Aurelia Butler

Scientists often refer to the neutrino as the “ghost particle.” Neutrinos were one of the most abundant particles at the origin of the universe and remain so today. Fusion reactions in the sun produce vast armies of them, which pour down on the Earth every day. Trillions pass through our bodies every second, then fly through the Earth as though it were not there.
“While first postulated almost a century ago and first detected 65 years ago, neutrinos remain shrouded in mystery because of their reluctance to interact with matter,” said Alessandro Lovato, a nuclear physicist at the U.S. Department of Energy’s (DOE) Argonne National Laboratory.
Lovato is a member of a research team from four national laboratories that has constructed a model to address one of the many mysteries about neutrinos — how they interact with atomic nuclei, complicated systems made of protons and neutrons (“nucleons”) bound together by the strong force. This knowledge is essential to unravel an even bigger mystery — why during their journey through space or matter neutrinos magically morph from one into another of three possible types or “flavors.”
To study these oscillations, two sets of experiments have been undertaken at DOE’s Fermi National Accelerator Laboratory (MiniBooNE and NOvA). In these experiments, scientists generate an intense stream of neutrinos in a particle accelerator, then send them into particle detectors over a long period of time (MiniBooNE) or five hundred miles from the source (NOvA).
Knowing the original distribution of neutrino flavors, the experimentalists then gather data related to the interactions of the neutrinos with the atomic nuclei in the detectors. From that information, they can calculate any changes in the neutrino flavors over time or distance. In the case of the MiniBooNE and NOvA detectors, the nuclei are from the isotope carbon-12, which has six protons and six neutrons.
“Our team came into the picture because these experiments require a very accurate model of the interactions of neutrinos with the detector nuclei over a large energy range,” said Noemi Rocco, a postdoc in Argonne’s Physics division and Fermilab. Given the elusiveness of neutrinos, achieving a comprehensive description of these reactions is a formidable challenge.
The team’s nuclear physics model of neutrino interactions with a single nucleon and a pair of them is the most accurate so far. “Ours is the first approach to model these interactions at such a microscopic level,” said Rocco. “Earlier approaches were not so fine grained.”
One of the team’s important findings, based on calculations carried out on the now-retired Mira supercomputer at the Argonne Leadership Computing Facility (ALCF), was that the nucleon pair interaction is crucial to model neutrino interactions with nuclei accurately. The ALCF is a DOE Office of Science User Facility.
“The larger the nuclei in the detector, the greater the likelihood the neutrinos will interact with them,” said Lovato. “In the future, we plan to extend our model to data from bigger nuclei, namely, those of oxygen and argon, in support of experiments planned in Japan and the U.S.”
Rocco added that “For those calculations, we will rely on even more powerful ALCF computers, the existing Theta system and upcoming exascale machine, Aurora.”
Scientists hope that, eventually, a complete picture will emerge of flavor oscillations for both neutrinos and their antiparticles, called “antineutrinos.” That knowledge may shed light on why the universe is built from matter instead of antimatter — one of the fundamental questions about the universe.

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Materials provided by DOE/Argonne National Laboratory. Original written by Joseph E. Harmon. Note: Content may be edited for style and length. More

Researchers at Mass Eye and Ear have developed a unique diagnostic tool that can detect dystonia from MRI scans, the first technology of its kind to provide an objective diagnosis of the disorder. Dystonia is a potentially disabling neurological condition that causes involuntary muscle contractions, leading to abnormal movements and postures. It is often misdiagnosed and can take people up to 10 years to get a correct diagnosis.
In a new study published September 28 in Proceedings of the National Academy of Sciences, researchers developed an AI-based deep learning platform — called DystoniaNet — to compare brain MRIs of 612 people, including 392 patients with three different forms of isolated focal dystonia and 220 healthy individuals. The platform diagnosed dystonia with 98.8 percent accuracy. During the process, the researchers identified a new microstructural neural network biological marker of dystonia. With further testing and validation, they believe DystoniaNet can be easily integrated into clinical decision-making.
“There is currently no biomarker of dystonia and no ‘gold standard’ test for its diagnosis. Because of this, many patients have to undergo unnecessary procedures and see different specialists until other diseases are ruled out and the diagnosis of dystonia is established,” said senior study author Kristina Simonyan, MD, PhD, Dr med, Director of Laryngology Research at Mass Eye and Ear, Associate Neuroscientist at Massachusetts General Hospital, and Associate Professor of Otolaryngology-Head and Neck Surgery at Harvard Medical School. “There is a critical need to develop, validate and incorporate objective testing tools for the diagnosis of this neurological condition, and our results show that DystoniaNet may fill this gap.”
A disorder notoriously difficult to diagnose
About 35 out of every 100,000 people have isolated or primary dystonia — prevalence that is likely underestimated due to the current challenges in diagnosing this disorder. In some cases, dystonia can be a result of a neurological event, such as Parkinson’s disease or a stroke. However, the majority of isolated dystonia cases have no known cause and affect a single muscle group in the body. These so-called focal dystonias can lead to disability and problems with physical and emotional quality of life.
The study included three of the most common types of focal dystonia: Laryngeal dystonia, characterized by involuntary movements of the vocal cords that can cause difficulties with speech (also called spasmodic dysphonia); Cervical dystonia, which causes the neck muscles to spasm and the neck to tilt in an unusual manner; Blepharospasm, a focal dystonia of the eyelid that causes involuntary twitching and forceful eyelid closure.

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Traditionally, a dystonia diagnosis is made based on clinical observations, said Dr. Simonyan. Previous studies have found that the agreement on dystonia diagnosis between clinicians based on purely clinical assessments is as low as 34 percent and have reported that about 50 percent of the cases go misdiagnosed or underdiagnosed at a first patient visit.
DystoniaNet could be integrated into medical decision-making
DystoniaNet utilizes deep learning, a particular type of AI algorithm, to analyze data from individual MRI and identify subtler differences in brain structure. The platform is able to detect clusters of abnormal structures in several regions of the brain that are known to control processing and motor commands. These small changes cannot be seen by a naked eye in MRI, and the patterns are only evident through the platform’s ability to take 3D brain images and zoom into their microstructural details.
“Our study suggests that the implementation of the DystoniaNet platform for dystonia diagnosis would be transformative for the clinical management of this disorder,” said the first study author Davide Valeriani, PhD, a postdoctoral research fellow in the Dystonia and Speech Motor Control Laboratory at Mass Eye and Ear and Harvard Medical School. “Importantly, our platform was designed to be efficient and interpretable for clinicians, by providing the patient’s diagnosis, the confidence of the AI in that diagnosis, and information about which brain structures are abnormal.”
DystoniaNet is a patent-pending proprietary platform developed by Dr. Simonyan and Dr. Valeriani, in conjunction with Mass General Brigham Innovation. The technology interprets an MRI scan for microstructural biomarker in 0.36 seconds. DystoniaNet has been trained using Amazon Web Services computational cloud platform. The researchers believe this technology can be easily translated into the clinical setting, such as by being integrated in an electronic medical record or directly in the MRI scanner software. If DystoniaNet finds a high probability of dystonia in the MRI, a physician can use this information to help confidently confirm the diagnosis, pursue future actions, and suggest a course of treatment without a delay. Dystonia cannot be cured, but some treatments can help reduce the incidence of dystonia-related spasms.
Future studies will look at more types of dystonia and will include trials at multiple hospitals to further validate the DystoniaNet platform in a larger number of patients.
This research was funded and supported by the National Institutes of Health (R01DC011805, R01DC012545, R01NS088160), Amazon Web Services through the Machine Learning Research Award, and a charitable gift by Keith and Bobbi Richardson. More

Biometric authentication, which uses unique anatomical features such as fingerprints or facial features to verify a person’s identity, is increasingly replacing traditional passwords for accessing everything from smartphones to law enforcement systems. A newly developed approach that uses 3D images of finger veins could greatly increase the security of this type of authentication.
“The 3D finger vein biometric authentication method we developed enables levels of specificity and anti-spoofing that were not possible before,” said Jun Xia, from University at Buffalo, The State University of New York, research team leader. “Since no two people have exactly the same 3D vein pattern, faking a vein biometric authentication would require creating an exact 3D replica of a person’s finger veins, which is basically not possible.”
In the Optical Society (OSA) journal Applied Optics, the researchers describe their new approach, which represents the first time that photoacoustic tomography has been used for 3D finger vein biometric authentication. Tests of the method on people showed that it can correctly accept or reject an identity 99 percent of the time.
“Due to the COVID-19 pandemic, many jobs and services are now performed remotely,” said research team member Giovanni Milione, from NEC Laboratories America, Inc. “Because our technique detects invisible features in 3D, it could be used to enable better authentication techniques to protect personnel data and sensitive documents.”
Adding depth information
Although other biometric authentication approaches based on finger veins have been developed, they are all based on 2D images. The additional depth from a 3D image increases security by making it more difficult to fake an identity and less likely that the technique will accept the wrong person or reject the right one.

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To accomplish 3D biometric authentication using the veins in a person’s fingers, the researchers turned to photoacoustic tomography, an imaging technique that combines light and sound. First, light from a laser is used to illuminate the finger. If the light hits a vein, it creates a sound much in the same way that a grill creates a “poof” sound when it is first lit. The system then detects that sound with an ultrasound detector and uses it to reconstruct a 3D image of the veins.
“It has been challenging to use photoacoustic tomography for 3D finger vein biometric authentication because of the bulky imaging system, small field of view and inconvenient positioning of the hand,” said Xia. “We addressed these issues in the new system design through a better combination of light and acoustic beams and custom-made transducers to improve the imaging field of view.”
Designing a practical system
To better integrate light illumination and acoustic detection, the researcher fabricated a new light- and acoustic-beam combiner. They also designed an imaging window that allows the hand to be naturally placed on the platform, similar to a full-size fingerprint scanner. Another critical development was a new matching algorithm, developed by Wenyao Xu from the Computer science and Engineering department that allows biometric identification and matching of features in 3D space.
The researchers tested their new system with 36 people by imaging their four left and four right fingers. The tests showed that the approach was not only feasible but also accurate, especially when multiple fingers were used.
“We envision this technique being used in critical facilities, such as banks and military bases, that require a high level of security,” said Milione. “With further miniaturization 3D vein authentication could also be used in personal electronics or be combined with 2D fingerprints for two-factor authentication.”
The researchers are now working to make the system even smaller and to reduce the imaging time to less than one second. They note that it should be possible to implement the photoacoustic system in smartphones since ultrasound systems have already been developed for use in smartphones. This could enable portable or wearable systems that perform biometric authentication in real time.

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For over 15 years, ETH Professor Andreas Hierlemann and his group have been developing microelectrode-array chips that can be used to precisely excite nerve cells in cell cultures and to measure electrical cell activity. These developments make it possible to grow nerve cells in cell-culture dishes and use chips located at the bottom of the dish to examine each individual cell in a connected nerve tissue in detail. Alternative methods for conducting such measurements have some clear limitations. They are either very time-consuming — because contact to each cell has to be individually established — or they require the use of fluorescent dyes, which influence the behaviour of the cells and hence the outcome of the experiments.
Now, researchers from Hierlemann’s group at the Department of Biosystems Science and Engineering of ETH Zurich in Basel, together with Urs Frey and his colleagues from the ETH spin-off MaxWell Biosystems, developed a new generation of microelectrode-array chips. These chips enable detailed recordings of considerably more electrodes than previous systems, which opens up new applications.
Stronger signal required
As with previous chip generations, the new chips have around 20,000 microelectrodes in an area measuring 2 by 4 millimetres. To ensure that these electrodes pick up the relatively weak nerve impulses, the signals need to be amplified. Examples of weak signals that the scientists want to detect include those of nerve cells, derived from human pluripotent stem cells (iPS cells). These are currently used in many cell-culture disease models. Another reason to significantly amplify the signals is if the researchers want to track nerve impulses in axons (fine, very thin fibrous extensions of a nerve cell).
However, high-performance amplification electronics take up space, which is why the previous chip was able to simultaneously amplify and read out signals from only 1,000 of the 20,000 electrodes. Although the 1,000 electrodes could be arbitrarily selected, they had to be determined prior to every measurement. This meant that it was possible to make detailed recordings over only a fraction of the chip area during a measurement.
Background noise reduced
In the new chip, the amplifiers are smaller, permitting the signals of all 20,000 electrodes to be amplified and measured at the same time. However, the smaller amplifiers have higher noise levels. So, to make sure they capture even the weakest nerve impulses, the researchers included some of the larger and more powerful amplifiers into the new chips and employ a nifty trick: they use these powerful amplifiers to identify the time points, at which nerve impulses occur in the cell culture dish. At these time points, they then can search for signals on the other electrodes, and by taking the average of several successive signals, they can reduce the background noise. This procedure yields a clear image of the signal activity over the entire area being measured.
In first experiments, which the researchers published in the journal Nature Communications, they demonstrated their method on human iPS-derived neuronal cells as well as on brain sections, retina pieces, cardiac cells and neuronal spheroids.
Application in drug development
With the new chip, the scientists can produce electrical images of not only the cells but also the extension of their axons, and they can determine how fast a nerve impulse is transmitted to the farthest reaches of the axons. “The previous generations of microelectrode array chips let us measure up to 50 nerve cells. With the new chip, we can perform detailed measurements of more than 1,000 cells in a culture all at once,” Hierlemann says.
Such comprehensive measurements are suitable for testing the effects of drugs, meaning that scientists can now conduct research and experiments with human cell cultures instead of relying on lab animals. The technology thus also helps to reduce the number of animal experiments.
The ETH spin-off MaxWell Biosystems is already marketing the existing microelectrode technology, which is now in use around the world by over a hundred research groups at universities and in industry. At present, the company is looking into a potential commercialisation of the new chip.

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Materials provided by ETH Zurich. Original written by Fabio Bergamin. Note: Content may be edited for style and length. More

New research published in EPJ D has revealed how robust initial states can be prepared in quantum information systems, minimising any unwanted transitions which lead to losses in quantum information.
Through new techniques for generating ‘exceptional points’ in quantum information systems, researchers have minimised the transitions through which they lose information to their surrounding environments.
Recently, researchers have begun to exploit the effects of quantum mechanics to process information in some fascinating new ways. One of the main challenges faced by these efforts is that systems can easily lose their quantum information as they interact with particles in their surrounding environments. To understand this behaviour, researchers in the past have used advanced models to observe how systems can spontaneously evolve into different states over time — losing their quantum information in the process. Through new research published in EPJ D, M. Reboiro and colleagues at the University of La Plata in Argentina have discovered how robust initial states can be prepared in quantum information systems, avoiding any unwanted transitions extensive time periods.
The team’s findings could provide valuable insights for the rapidly advancing field of quantum computing; potentially enabling more complex operations to be carried out using the cutting-edge devices. Their study considered a ‘hybrid’ quantum information system based around a specialised loop of superconducting metal, which interacted with an ensemble of imperfections within the atomic lattice of diamond. Within this system, the researchers aimed to generate sets of ‘exceptional points.’ When these are present, information states don’t decay in the usual way: instead, any gains and losses of quantum information can be perfectly balanced between states.
By accounting for quantum effects, Reboiro and colleagues modelled how the dynamics of ensembled imperfections were affected by their surrounding environments. From these results, they combined information states which displayed large transition probabilities over long time intervals — allowing them to generate exceptional points. Since this considerably increased the survival probability of a state, the team could finally prepare initial states which were robust against the effects of their environments. Their techniques could soon be used to build quantum information systems which retain their information for far longer than was previously possible.

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In large systems of interacting particles in quantum mechanics, an intriguing phenomenon often emerges: groups of particles begin to behave like single particles. Physicists refer to such groups of particles as quasiparticles.
Understanding the properties of quasiparticles may be key to comprehending, and eventually controlling, technologically important quantum effects like superconductivity and superfluidity.
Unfortunately, quasiparticles are only useful while they live. It is thus particularly unfortunate that many quasiparticles die young, lasting far, far less than a second.
The authors of a new Monash University-led study published today in Physical Review Letters investigate the crucial question: how do quasiparticles die?
Beyond the usual suspect — quasiparticle decay into lower energy states — the authors identify a new culprit: many-body dephasing.

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MANY BODY DEPHASING
Many-body dephasing is the disordering of the constituent particles in the quasiparticle that occurs naturally over time.
As the disorder increases, the quasiparticle’s resemblance to a single particle fades. Eventually, the inescapable effect of many-body dephasing kills the quasiparticle.
Far from a negligible effect, the authors demonstrate that many-body dephasing can even dominate over other forms of quasiparticle death.
This is shown through investigations of a particularly ‘clean’ quasiparticle — an impurity in an ultracold atomic gas — where the authors find strong evidence of many-body dephasing in past experimental results.
The authors focus on the case where the ultracold atomic gas is a Fermi sea. An impurity in a Fermi sea gives rise to a quasiparticle known as the repulsive Fermi polaron.
The repulsive Fermi polaron is a highly complicated quasiparticle and has a history of eluding both experimental and theoretical studies.
Through extensive simulations and new theory, the authors show that an established experimental protocol — Rabi oscillations between impurity spin states — exhibits the effects of many-body dephasing in the repulsive Fermi polaron.
These previously unrecognised results provide strong evidence that many-body dephasing is fundamental to the nature of quasiparticles. More

Millions of people turn to their mobile devices when seeking medical advice. They’re able to share their symptoms and receive potential diagnoses through chatbot-based symptom-checker (CSC) apps.
But how do these apps compare to a trip to the doctor’s office?
Not well, according to a new study. Researchers from Penn State’s College of Information Sciences and Technology have found that existing CSC apps lack the functions to support the full diagnostic process of a traditional visit to a medical facility. Rather, they said, the apps can only support five processes of an actual exam: establishing a patient history, evaluating symptoms, giving an initial diagnosis, ordering further diagnostic tests, and providing referrals or other follow-up treatments.
“These apps do not support conducting physical exams, providing a final diagnosis, and performing and analyzing test results, because these three processes are difficult to realize using mobile apps,” said Yue You, a graduate student in the College of Information Sciences and Technology and lead author on the study.
In the study, the researchers investigated the functionalities of popular CSC apps through a feature review, then examined user experiences by analyzing user reviews and conducting user interviews. Through their user experience analysis, You and her team also found that users perceive CSC apps to lack support for a comprehensive medical history, flexible symptom input, comprehensible questions, and diverse diseases and user groups.
The findings could inform functional and conversational design updates for health care chatbots, such as improving the functions that enable users to input their symptoms or using comprehensible language and providing explanations during conversations.
“Especially in health and medicine, [another question is] is there something else we should consider in the chatbot design, such as how should we let users describe their symptoms when interacting with the chatbot?” said You.
Additionally, the findings could help individuals understand the influence of AI technology, such as how AI could influence or change traditional medical visits.
“In the past, people generally trusted doctors,” You said. “But now with the emergence of AI symptom checkers and the internet, people have more sources of information. How would this information challenge doctors? Do people trust this information and why? I think this work is a starting point to think about the influence of AI symptom checkers.”
The findings will be presented at the American Medical Informatics Association (AMIA) Virtual Annual Symposium in November.
You’s work serves as a preliminary study for future in-depth exploration. Currently, she is working to investigate how to design a better, explainable COVID-19 symptom checker with College of Information Sciences and Technology faculty members Xinning Gui, assistant professor; Jack Carroll, Distinguished Professor of Information Sciences and Technology; Yubo Kou, assistant professor; and Chun-Hua Tsai, assistant research professor.

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Materials provided by Penn State. Original written by Jessica Hallman. Note: Content may be edited for style and length. More

Gravitational wave detectors have opened a new window to the universe by measuring the ripples in spacetime produced by colliding black holes and neutron stars, but they are ultimately limited by quantum fluctuations induced by light reflecting off of mirrors. LSU Ph.D. physics alumnus Jonathan Cripe and his team of LSU researchers have conducted a new experiment with scientists from Caltech and Thorlabs to explore a way to cancel this quantum backaction and improve detector sensitivity.
In a new paper in Physical Review X, the investigators present a method for removing quantum backaction in a simplified system using a mirror the size of a human hair and show the motion of the mirror is reduced in agreement with theoretical predictions. The research was supported by the National Science Foundation.
Despite using 40-kilogram mirrors for detecting passing gravitational waves, quantum fluctuations of light disturb the position of the mirrors when the light is reflected. As gravitational wave detectors continue to grow more sensitive with incremental upgrades, this quantum backaction will become a fundamental limit to the detectors’ sensitivity, hampering their ability to extract astrophysical information from gravitational waves.
“We present an experimental testbed for studying and eliminating quantum backaction,” Cripe said. “We perform two measurements of the position of a macroscopic object whose motion is dominated by quantum backaction and show that by making a simple change in the measurement scheme, we can remove the quantum effects from the displacement measurement. By exploiting correlations between the phase and intensity of an optical field, quantum backaction is eliminated.”
Garrett Cole, technology manager at Thorlabs Crystalline Solutions (Crystalline Mirror Solutions was acquired by Thorlabs Inc. last year), and his team constructed the micromechanical mirrors from an epitaxial multilayer consisting of alternating GaAs and AlGaAs. An outside foundry, IQE North Carolina, grew the crystal structure while Cole and his team, including process engineers Paula Heu and David Follman, manufactured the devices at the University of California Santa Barbara nanofabrication facility.
“By performing this measurement on a mirror visible to the naked eye — at room temperature and at frequencies audible to the human ear — we bring the subtle effects of quantum mechanics closer to the realm of human experience,” said LSU Ph.D. candidate Torrey Cullen. “By quieting the quantum whisper, we can now listen to the more subtle notes of the cosmic symphony.”
“This research is especially timely because the Laser Interferometer Gravitational-wave Observatory, or LIGO, just announced last month in Nature that they have seen the effects of quantum radiation pressure noise at the LIGO Livingston observatory,” said Thomas Corbitt, associate professor in the LSU Department of Physics & Astronomy.
The effort behind that paper, “Quantum correlations between light and the kilogram-mass mirrors of LIGO,” has been led by Nergis Mavalvala, dean of the MIT School of Science, as well as postdoctoral scholar Haocun Yu and research scientist Lee McCuller, both at the MIT Kavli Institute for Astrophysics and Space Research.
“Quantum radiation pressure noise is already poking out of the noise floor in Advanced LIGO, and before long, it will be a limiting noise source in GW detectors,” Mavalvala said. “Deeper astrophysical observations will only be possible if we can reduce it, and this beautiful result from the Corbitt group at LSU demonstrates a technique for doing just that.”

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