Computers Math

Silicon is one of the most abundant elements on Earth, and in its pure form the material has become the foundation of much of modern technology, from solar cells to computer chips. But silicon’s properties as a semiconductor are far from ideal.
For one thing, although silicon lets electrons whizz through its structure easily, it is much less accommodating to “holes” — electrons’ positively charged counterparts — and harnessing both is important for some kinds of chips. What’s more, silicon is not very good at conducting heat, which is why overheating issues and expensive cooling systems are common in computers.
Now, a team of researchers at MIT, the University of Houston, and other institutions has carried out experiments showing that a material known as cubic boron arsenide overcomes both of these limitations. It provides high mobility to both electrons and holes, and has excellent thermal conductivity. It is, the researchers say, the best semiconductor material ever found, and maybe the best possible one.
So far, cubic boron arsenide has only been made and tested in small, lab-scale batches that are not uniform. The researchers had to use special methods originally developed by former MIT postdoc Bai Song to test small regions within the material. More work will be needed to determine whether cubic boron arsenide can be made in a practical, economical form, much less replace the ubiquitous silicon. But even in the near future, the material could find some uses where its unique properties would make a significant difference, the researchers say.
The findings are reported in the journal Science, in a paper by MIT postdoc Jungwoo Shin and MIT professor of mechanical engineering Gang Chen; Zhifeng Ren at the University of Houston; and 14 others at MIT, the University of Houston, the University of Texas at Austin, and Boston College.
Earlier research, including work by David Broido, who is a co-author of the new paper, had theoretically predicted that the material would have high thermal conductivity; subsequent work proved that prediction experimentally. This latest work completes the analysis by confirming experimentally a prediction made by Chen’s group back in 2018: that cubic boron arsenide would also have very high mobility for both electrons and holes, “which makes this material really unique,” says Chen. More

Graphene consists of carbon atoms that crosslink in a plane to form a flat honeycomb structure. In addition to surprisingly high mechanical stability, the material has exciting electronic properties: The electrons behave like massless particles, which can be clearly demonstrated in spectrometric experiments. Measurements reveal a linear dependence of energy on momentum, namely the so-called Dirac cones — two lines that cross without a band gap — i.e. an energy difference between electrons in the conduction band and those in the valence bands.
Variants in graphene architecture
Artificial variants of graphene architecture are a hot topic in materials research right now. Instead of carbon atoms, quantum dots of silicon have been placed, ultracold atoms have been trapped in the honeycomb lattice with strong laser fields, or carbon monoxide molecules have been pushed into place on a copper surface piece by piece with a scanning tunneling microscope, where they could impart the characteristic graphene properties to the electrons of the copper.
Artificial graphene with buckyballs?
A recent study suggested that it is infinitely easier to make artificial graphene using C60 molecules called buckyballs. Only a uniform layer of these needs to be vapor-deposited onto gold for the gold electrons to take on the special graphene properties. Measurements of photoemission spectra appeared to show a kind of Dirac cone.
Analysis of band structures at BESSY II
“That would be really quite amazing,” says Dr. Andrei Varykhalov, of HZB, who heads a photoemission and scanning tunneling microscopy group. “Because the C60 molecule is absolutely nonpolar, it was hard for us to imagine how such molecules would exert a strong influence on the electrons in the gold.” So Varykhalov and his team launched a series of measurements to test this hypothesis.
In tricky and detailed analyses, the Berlin team was able to study C60 layers on gold over a much larger energy range and for different measurement parameters. They used angle-resolved ARPES spectroscopy at BESSY II, which enables particularly precise measurements, and also analysed electron spin for some measurements.
Normal behavior
“We see a parabolic relationship between momentum and energy in our measured data, so it’s a very normal behavior. These signals come from the electrons deep in the substrate (gold or copper) and not the layer, which could be affected by the buckyballs,” explains Dr. Maxim Krivenkov, lead author of the study. The team was also able to explain the linear measurement curves from the previous study. “These measurement curves merely mimic the Dirac cones; they are an artifact, so to speak, of a deflection of the photoelectrons as they leave the gold and pass through the C60 layer,” Varykhalov explains. Therefore, the buckyball layer on gold cannot be considered an artificial graphene.
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Materials provided by Helmholtz-Zentrum Berlin für Materialien und Energie. Note: Content may be edited for style and length. More

Imaging of tissue specimens is an important aspect of translational research that bridges the gap between basic laboratory science and clinical science to improve the understanding of cancer and aid in the development of new therapies. To analyze images to their fullest potential, scientists ideally need an application that enables multiple images to be viewed simultaneously. In an article published in the journal Patterns, Moffitt Cancer Center researchers describe a new open-source software program they developed that allows users to view many multiplexed images simultaneously.
There have been significant improvements in the approaches to study cancer over the past decade, including new techniques to study tissue samples. For example, machines can now be programmed to stain hundreds of slides simultaneously, or alternatively, up to 1,000 different tissue sample cores can be placed on a single slide and stained for biomarkers at the same time. With the advent of these approaches comes a wealth of possibilities to generate new data and information. Due to the magnitude of this information and the complex nature of cancer itself, computational modeling and software are needed to view and study the cancer biomarkers, tissue architecture, and cellular interactions among these samples.
As researchers in Moffitt’s Integrated Mathematical Oncology Department (IMO) were working on a project, they realized that the currently available software for image viewing was not amenable to their needs.
“We were interested in understanding the underlying spatial patterns between tumor and immune cells and how the tumors were organized. This required us to compare multiple images simultaneously and we realized there was no software, free or commercial, enabling this,” said Sandhya Prabhakaran, Ph.D., lead author and applied research scientist at Moffitt.
The IMO team decided to create a software program that would enable them to view multiple images at the same time and extract data through additional analyses that could be used for a variety of purposes, including identifying biomarkers and understanding tissue architecture and the spatial organization of different cell types. Their program, called Mistic, takes information from multidimensional images and uses dimensionality reduction methods called t-distributed stochastic neighbor embedding (t-SNE) to abstract each image to a point in reduced space. Mistic is an open-source software that can be used with images from Vectra, CyCIF, t-CyCIF and CODEX.
In their publication, the researchers describe the creation of Mistic and some of the applications that it could be used for. For example, they demonstrated that the software could be used to view 92 images from patients with non-small cell lung cancer and deduce how biomarkers cluster across patients with different responses to treatment. In another example, the researchers used Mistic combined with statistical analysis to assess the spatial colocalization and coexpression of immune cell markers in 210 endometrial cancer samples.
The team is excited about the potential applications for Mistic and have plans to improve the software.
“We will enhance Mistic to use biologically meaningful regions of interest from the multiplexed image to render the overall image t-SNE. We also have plans to augment Mistic with other visualization software and build a cross-platform viewer plugin to improve the adoption, usability and functionality of Mistic in the biomedical research community,” said Sandy Anderson, Ph.D., author and chair of Moffitt’s IMO Department.
In addition to Mistic, the Patterns featured the IMO team in a People of Data article titled “Developing tools for analyzing and viewing multiplexed images.” Here, the IMO team gets to introduce themselves, discuss their research passion and the challenges and opportunities relevant to imaging in mathematical oncology. More

Patients are 20% less likely to die of sepsis because a new AI system developed at Johns Hopkins University catches symptoms hours earlier than traditional methods, an extensive hospital study demonstrates. The system, created by a Johns Hopkins researcher whose young nephew died from sepsis, scours medical records and clinical notes to identify patients at risk of life-threatening complications. The work, which could significantly cut patient mortality from one of the top causes of hospital deaths worldwide, is published today in Nature Medicine and Nature Digital Medicine.
“It is the first instance where AI is implemented at the bedside, used by thousands of providers, and where we’re seeing lives saved,” said Suchi Saria, founding research director of the Malone Center for Engineering in Healthcare at Johns Hopkins and lead author of the studies, which evaluated more than a half million patients over two years. “This is an extraordinary leap that will save thousands of sepsis patients annually. And the approach is now being applied to improve outcomes in other important problem areas beyond sepsis.” Sepsis occurs when an infection triggers a chain reaction throughout the body. Inflammation can lead to blood clots and leaking blood vessels, and ultimately can cause organ damage or organ failure. About 1.7 million adults develop sepsis every year in the United States and more than 250,000 of them die.
Sepsis is easy to miss since symptoms such as fever and confusion are common in other conditions, Saria said. The faster it’s caught, the better a patient’s chances for survival. “One of the most effective ways of improving outcomes is early detection and giving the right treatments in a timely way, but historically this has been a difficult challenge due to lack of systems for accurate early identification,” said Saria, who directs the Machine Learning and Healthcare Lab at Johns Hopkins.
To address the problem, Saria and other Johns Hopkins doctors and researcher developed the Targeted Real-Time Early Warning System. Combining a patient’s medical history with current symptoms and lab results, the machine-learning system shows clinicians when someone is at risk for sepsis and suggests treatment protocols, such as starting antibiotics. The AI tracks patients from when they arrive in the hospital through discharge, ensuring that critical information isn’t overlooked even if staff changes or a patient moves to a different department. During the study, more than 4,000 clinicians from five hospitals used the AI in treating 590,000 patients. The system also reviewed 173,931 previous patient cases. In 82% of sepsis cases, the AI was accurate nearly 40% of the time.
Previous attempts to use electronic tools to detect sepsis caught less than half that many cases and were accurate 2% to 5% of the time. All sepsis cases are eventually caught, but with the current standard of care, the condition kills 30% of the people who develop it. In the most severe sepsis cases where an hour delay is the difference between life and death, the AI detected it an average of nearly six hours earlier than traditional methods. “This is a breakthrough in many ways,” said co-author Albert Wu, an internist and director of the Johns Hopkins Center for Health Services and Outcomes Research.
“Up to this point, most of these types of systems have guessed wrong much more often than they get it right. Those false alarms undermine confidence.” Unlike conventional approaches, the system allows doctors to see why the tool is making specific recommendations. The work is extremely personal to Saria, who lost her nephew as a young adult to sepsis. “Sepsis develops very quickly and this is what happened in my nephew’s case,” she said. “When doctors detected it, he was already in septic shock.” Bayesian Health, a company spun-off from Johns Hopkins, led and managed the deployment across all testing sites. The team also partnered with the two largest electronic health record system providers, Epic and Cerner, to ensure that the tool can be implemented at other hospitals. The team has adapted the technology to identify patients at risk for pressure injuries, commonly known as bed sores, and those at risk for sudden deterioration caused by bleeding, acute respiratory failure, and cardiac arrest.
“The approach used here is foundationally different,” Saria said. “It’s adaptive and takes into consideration the diversity of the patient population, the unique ways in which doctors and nurses deliver care across different sites, and the unique characteristics of each health system, allowing it to be significantly more accurate and to gain provider trust and adoption.”
Co-authors of the three studies in Nature Medicine and Nature Digital Medicine include Katharine Henry, Roy Adams, Cassandra Parent, David Hager, Edward Chen, Mustapha Saheed, and Albert Wu of Johns Hopkins University; Hossein Soleimani of University of California, San Francisco; Anirudh Sridharan of Howard County General Hospital; Lauren Johnson, Maureen Henley, Sheila Miranda, Katrina Houston, and Anushree Ahluwalia of The Johns Hopkins Hospital; Sara Cosgrove and Eili Klein of Johns Hopkins University School of Medicine; Andrew Markowski of Suburban Hospital; and Robert Linton of Howard County General Hospital.
The work was funded by the Gordon and Betty Moore Foundation (No. 3926 and 3186.01), the National Science Foundation Future of Work at the Human-technology Frontier (No. 1840088), and the Alfred P. Sloan Foundation research fellowship (2018).
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Materials provided by Johns Hopkins University. Original written by Laura Cech. Note: Content may be edited for style and length. More

For decades computers have been synonymous with binary information — zeros and ones. Now a team at the University of Innsbruck, Austria, realized a quantum computer that breaks out of this paradigm and unlocks additional computational resources, hidden in almost all of today’s quantum devices.
We all learn from early on that computers work with zeros and ones, also known as binary information. This approach has been so successful that computers now power everything from coffee machines to self-driving cars and it is hard to imagine a life without them.
Building on this success, today’s quantum computers are also designed with binary information processing in mind. “The building blocks of quantum computers, however, are more than just zeros and ones,” explains Martin Ringbauer, an experimental physicist from Innsbruck, Austria. “Restricting them to binary systems prevents these devices from living up to their true potential.”
The team led by Thomas Monz at the Department of Experimental Physics at the University of Innsbruck, now succeeded in developing a quantum computer that can perform arbitrary calculations with so-called quantum digits (qudits), thereby unlocking more computational power with fewer quantum particles.
Quantum systems are different
Although storing information in zeros and ones is not the most efficient way of doing calculations, it is the simplest way. Simple often also means reliable and robust to errors and so binary information has become the unchallenged standard for classical computers.
In the quantum world, the situation is quite different. In the Innsbruck quantum computer, for example, information is stored in individual trapped Calcium atoms. Each of these atoms naturally has eight different states, of which typically only two are used to store information. Indeed, almost all existing quantum computers have access to more quantum states than they use for computation.
A natural approach for hardware and software
The physicists from Innsbruck now developed a quantum computer that can make use of the full potential of these atoms, by computing with qudits. Contrary to the classical case, using more states does not make the computer less reliable. “Quantum systems naturally have more than just two states and we showed that we can control them all equally well,” says Thomas Monz.
On the flipside, many of the tasks that need quantum computers, such as problems in physics, chemistry, or material science, are also naturally expressed in the qudit language. Rewriting them for qubits can often make them too complicated for today’s quantum computers. “Working with more than zeros and ones is very natural, not only for the quantum computer but also for its applications, allowing us to unlock the true potential of quantum systems,” explains Martin Ringbauer.
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Materials provided by University of Innsbruck. Note: Content may be edited for style and length. More

Scientists today can access genomic data from Indigenous Peoples without their free, prior, and informed consent, leading to potential misuse and the reinforcement of stereotypes. Despite existing tools that facilitate the sharing of genomic information with researchers, none of those options give Indigenous governments control over how these data are used. In an article publishing in the journal Cell on July 21, authors propose a new blockchain model where researchers are only allowed to access the genomic data after the Indigenous entities have approved the research project.

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An artificial intelligence-driven device that works to detect and predict hemodynamic instability may provide a more accurate picture of patient deterioration than traditional vital sign measurements, a Michigan Medicine study suggests.
Researchers captured data from over 5,000 adult patients at University of Michigan Health with the Analytic for Hemodynamic Instability. Developed at the U-M Weil Institute for Critical Care Research and Innovation, AHI is a software as a medical device designed to detect and predict changes in hemodynamic status in real-time using data from a single electrocardiogram lead. The researchers compared the results against gold standard vital sign measurements of continuous heart rate and blood pressure measured by invasive arterial monitoring in several intensive care units to determine if the AHI could indicate hemodynamic instability in real-time.
They found that the AHI detected standard indications of hemodynamic instability, a combination of elevated heart rate and low blood pressure, with nearly 97% sensitivity and 79% specificity. The results are published in Critical Care Explorations (a Society of Critical Care Medicine journal).
The findings suggest that the AHI may be able to provide continuous dynamic monitoring capabilities in patients who traditionally have intermittent static vital sign measurements, says senior author Ben Bassin, M.D., director of the Joyce and Don Massey Family Foundation Emergency Critical Care Center, also known as EC3, and an associate professor of emergency medicine at U-M Medical School.
“AHI performs extremely well, and it functions in a way that we think may have transformative clinical utility,” Bassin said. “Most vital signs measurements are static, subject to human error, and require validation and interpretation. AHI is the opposite of that. It’s dynamic, produces a binary output of ‘stable’ or ‘unstable,’ and it may enable early martialing of resources to patients who may not have been on a clinician’s radar.”
Traditional vital signs have limitations, including limited accuracy in non-invasive monitoring and the fact that patients who are not at obvious risk for immediate deterioration may only be monitored periodically every 4-6 hours or longer. The AHI, which was approved by the United States Food and Drug Administration in 2021 and is licensed to Fifth Eye, Inc. (a U-M spinoff), was designed to address those limitations.
“The vision of AHI was born out of our continued inability to identify unstable patients and to predict when patients would become unstable, especially in settings where they cannot be intensively monitored, said co-author Kevin Ward, M.D., executive director of the Weil Institute and professor of emergency medicine and biomedical engineering at Michigan Medicine.
“AHI is ideally suited to be utilized with wearable monitors such as ECG patches, that could make any hospital bed, waiting room or other setting into a sophisticated monitoring environment. The implication of such a technology is that it has the potential to save lives not only in the hospital, but also at home, in the ambulance and on the battlefield.”
Researchers say future studies are needed to determine if AHI provides clinical and resource allocation benefits in patients undergoing infrequent blood pressure monitoring. The next phase of research will focus on how AHI is used at Michigan Medicine.
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Materials provided by Michigan Medicine – University of Michigan. Original written by Noah Fromson. Note: Content may be edited for style and length. More

Researchers have developed a new method for flexibly creating various needle-shaped laser beams. These long, narrow beams can be used to improve optical coherence tomography (OCT), a noninvasive and versatile imaging tool that is used for scientific research and various types of clinical diagnoses.
“Needle-shaped laser beams can effectively extend the depth-of-focus of an OCT system, improving the lateral resolution, signal-to-noise ratio, contrast and image quality over a long depth range,” said research team leader Adam de la Zerda from Stanford University School of Medicine. “However, before now, implementing a specific needle-shaped beam has been difficult due to the lack of a common, flexible generation method.”
In Optica, Optica Publishing Group’s journal for high-impact research, the researchers describe their new platform for creating needle-shaped beams with different lengths and diameters. It can be used to create various types of beams such as one with an extremely long depth of field or one that is smaller than the diffraction-limit of light, for example.
The needle-shaped beams generated with this method could benefit a variety of OCT applications. For example, utilizing a long, narrow beam could allow high-resolution OCT imaging of the retina without any dynamic focusing, making the process faster and thus more comfortable for patients. It could also extend the depth-of-focus for OCT endoscopy, which would improve diagnosis accuracy.
“The rapid high-resolution imaging ability of needle-shaped beams can also get rid of adverse effects that occur due to human movements during image acquisition,” said the paper’s first author Jingjing Zhao. “This can help to pinpoint melanoma and other skin problems using OCT.”
A flexible solution
As a noninvasive imaging tool, OCT features an axial resolution that is constant along its imaging depth. However, its axial resolution, which is determined by the light source, has a very small depth of focus. To address this issue, OCT instruments are often made so that the focus can be moved along the depth to capture clear images of an entire region of interest. However, this dynamic focusing can make imaging slower and doesn’t work well for applications where the sample isn’t static. More