiple points simultaneously, scientists could one day be able to map atom-level changes in magnetism through time and space.

How it works

How did the team make these informative measurements? They got granular.

Rather than average over many raw values to arrive at the overall magnetic field strength, the researchers kept track of individual readings at each NV center, and then applied a mathematical maneuver called ?”covariance” to the two lists.

Comparing the covariance-calculated figures — which capture more detail than a couple of raw averages — let them see whether the fields were correlated.

“We’re doing that averaging differently than what’s been done in the past, so we don’t lose this information in the process of averaging,” Kolkowitz said ?”That’s part of what’s special here.”

So why hasn’t covariance magnetometry, as the method is called, been tested before now?

For one, the team had to build an experimental setup for taking simultaneous measurements at multiple NV centers. This microscope was built by the team at Princeton, led by Professor Nathalie de Leon, a member of the Co-Design Center for Quantum Advantage, another DOE National Quantum Information Science Research Center, led by Brookhaven National Laboratory.

For another, covariance magnetometry works only when the individual measurements of these tiny magnetic fields are highly reliable. (A readout is only as good as its contributing measurements.) That’s why the researchers used a special technique called spin-to-charge conversion, which produces a raw reading with more information about the magnetic field for each measurement than other commonly used tools.

With spin-to-charge conversion, individual measurements take longer. That’s the price scientists pay for higher reliability.

However, when combined with covariance to measure minuscule, correlated magnetic fields, it saves buckets of time.

“Using the conventional method, you’d have to average for 10 full days continuously to get one piece of data to say that you saw this correlated nanotesla signal,” Kolkowitz said. ?”Whereas with this new method, it’s an hour or two.”

By integrating covariance information with spin-to-charge conversion, researchers can gain access to atomic and subatomic details they didn’t have before, supercharging the already powerful capabilities of quantum sensing.

“As far as I know, this is something people hadn’t tried to do, and that’s why we see these correlations where nobody else was able to,” Kolkowitz said. ?”You really win from that.”

This work was supported by the DOE Office of Science National Quantum Information Science Research Centers as part of the Q-NEXT center, the National Science Foundation, the Princeton Catalysis Initiative, the DOE, Office of Science, Office of Basic Energy Sciences, a Princeton Quantum Initiative Postdoctoral Fellowship, and the Intelligence Community Postdoctoral Research Fellowship Program by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and the Office of the Director of National Intelligence.

“Plagiarism comes in different flavors,” said Dongwon Lee, professor of information sciences and technology at Penn State. “We wanted to see if language models not only copy and paste but resort to more sophisticated forms of plagiarism without realizing it.”

The researchers focused on identifying three forms of plagiarism: verbatim, or directly copying and pasting content; paraphrase, or rewording and restructuring content without citing the original source; and idea, or using the main idea from a text without proper attribution. They constructed a pipeline for automated plagiarism detection and tested it against OpenAI’s GPT-2 because the language model’s training data is available online, allowing the researchers to compare generated texts to the 8 million documents used to pre-train GPT-2.

The scientists used 210,000 generated texts to test for plagiarism in pre-trained language models and fine-tuned language models, or models trained further to focus on specific topic areas. In this case, the team fine-tuned three language models to focus on scientific documents, scholarly articles related to COVID-19, and patent claims. They used an open-source search engine to retrieve the top 10 training documents most similar to each generated text and modified an existing text alignment algorithm to better detect instances of verbatim, paraphrase and idea plagiarism.

The team found that the language models committed all three types of plagiarism, and that the larger the dataset and parameters used to train the model, the more often plagiarism occurred. They also noted that fine-tuned language models reduced verbatim plagiarism but increased instances of paraphrase and idea plagiarism. In addition, they identified instances of the language model exposing individuals’ private information through all three forms of plagiarism. The researchers will present their findings at the 2023 ACM Web Conference, which takes place April 30-May 4 in Austin, Texas.

“People pursue large language models because the larger the model gets, generation abilities increase,” said lead author Jooyoung Lee, doctoral student in the College of Information Sciences and Technology at Penn State. “At the same time, they are jeopardizing the originality and creativity of the content within the training corpus. This is an important finding.”

The study highlights the need for more research into text generators and the ethical and philosophical questions that they pose, according to the researchers.

“Even though the output may be appealing, and language models may be fun to use and seem productive for certain tasks, it doesn’t mean they are practical,” said Thai Le, assistant professor of computer and information science at the University of Mississippi who began working on the project as a doctoral candidate at Penn State. “In practice, we need to take care of the ethical and copyright issues that text generators pose.”

Though the results of the study only apply to GPT-2, the automatic plagiarism detection process that the researchers established can be applied to newer language models like ChatGPT to determine if and how often these models plagiarize training content. Testing for plagiarism, however, depends on the developers making the training data publicly accessible, said the researchers.

The current study can help AI researchers build more robust, reliable and responsible language models in future, according to the scientists. For now, they urge individuals to exercise caution when using text generators.

“AI researchers and scientists are studying how to make language models better and more robust, meanwhile, many individuals are using language models in their daily lives for various productivity tasks,” said Jinghui Chen, assistant professor of information sciences and technology at Penn State. “While leveraging language models as a search engine or a stack overflow to debug code is probably fine, for other purposes, since the language model may produce plagiarized content, it may result in negative consequences for the user.”

The plagiarism outcome is not something unexpected, added Dongwon Lee.

“As a stochastic parrot, we taught language models to mimic human writings without teaching them how not to plagiarize properly,” he said. “Now, it’s time to teach them to write more properly, and we have a long way to go.”

The National Science Foundation supported this work.