University of Exeter scientists showed food pictures to more than 700 people, including omnivores (who eat meat and other foods), flexitarians (who try to eat less meat) and vegetarians.

About 7% of meat eaters (15% of flexitarians and 3% of omnivores) had a “fairly strong disgust response” to images of meat dishes commonly eaten in the UK, like roast chicken or bacon.

As a group, omnivores rated meat images about twice as disgusting on average as pictures of carbohydrate-rich foods like bread, chips and rice.

Based on the findings, the researchers say harnessing the “yuk factor” may be more effective than relying on willpower for anyone who wants to eat less meat.

“We were surprised to find that so many people are grossed out by meat — even people who eat meat all the time,” said Elisa Becker, of the University of Exeter.

“Our results don’t explain why these people eat meat, but it’s possible that habits, family and cultural traditions all play a part.

“Meat consumption is increasingly seen as unsustainable, unhealthy and unethical, and many people want to eat less meat.

“If you’re trying to cut down your meat intake, sheer willpower may not be enough — but harnessing the ‘yuk factor’ could be the way to go.”

The study’s 711 participants — 402 omnivores, 203 flexitarians and 106 vegetarians — each completed a survey and took a rapid-response task (measuring instinctive reactions) to test their levels of “meat disgust.”

“Meat liking” was also measured. About 75% of omnivores — and more than 20% of vegetarians — showed a fairly strong liking for meat.

To be classified as having “fairly strong” meat disgust, participants had to rate six meat images closer to “very much” than “not at all” on a sliding scale of disgust, and also had to show evidence of meat disgust on the rapid-response task.

Among flexitarians — the only group attempting to reduce their meat intake — meat disgust was a better predictor than self-control (measured in a separate questionnaire) of reduced meat-eating.

Meat disgust was also associated with reduced intake over the following six months.

“We hope that this information can help us develop new interventions to help people reduce their meat intake,” said Professor Natalia Lawrence, of the University of Exeter.

“Not everyone wants to reduce their meat consumption — but for those who do, we are working on computer tasks that might help them harness the power of disgust in a fun way.

“It’s important to note that our study does not establish causation — so further research is needed to find out whether meat disgust causes people to eat less meat, or whether avoiding meat allows these negative emotional responses to develop or be expressed.”

Becker added: “It’s interesting to note that almost all of us experience meat disgust from time to time — for example when we see unfamiliar meats or dishes made from parts of animals we don’t usually eat, like squirrel meat or beef heart.

“Humans may have evolved a degree of meat disgust because eating spoilt meat can be much more dangerous than eating a carrot that’s a bit off.”

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Most of the many millions of songbirds that migrate every year between Europe and Africa fly by night and spend the daytime hours resting and eating. Some species, which normally only fly by night, occasionally fly for over 24 consecutive hours to avoid having to stop in inhospitable locations with insufficient access to food, such as deserts and seas. The great reed warbler, Acrocephalus arundinaceus, is such a species. During its month-long migration, it can fly for up to 34 consecutive hours without landing.

A research team from Lund University in Sweden, University of Copenhagen and the Nature Research Centre in Vilnius has used unique data loggers developed at Lund University to study these long migratory flights almost minute by minute. The researchers used the data loggers, weighing no more than 1.2 grams, to track 14 great reed warblers which were captured during the summer at Lake Kvismaren, close to Örebro in Central Sweden.

The data loggers collected data over a full year, thereby providing the researchers with valuable information. As the great reed warblers migrated to and from Africa, the data loggers continuously stored information about altitude, activity and the sun’s ascend and descend. The altitude measurements were recorded once per hour and every five minutes the data loggers saved information about whether the birds were flying, looking for food on the ground or resting.

Based on this information, the researchers calculated that the great reed warblers flew at an average altitude of 2400 metres at night. On the few occasions on which the birds flew for more than one consecutive night, they climbed 3000 metres at dawn to fly at an average altitude of 5400 metres during the day. They remained cruising at that altitude until dusk, when they dived 3-4000 metres and continued their flight at an altitude of around 2000 metres the following night.

Sissel Sjöberg and Dennis Hasselquist at Lund University led the study, and the results came as a surprise to them and their colleagues. They describe the great reed warblers’ behaviour as very consistent, indicating that the birds may have to fly at more than twice their night-time altitude to manage migrating during daylight hours at all.

There is no definitive explanation as yet. However, the research team rules out the previously predominant explanations for choice of altitudes in migratory birds: winds and air temperature.

“At altitudes over 2000 metres, winds and air temperatures do not change merely going from day to night or vice versa,” says Sissel Sjöberg.

Nor are there any high mountains along the migration route that could push the birds up to extreme altitudes. Two possible explanations could be that during daytime, the great reed warblers see further from a great height and that they can avoid birds of prey. But the researchers also suggest another explanation: the birds climb to more than double the altitude during the day to avoid becoming overheated when they are exposed to the additional effect of the sun’s heat radiation.

“It is colder higher up in the air and when the birds rise to 5400 metres, they reach a layer of air with a temperature of around minus 9 degrees centigrade. That is 22 degrees colder than the altitude at which the birds fly during the night,” says Sissel Sjöberg.

“The wings of migratory songbirds beat several times per second, so they are working extremely hard, which makes their bodies very warm regardless of whether they are flying by day or by night. But if they fly by day, they are also exposed to the heat from the sun’s radiation; that is what we suggest they are compensating for when they climb to a much colder layer of air at daytime. It is likely that they would not manage to fly by day without becoming overheated if they did not climb to these extreme altitudes,” says Dennis Hasselquist.

The researchers are continuing to study the great reed warblers. In time, they hope to find answers to the question of why the birds behave in this way, which might also help to explain why most birds that migrate to the tropics during the winter fly almost entirely by night, whereas they are active during the day and sleep at night for the rest of the year.

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Two papers published in The Astrophysical Journal report these findings, with Kyle Boone as lead author. Currently a postdoctoral fellow at the University of Washington, Boone is a former graduate student of Nobel Laureate Saul Perlmutter, the Berkeley Lab senior scientist and UC Berkeley professor who led one of the teams that originally discovered dark energy. Perlmutter was also a co-author on both studies.

Supernovae were used in 1998 to make the startling discovery that the expansion of the universe is speeding up, rather than slowing down as had been expected. This acceleration — attributed to the dark energy that makes up two-thirds of all the energy in the universe — has since been confirmed by a variety of independent techniques as well as with more detailed studies of supernovae.

The discovery of dark energy relied on using a particular class of supernovae, Type Ia. These supernovae always explode with nearly the same intrinsic maximum brightness. Because the observed maximum brightness of the supernova is used to infer its distance, the small remaining variations in the intrinsic maximum brightness limited the precision with which dark energy could be tested. Despite 20 years of improvements by many groups, supernovae studies of dark energy have until now remained limited by these variations.

Quadrupling the number of supernovae

The new results announced by the SNfactory come from a multi-year study devoted entirely to increasing the precision of cosmological measurements made with supernovae. Measurement of dark energy requires comparisons of the maximum brightnesses of distant supernovae billions of light-years away with those of nearby supernovae “only” 300 million light-years away. The team studied hundreds of such nearby supernovae in exquisite detail. Each supernova was measured a number of times, at intervals of a few days. Each measurement examined the spectrum of the supernova, recording its intensity across the wavelength range of visible light. An instrument custom-made for this investigation, the SuperNova Integral Field Spectrometer, installed at the University of Hawaii 2.2-meter telescope at Maunakea, was used to measure the spectra.

“We’ve long had this idea that if the physics of the explosion of two supernovae were the same, their maximum brightnesses would be the same. Using the Nearby Supernova Factory spectra as a kind of CAT scan through the supernova explosion, we could test this idea,” said Perlmutter.

Indeed, several years ago, physicist Hannah Fakhouri, then a graduate student working with Perlmutter, made a discovery key to today’s results. Looking at a multitude of spectra taken by the SNfactory, she found that in quite a number of instances, the spectra from two different supernovae looked very nearly identical. Among the 50 or so supernovae, some were virtually identical twins. When the wiggly spectra of a pair of twins were superimposed, to the eye there was just a single track. The current analysis builds on this observation to model the behavior of supernovae in the period near the time of their maximum brightness.

The new work nearly quadruples the number of supernovae used in the analysis. This made the sample large enough to apply machine-learning techniques to identify these twins, leading to the discovery that Type Ia supernova spectra vary in only three ways. The intrinsic brightnesses of the supernovae also depend primarily on these three observed differences, making it possible to measure supernova distances to the remarkable accuracy of about 3%.

Just as important, this new method does not suffer from the biases that have beset previous methods, seen when comparing supernovae found in different types of galaxies. Since nearby galaxies are somewhat different than distant ones, there was a serious concern that such dependence would produce false readings in the dark energy measurement. Now this concern can be greatly reduced by measuring distant supernovae with this new technique.

In describing this work, Boone noted, “Conventional measurement of supernova distances uses light curves — images taken in several colors as a supernova brightens and fades. Instead, we used a spectrum of each supernova. These are so much more detailed, and with machine-learning techniques it then became possible to discern the complex behavior that was key to measuring more accurate distances.”

The results from Boone’s papers will benefit two upcoming major experiments. The first experiment will be at the 8.4-meter Rubin Observatory, under construction in Chile, with its Legacy Survey of Space and Time, a joint project of the Department of Energy and the National Science Foundation. The second is NASA’s forthcoming Nancy Grace Roman Space Telescope. These telescopes will measure thousands of supernovae to further improve the measurement of dark energy. They will be able to compare their results with measurements made using complementary techniques.

Aldering, also a co-author on the papers, observed that “not only is this distance measurement technique more accurate, it only requires a single spectrum, taken when a supernova is brightest and thus easiest to observe — a game changer!” Having a variety of techniques is particularly valuable in this field where preconceptions have turned out to be wrong and the need for independent verification is high.

The SNfactory collaboration includes Berkeley Lab, the Laboratory for Nuclear Physics and High Energy at Sorbonne University, the Center for Astronomical Research of Lyon, the Institute of Physics of the 2 Infinities at the University Claude Bernard, Yale University, Germany’s Humboldt University, the Max Planck Institute for Astrophysics, China’s Tsinghua University, the Center for Particle Physics of Marseille, and Clermont Auvergne University.

This work was supported by the Department of Energy’s Office of Science, NASA’s Astrophysics Division, the Gordon and Betty Moore Foundation, the French National Institute of Nuclear and Particle Physics and the National Institute for Earth Sciences and Astronomy of the French National Centre for Scientific Research, the German Research Foundation and German Aerospace Center, the European Research Council, Tsinghua University, and the National Natural Science Foundation of China.

Additional background

In 1998, two competing groups studying supernovae, the Supernova Cosmology Project and the High-z Supernova Search team, both announced they had found evidence that, contrary to expectations, the expansion of the universe was not slowing but becoming faster and faster. Dark energy is the term used to describe the cause of the acceleration. The 2011 Nobel Prize was awarded to leaders of the two teams: Saul Perlmutter of Berkeley Lab and UC Berkeley, leader of the Supernova Cosmology Project, and to Brian Schmidt of the Australian National University and Adam Riess of Johns Hopkins University, from the High-z team.

Additional techniques for measuring dark energy include the DOE-supported Dark Energy Spectroscopic Instrument, led by Berkeley Lab, which will use spectroscopy on 30 million galaxies in a technique called baryon acoustic oscillation. The Rubin Observatory will also use another called weak gravitational lensing.

Informatics experts at the University of Sussex have developed a new computational model that demonstrates a long sought after link between insect and mammalian learning, as detailed in a new paper published today in Nature Communications.

Incorporating anatomical and functional data from recent experiments, Dr James Bennett and colleagues modelled how the anatomy and physiology of the fruit fly’s brain can support learning according to the reward prediction error (RPE) hypothesis.

The computational model indicates how dopamine neurons in an area of a fruit fly’s brain, known as the mushroom body, can produce similar signals to dopamine neurons in mammals, and how these dopamine signals can reliably instruct learning.

The academics believe that establishing whether flies also use prediction errors to learn could lead to more humane animal research allowing researchers to replace animals with more simple insect species for future studies into the mechanisms of learning.

By opening up new opportunities to study neural mechanisms of learning, the researchers hope the model could also be helpful in illuminating greater understanding of mental health issues such as depression or addiction which are underpinned by the RPE hypothesis.

Dr Bennett, research fellow in the University of Sussex’s School of Engineering and Informatics, said: “Using our computational model, we were able to show that data from insect experiments did not necessarily conflict with predictions from the RPE hypothesis, as had been thought previously.

“Establishing a bridge between insect and mammal studies on learning may open up the possibility to exploit the powerful genetic tools available for performing experiments in insects, and the smaller scale of their brains, to make sense of brain function and disease in mammals, including humans.”

Understanding of how mammals learn has come a long way thanks to the RPE hypothesis, which suggests that associative memories are learned in proportion to how inaccurate they are.

The hypothesis has had considerable success explaining experimental data about learning in mammals, and has been extensively applied to decision-making and mental health illnesses such as addiction and depression. But scientists have encountered difficulties when applying the hypothesis to learning in insects due to conflicting results from different experiments.

The University of Sussex research team created a computational model to show how the major features of mushroom body anatomy and physiology can implement learning according to the RPE hypothesis.

The model simulates a simplification of the mushroom body, including different neuron types and the connections between them, and how the activity of those neurons promote learning and influence the decisions a fly makes when certain choices are rewarded.

To further understanding of learning in fly brains, the research team used their model to make five novel predictions about the influence different neurons in the mushroom body have on learning and decision-making, in the hope that they promote future experimental work.

Dr Bennett said: “While other models of the mushroom body have been created, to the best of our knowledge no other model until now has included connections between dopamine neurons and another set of neurons that predict and drive behaviour towards rewards. For example, when the reward is the sugar content of food, these connections would allow the predicted sugar availability to be compared with the actual sugar ingested, allowing more accurate predictions and appropriate sugar-seeking behaviours to be learned.

“The model can explain a large array of behaviours exhibited by fruit flies when the activity of particular neurons in their brains are either silenced or activated artificially in experiments. We also propose connections between dopamine neurons and other neurons in the mushroom body, which have not yet been reported in experiments, but would help to explain even more experimental data.”

Thomas Nowotny, Professor of Informatics at the University of Sussex, said: “The model brings together learning theory and experimental knowledge in a way that allows us to think systematically how fly brains actually work. The results show how learning in simple flies might be more similar to how we learn than previously thought.”

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Materials provided by University of Sussex. Original written by Neil Vowles. Note: Content may be edited for style and length.

“One of the things that is foremost in the minds of anesthesiologists is ‘Do I have somebody who is lying in front of me who may be conscious and I don’t realize it?’ Being able to reliably maintain unconsciousness in a patient during surgery is fundamental to what we do,” said senior author Emery N. Brown, Edward Hood Taplin Professor in The Picower Institute for Learning and Memory and the Institute for Medical Engineering and Science at MIT, and an anesthesiologist at MGH. “This is an important step forward.”

More than providing a good readout of unconsciousness, Brown added, the new algorithms offer the potential to allow anesthesiologists to maintain it at the desired level while using less drug than they might administer when depending on less direct, accurate and reliable indicators. That can improve patient’s post-operative outcomes, such as delirium.

“We may always have to be a little bit ‘overboard’,” said Brown, who is also a professor at Harvard Medical School. “But can we do it with sufficient accuracy so that we are not dosing people more than is needed?”

Used to drive an infusion pump, for instance, algorithms could help anesthesiologists precisely throttle drug delivery to optimize a patient’s state and the doses they are receiving.

Artificial intelligence, real-world testing

To develop the technology to do so, postdocs John Abel and Marcus Badgeley led the study, published in PLOS ONE [LINK TBD], in which they trained machine learning algorithms on a remarkable data set the lab gathered back in 2013. In that study, 10 healthy volunteers in their 20s underwent anesthesia with the commonly used drug propofol. As the dose was methodically raised using computer controlled delivery, the volunteers were asked to respond to a simple request until they couldn’t anymore. Then when they were brought back to consciousness as the dose was later lessened, they became able to respond again. All the while, neural rhythms reflecting their brain activity were recorded with electroencephalogram (EEG) electrodes, providing a direct, real-time link between measured brain activity and exhibited unconsciousness.

In the new work, Abel, Badgeley and the team trained versions of their AI algorithms, based on different underlying statistical methods, on more than 33,000 two-second-long snippets of EEG recordings from seven of the volunteers. This way the algorithms could “learn” the difference between EEG readings predictive of consciousness and unconsciousness under propofol. Then the researchers tested the algorithms in three ways.

First, they checked whether their three most promising algorithms accurately predicted unconsciousness when applied to EEG activity recorded from the other three volunteers of the 2013 study. They did.

Then they used the algorithms to analyze EEG recorded from 27 real surgery patients who received propofol for general anesthesia. Even though the algorithms were now being applied to data gathered from a “noisier” real-world surgical setting where the rhythms were also being measured with different equipment, the algorithms still distinguished unconsciousness with higher accuracy than other studies have shown. The authors even highlight one case in which the algorithms were able to detect a patient’s decreasing level of unconsciousness several minutes before the actual attending anesthesiologist did, meaning that if it had been in use during the surgery itself, it could have provided an accurate and helpful early warning.

As a third test, the team applied the algorithms to EEG recordings from 17 surgery patients who were anesthetized with sevoflurane. Though sevoflurane is different from propofol and is inhaled rather than infused, it works in a similar manner, by binding to the same GABA-A receptors on the same key types of brain cells. The team’s algorithms again performed with high, though somewhat reduced accuracy, suggesting that their ability to classify unconsciousness carried over reliably to another anesthetic drug that works in a similar way.

The ability to predict unconsciousness across different drugs with the same mechanism of action is key, the authors said. One of the main flaws with current EEG-based systems for monitoring consciousness, they said, is that they don’t distinguish among drug classes, even though different categories of anesthesia drugs work in very different ways, producing distinct EEG patterns. They also don’t adequately account for known age differences in brain response to anesthesia. These limitations on their accuracy have also limited their clinical use.

In the new study, while the algorithms trained on 20-somethings applied well to cohorts of surgery patients whose average age skewed significantly older and varied more widely, the authors acknowledge that they want to train algorithms distinctly for use with children or seniors. They can also train new algorithms to apply specifically for other kinds of drugs with different mechanisms of action. All together a suite of well trained and attuned algorithms could provide high accuracy that accounts for patient age and the drug in use.

Abel said the team’s approach of framing the problem as a matter of predicting consciousness via EEG for a specific class of drugs made the machine learning approach very simple to implement and extend.

“This is a proof of concept showing that now we can go and say let’s look at an older population or let’s look at a different kind of drug,” he said. “Doing this is simple if you set it up the right way.”

The resulting algorithms aren’t even computationally demanding. The authors noted that for a given 2 seconds of EEG data, the algorithms could make an accurate prediction of consciousness in less than a tenth of a second running on just a standard MacBook Pro computer.

The lab is already building on the findings to refine the algorithms further, Brown said. He said he also wants to expand testing to hundreds more cases to further confirm their performance, and also to determine whether wider distinctions may begin to emerge among the different underlying statistical models the team employed.

In a new study, physicists led by the University of Iowa report a new feature to Earth’s atmospheric light show. Examining video taken nearly two decades ago, the researchers describe multiple instances where a section of the diffuse aurora — the faint, background-like glow accompanying the more vivid light commonly associated with auroras — goes dark, as if scrubbed by a giant blotter. Then, after a short period of time, the blacked-out section suddenly reappears.

The researchers say the behavior, which they call “diffuse auroral erasers,” has never been mentioned in the scientific literature. The findings appear in the Journal of Geophysical Research Space Physics.

Auroras occur when charged particles flowing from the sun — called the solar wind — interact with Earth’s protective magnetic bubble. Some of those particles escape and fall toward our planet, and the energy released during their collisions with gases in Earth’s atmosphere generate the light associated with auroras.

“The biggest thing about these erasers that we didn’t know before but know now is that they exist,” says Allison Jaynes, assistant professor in the Department of Physics and Astronomy at Iowa and study co-author. “It raises the question: Are these a common phenomenon that has been overlooked, or are they rare?

“Knowing they exist means there is a process that is creating them,” Jaynes continues, “and it may be a process that we haven’t started to look at yet because we never knew they were happening until now.”

It was on March 15, 2002, that David Knudsen, a physicist at the University of Calgary, set up a video camera in Churchill, a town along Hudson Bay in Canada, to film auroras. Knudsen’s group was a little disheartened; the forecast called for clear, dark skies — normally perfect conditions for viewing auroras — but no dazzling illumination was happening. Still, the team was using a camera specially designed to capture low-level light, much like night-vision goggles.

Though the scientists saw only mostly darkness as they gazed upward with their own eyes, the camera was picking up all sorts of auroral activity, including an unusual sequence where areas of the diffuse aurora disappeared, then came back.

Knudsen, looking at the video as it was being recorded, scribbled in his notebook, “pulsating ‘black out’ diffuse glow, which then fills in over several seconds.”

“What surprised me, and what made me write it in the notebook, is when a patch brightened and turned off, the background diffuse aurora was erased. It went away,” says Knudsen, a Fort Dodge, Iowa, native who has studied aurora for more than 35 years and is a co-author on the study. “There was a hole in the diffuse aurora. And then that hole would fill back in after a half-minute or so. I had never seen something like that before.”

The note lay dormant, and the video unstudied, until Iowa’s Jaynes handed it to graduate student Riley Troyer to investigate. Jaynes learned about Knudsen’s recording at a scientific meeting in 2010 and referenced the eraser note in her doctoral thesis on diffuse aurora a few years later. Now on the faculty at Iowa, she wanted to learn more about the phenomenon.

“I knew there was something there. I knew it was different and unique,” says Jaynes, assistant professor in the Department of Physics and Astronomy. “l had some ideas how it could be analyzed, but I hadn’t done that yet. I handed it to Riley, and he went much further with it by figuring out his own way to analyze the data and produce some significant conclusions.”

Troyer, from Fairbanks, Alaska, took up the assignment with gusto.

“I’ve seen hundreds of auroras growing up,” says Troyer, who is in his third year of doctoral studies at Iowa. “They’re part of my heritage, something I can study while keeping ties to where I’m from.”

Troyer created a software program to key in on frames in the video when the faint erasers were visible. In all, he cataloged 22 eraser events in the two-hour recording.

“The most valuable thing we found is showing the time that it takes for the aurora to go from an eraser event (when the diffuse aurora is blotted out) to be filled or colored again,” says Troyer, who is the paper’s corresponding author, “and how long it takes to go from that erased state back to being diffuse aurora. Having a value on that will help with future modeling of magnetic fields.”

Jaynes says learning about diffuse auroral erasers is akin to studying DNA to understand the entire human body.

“Particles that fall into our atmosphere from space can affect our atmospheric layers and our climate,” Jaynes says. “While particles with diffuse aurora may not be the main cause, they are smaller building blocks that can help us understand the aurora system as a whole, and may broaden our understanding how auroras happen on other planets in our solar system.”

Video: https://www.youtube.com/watch?v=7furDMNIil0&t=1s

But growing demand for data, and the means to store them, is driving scientists to search beyond silicon for materials that can push memory devices to higher densities, speeds, and security.

Now MIT physicists have shown preliminary evidence that data might be stored as faster, denser, and more secure bits made from antiferromagnets.

Antiferromagnetic, or AFM materials are the lesser-known cousins to ferromagnets, or conventional magnetic materials. Where the electrons in ferromagnets spin in synchrony — a property that allows a compass needle to point north, collectively following the Earth’s magnetic field — electrons in an antiferromagnet prefer the opposite spin to their neighbor, in an “antialignment” that effectively quenches magnetization even at the smallest scales.

The absence of net magnetization in an antiferromagnet makes it impervious to any external magnetic field. If they were made into memory devices, antiferromagnetic bits could protect any encoded data from being magnetically erased. They could also be made into smaller transistors and packed in greater numbers per chip than traditional silicon.

Now the MIT team has found that by doping extra electrons into an antiferromagnetic material, they can turn its collective antialigned arrangement on and off, in a controllable way. They found this magnetic transition is reversible, and sufficiently sharp, similar to switching a transistor’s state from 0 to 1. The results, published today in Physical Review Letters, demonstrate a potential new pathway to use antiferromagnets as a digital switch.

“An AFM memory could enable scaling up the data storage capacity of current devices — same volume, but more data,” says the study’s lead author Riccardo Comin, assistant professor of physics at MIT.

Comin’s MIT co-authors include lead author and graduate student Jiarui Li, along with Zhihai Zhu, Grace Zhang, and Da Zhou; as well as Roberg Green of the University of Saskatchewan; Zhen Zhang, Yifei Sun, and Shriram Ramanathan of Purdue University; Ronny Sutarto and Feizhou He of Canadian Light Source; and Jerzy Sadowski at Brookhaven National Laboratory.

Magnetic memory

To improve data storage, some researchers are looking to MRAM, or magnetoresistive RAM, a type of memory system that stores data as bits made from conventional magnetic materials. In principle, an MRAM device would be patterned with billions of magnetic bits. To encode data, the direction of a local magnetic domain within the device is flipped, similar to switching a transistor from 0 to 1.

MRAM systems could potentially read and write data faster than silicon-based devices and could run with less power. But they could also be vulnerable to external magnetic fields.

“The system as a whole follows a magnetic field like a sunflower follows the sun, which is why, if you take a magnetic data storage device and put it in a moderate magnetic field, information is completely erased,” Comin says.

Antiferromagnets, in contrast, are unaffected by external fields and could therefore be a more secure alternative to MRAM designs. An essential step toward encodable AFM bits is the ability to switch antiferromagnetism on and off. Researchers have found various ways to accomplish this, mostly by using electric current to switch a material from its orderly antialignment, to a random disorder of spins.

“With these approaches, switching is very fast,” says Li. “But the downside is, everytime you need a current to read or write, that requires a lot of energy per operation. When things get very small, the energy and heat generated by running currents are significant.”

Doped disorder

Comin and his colleagues wondered whether they could achieve antiferromagnetic switching in a more efficient manner. In their new study, they work with neodymium nickelate, an antiferromagnetic oxide grown in the Ramanathan lab. This material exhibits nanodomains that consist of nickel atoms with an opposite spin to that of its neighbor, and held together by oxygen and neodymium atoms. The researchers had previously mapped the material’s fractal properties.

Since then, the researchers have looked to see if they could manipulate the material’s antiferromagnetism via doping — a process that intentionally introduces impurities in a material to alter its electronic properties. In their case, the researchers doped neodymium nickel oxide by stripping the material of its oxygen atoms.

When an oxygen atom is removed, it leaves behind two electrons, which are redistributed among the other nickel and oxygen atoms. The researchers wondered whether stripping away many oxygen atoms would result in a domino effect of disorder that would switch off the material’s orderly antialignment.

To test their theory, they grew 100-nanometer-thin films of neodymium nickel oxide and placed them in an oxygen-starved chamber, then heated the samples to temperatures of 400 degrees Celsius to encourage oxygen to escape from the films and into the chamber’s atmosphere.

As they removed progressively more oxygen, they studied the films using advanced magnetic X-ray crystallography techniques to determine whether the material’s magnetic structure was intact, implying that its atomic spins remained in their orderly antialignment, and therefore retained antiferomagnetism. If their data showed a lack of an ordered magnetic structure, it would be evidence that the material’s antiferromagnetism had switched off, due to sufficient doping.

Through their experiments, the researchers were able to switch off the material’s antiferromagnetism at a certain critical doping threshold. They could also restore antiferromagnetism by adding oxygen back into the material.

Now that the team has shown doping effectively switches AFM on and off, scientists might use more practical ways to dope similar materials. For instance, silicon-based transistors are switched using voltage-activated “gates,” where a small voltage is applied to a bit to alter its electrical conductivity. Comin says that antiferromagnetic bits could also be switched using suitable voltage gates, which would require less energy than other antiferromagnetic switching techniques.

“This could present an opportunity to develop a magnetic memory storage device that works similarly to silicon-based chips, with the added benefit that you can store information in AFM domains that are very robust and can be packed at high densities,” Comin says. “That’s key to addressing the challenges of a data-driven world.”

In fact, new research shows that stepchildren are not at a disadvantage compared to their peers from single-parent households and actually experience better outcomes than their halfsiblings — good news for the more than 113 million Americans that are part of a steprelationship.

Led by East Carolina University anthropologist Ryan Schacht and researchers from the University of Utah, the study, “Was Cinderella just a fairy tale? Survival differences between stepchildren and their half-siblings,” is available in the May edition of the Philosophical Transactions of the Royal Society B.

The study challenges the “Cinderella effect” theory. The effect contends that conflict within stepfamilies over physical, financial and emotional resources leads to higher mortality risks for stepchildren, and is a main factor in higher rates of abuse and neglect. The phenomenon suggests that stepparents play a major role in this abuse, hoarding resources for their biological children and leading to negative outcomes for stepchildren.

Schacht proposes that previous studies have placed blame for the negative outcomes associated with parental loss on stepparents yet have done so through an an “apples-to-oranges comparison.” Specifically, they compare of the long-term outcomes of children who have suffered trauma like parental loss versus children from stable households. When the team compared stepchild outcomes more appropriately among those children who too have experienced the economic and emotional hardships associated with parental loss, they found no difference. Specifically, the introduction stepparenents did not increase stepchild mortality.

“The idea of a stepparent, especially the stepmother, as being an agent of evil seems to be a story as old as time,” Schacht said. “It’s easy to sell the Cinderella effect’s result because we’ve been told these stories about the problems that stepfamilies experience for hundreds of years.

“We’re not denying that some stepchildren suffer,” he said. “However, if we truly believe it is the stepparent that is the source of negative outcomes for a stepchild, then we need to compare similar environments and experiences. A child that hasn’t lost a parent through death or divorce hasn’t experienced the same trauma that a stepchild has; comparing those two experiences and blaming the stepparent for diverging outcomes isn’t a fair comparison.”

The study compared the mortality of stepchildren whose parents remarried after the death of a spouse to children whose parents did not remarry and found three key findings:

* Parental mortality has a negative effect on children under 18 years old, especially for infants losing a mother;

* Children whose parents remarried after the loss of a spouse did not suffer a mortality rate any greater than children whose parents did not remarry; and

* Stepchildren received a protective effect when a halfsibling was introduced to their new family.

“The metrics of what makes a family successful — household stability, relationship stability and economic stability — are achieved by stepparents investing in their stepchildren to make that a reality. Coming in with an antagonistic approach doesn’t make sense if stepparents want their relationship to succeed.”

The research team analyzed a data set of more than 400,000 children from Utah from 1847-1940. Schacht said the time period provided an opportunity to analyze stepchild mortality rates in families during a natural fertility period where families were larger in size and most stepfamilies were formed due to the death of a parent.

The study adds that children who have suffered parental loss have more in common with their peers from single-parent households, facing many of the same educational, economic and health care disparities.

Schacht hopes the study will shed a light on public policy funding for interventions for families that have suffered parental loss.

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Materials provided by East Carolina University. Original written by Matt Smith. Note: Content may be edited for style and length.