Computers Math

Each year, at least 1.7 million adults in the United States develop sepsis, and approximately 350,000 will die from the serious blood infection that can trigger a life-threatening chain reaction throughout the entire body.
In a new study, published in the January 23, 2024 online edition of npj Digital Medicine, researchers at University of California San Diego School of Medicine utilized an artificial intelligence (AI) model in the emergency departments at UC San Diego Health in order to quickly identify patients at risk for sepsis infection.
The study found the AI algorithm, entitled COMPOSER, which was previously developed by the research team, resulted in a 17% reduction in mortality.
“Our COMPOSER model uses real-time data in order to predict sepsis before obvious clinical manifestations,” said study co-author Gabriel Wardi, MD, chief of the Division of Critical Care in the Department of Emergency Medicine at UC San Diego School of Medicine. “It works silently and safely behind the scenes, continuously surveilling every patient for signs of possible sepsis.”
Once a patient checks in at the emergency department, the algorithm begins to continuously monitor more than 150 different patient variables that could be linked to sepsis, such as lab results, vital signs, current medications, demographics and medical history.
Should a patient present with multiple variables, resulting in high risk for sepsis infection, the AI algorithm will notify nursing staff via the hospital’s electronic health record. The nursing team will then review with the physician and determine appropriate treatment plans.
“These advanced AI algorithms can detect patterns that are not initially obvious to the human eye,” said study co-author Shamim Nemati, PhD, associate professor of biomedical informatics and director of predictive analytics at UC San Diego School of Medicine. “The system can look at these risk factors and come up with a highly accurate prediction of sepsis. Conversely, if the risk patterns can be explained by other conditions with higher confidence, then no alerts will be sent.”
The study examined more than 6,000 patient admissions before and after COMPOSER was deployed in the emergency departments at UC San Diego Medical Center in Hillcrest and at Jacobs Medical Center in La Jolla.

It is the first study to report improvement in patient outcomes by utilizing an AI deep-learning model, which is a model that uses artificial neural networks as a check and balance in order to safely, and correctly, identify health concerns in patients. The model is able to identify complex and multiple risk factors, which are then reviewed by the health care team for confirmation.
“It is because of this AI model that our teams can provide life-saving therapy for patients quicker,” said Wardi, emergency medicine and critical care physician at UC San Diego Health.
COMPOSER was activated in December 2022 and is now also being utilized in many hospital in-patient units throughout UC San Diego Health. It will soon be activated at the health system’s newest location, UC San Diego Health East Campus.
UC San Diego Health, the region’s only academic medical system, is a pioneer in the field of AI health care, with a recent announcement of its inaugural chief health AI officer and opening of the Joan and Irwin Jacobs Center for Health Innovation at UC San Diego Health, which seeks to develop sophisticated and advanced solutions in health care.
Additionally, the health system recently launched a pilot in which Epic, a cloud-based electronic health record system, and Microsoft’s generative AI integration automatically drafts more compassionate message responses through ChatGPT, alleviating this additional step from doctors and caregivers so they can focus on patient care.
“Integration of AI technology in the electronic health record is helping to deliver on the promise of digital health, and UC San Diego Health has been a leader in this space to ensure AI-powered solutions support high reliability in patient safety and quality health care,” said study co-author Christopher Longhurst, MD, executive director of the Jacobs Center for Health Innovation, and chief medical officer and chief digital officer at UC San Diego Health.
Co-authors of this study include Aaron Boussina, Theodore Chan, Allison Donahue, Robert El-Kareh, Atul Malhotra, Robert Owens, Kimberly Quintero and Supreeth Shashikumar, all at UC San Diego. More

IntelliGenes, a first of its kind software created at Rutgers Health, combines artificial intelligence (AI) and machine-learning approaches to measure the significance of specific genomic biomarkers to help predict diseases in individuals, according to its developers.
A study published in Bioinformatics explains how IntelliGenes can be utilized by a wide range of users to analyze multigenomic and clinical data.
Zeeshan Ahmed, lead author of the study and a faculty member at Rutgers Institute for Health, Health Care Policy and Aging Research (IFH), said there currently are no AI or machine-learning tools available to investigate and interpret the complete human genome, especially for nonexperts. Ahmed and members of his Rutgers lab designed IntelliGenes so anyone can use the platform, including students or those without strong knowledge of bioinformatics techniques or access to high-performing computers.
The software combines conventional statistical methods with cutting-edge machine learning algorithms to produce personalized patient predictions and a visual representation of the biomarkers significant to disease prediction.
In another study, published in Scientific Reports, the researchers applied IntelliGenes to discover novel biomarkers and predict cardiovascular disease with high accuracy.
“There is huge potential in the convergence of datasets and the staggering developments in artificial intelligence and machine learning,” said Ahmed, who also is an assistant professor of medicine at Robert Wood Johnson Medical School.
“IntelliGenes can support personalized early detection of common and rare diseases in individuals, as well as open avenues for broader research ultimately leading to new interventions and treatments.”
Researchers tested the software using Amarel, the high-performance computing cluster managed by the Rutgers Office of Advanced Research Computing. The office provides a research computing and data environment for Rutgers researchers engaged in complex computational and data-intensive projects.
Coauthors of the study include William DeGroat, Dinesh Mendhe, Atharva Bhusari and Habiba Abdelhalim of IFH and Saman Zeeshan of Rutgers Cancer Institute of New Jersey. More

Music, often referred to as the universal language, is known to be a common component in all cultures. Then, could ‘musical instinct’ be something that is shared to some degree despite the extensive environmental differences amongst cultures?
On January 16, a KAIST research team led by Professor Hawoong Jung from the Department of Physics announced to have identified the principle by which musical instincts emerge from the human brain without special learning using an artificial neural network model.
Previously, many researchers have attempted to identify the similarities and differences between the music that exist in various different cultures, and tried to understand the origin of the universality. A paper published in Science in 2019 had revealed that music is produced in all ethnographically distinct cultures, and that similar forms of beats and tunes are used. Neuroscientist have also previously found out that a specific part of the human brain, namely the auditory cortex, is responsible for processing musical information.
Professor Jung’s team used an artificial neural network model to show that cognitive functions for music forms spontaneously as a result of processing auditory information received from nature, without being taught music. The research team utilized AudioSet, a large-scale collection of sound data provided by Google, and taught the artificial neural network to learn the various sounds. Interestingly, the research team discovered that certain neurons within the network model would respond selectively to music. In other words, they observed the spontaneous generation of neurons that reacted minimally to various other sounds like those of animals, nature, or machines, but showed high levels of response to various forms of music including both instrumental and vocal.
The neurons in the artificial neural network model showed similar reactive behaviours to those in the auditory cortex of a real brain. For example, artificial neurons responded less to the sound of music that was cropped into short intervals and were rearranged. This indicates that the spontaneously-generated music-selective neurons encode the temporal structure of music. This property was not limited to a specific genre of music, but emerged across 25 different genres including classic, pop, rock, jazz, and electronic.
Furthermore, suppressing the activity of the music-selective neurons was found to greatly impede the cognitive accuracy for other natural sounds. That is to say, the neural function that processes musical information helps process other sounds, and that ‘musical ability’ may be an instinct formed as a result of an evolutionary adaptation acquired to better process sounds from nature.
Professor Hawoong Jung, who advised the research, said, “The results of our study imply that evolutionary pressure has contributed to forming the universal basis for processing musical information in various cultures.” As for the significance of the research, he explained, “We look forward for this artificially built model with human-like musicality to become an original model for various applications including AI music generation, musical therapy, and for research in musical cognition.” He also commented on its limitations, adding, “This research however does not take into consideration the developmental process that follows the learning of music, and it must be noted that this is a study on the foundation of processing musical information in early development.”
This research, conducted by first author Dr. Gwangsu Kim of the KAIST Department of Physics (current affiliation: MIT Department of Brain and Cognitive Sciences) and Dr. Dong-Kyum Kim (current affiliation: IBS) was published in Nature Communications under the title, “Spontaneous emergence of rudimentary music detectors in deep neural networks.”
This research was supported by the National Research Foundation of Korea. More

Web 3.0 is an internet paradigm that is based around blockchain technology, an advanced database mechanism. Compared to Web 2.0, the current internet paradigm, Web 3.0 provides some added advantages, such as transparency and decentralized control structures. This is because Web 3.0 is designed to work over trustless and permissionless networks. Unfortunately, owing to certain technical difficulties, the implementation of Web 3.0 media streaming requires modifications to the service architecture of existing media streaming services. These difficulties include the degradation of user experience and Web 3.0’s incompatibility with certain operating softwares and browsers.
To address these issues, a team of researchers, led by Assistant Professor Gi Seok Park from Incheon National University undertook a novel project. Their findings were made available on 22 August 2023 and recently published in Volume 16, Issue 6 of the journal IEEE Transactions on Services Computing in November-December 2023. In this study, the researchers proposed an end-to-end system architecture that is specifically designed for Web 3.0 streaming services. They made use of Inter-Planetary file system (IPFS), a type of Web 3.0 peer-to-peer (P2P) data storage technology, to reduce service delays and improve user experience.
Web 3.0 services have also been implemented using the application programming interfaces of third-party service providers called IPFS pinning service. Unfortunately, they limit performance. Taking this into consideration, the team designed a system in which they were able to fully control the blockchain nodes by deploying their own IPFS nodes that ran directly on their system. They also implemented new protocols that cached content and scheduled chunks on their IPFS nodes, which enabled the nodes to collaborate with each other and quickly download data.
The researchers found that their proposed system was compatible with IPFS nodes and still ran on IPFS P2P networks. They also launched Retriever, a media non-fungible token (NFT) marketplace that was developed using Web 3.0 technologies. Retriever allowed users to watch video content, ensure data privacy, and was found to be compatible with multiple mobile devices. “Our service can allow creators to monetize their video content and even sell their video content if they wish to. This is because each content will now be managed as an NFT. More importantly, this entire process will be fair and transparent,” says Dr. Park, while speaking about Retriever.
When asked about the real-life implications of this study, Dr. Park explains, ” Our proposed service would establish digital trust from users. Moreover, thanks to blockchain technology, web services will no longer need to force trust on users in the future. All transactions will be made fairly through smart contracts and recorded transparently through the blockchain ledger.” More

Over the last 20 years, researchers in biology and medicine have created Boolean network models to simulate complex systems and find solutions, including new treatments for colorectal cancer.
“Boolean network models operate under the assumption that each gene in a regulatory network can have one of two states: on or off,” says Claus Kadelka, a systems biologist and associate professor of mathematics at Iowa State University.
Kadelka and undergraduate student researchers recently published a study that disentangles the common design principles in these mathematical models for gene regulatory networks. He says showing what features have evolved over millions of years can “guide the process of accurate model building” for mathematicians, computer scientists and synthetic biologists.
“Evolution has shaped the networks that control the decision-making of our cells in very specific, optimized ways. Synthetic biologists who try to engineer circuits that perform a particular function can learn from this evolution-inspired design,” says Kadelka.
Gene regulatory networks determine what happens and where it happens in an organism. For example, they prompt cells in your stomach lining — but not in your eyes — to produce hydrochloric acid, even though all the cells in your body contain the same DNA.
On a piece of paper, Kadelka draws a simple, hypothetical gene regulatory network. Gene A produces a protein that turns on gene B, which turns on gene C, which turns off gene A. This negative feedback loop is the same concept as an air conditioner that shuts off once a room reaches a certain temperature.
But gene regulatory networks can be large and complex. One of the Boolean models in the researchers’ dataset involves more than 300 genes. And along with negative feedback loops, gene regulatory networks may contain positive feedback loops and feed-forward loops, which reinforce or delay responses. Redundant genes that perform the same function are also common.

Among these and other design principles highlighted in the new paper, Kadelka says one of the most abundant is “canalization.” It refers to a hierarchy or importance ordering among genes in a network.
Accessible data, bolstered with undergraduate research
Kadelka emphasizes that the project would have been difficult to complete without the First-Year Mentor Program, which matches students in the Iowa State Honors Program with research opportunities across campus.
Undergraduate students helped Kadelka develop an algorithm to scan 30 million biomedical journal articles and filter those most likely to include Boolean biological network models. After reviewing 2,000 articles one by one, the researchers identified around 160 models with close to 7,000 regulated genes.
Addison Schmidt, now a senior in computer science, is one of the paper’s co-authors. When he worked on the project as a freshman in 2021, he created an online database for the project.
“A major benefit of the research is that it collects and standardizes Boolean gene regulatory networks from many sources and presents them, along with a set of analysis tools, through a centralized web interface. This expands the accessibility of the data, and the web interface makes the analysis tools useable without a programming background,” says Schmidt.

Kadelka says systems biologists have used the database for their research and expressed gratitude for the resource. He plans to maintain and update the website and investigate why evolution selects for certain design principles in gene regulatory networks.
As for Schmidt, he says working on the project as a freshman helped him expand his expertise with the Python programming language and become more comfortable applying his skills to research.
“This project also motivated me to pursue other research at Iowa State where I developed other tools and, coincidentally, another website to present them,” says Schmidt.
He adds that he appreciated Kadelka’s mentorship and hopes the First-Year Mentor Program will continue to foster opportunities for undergraduate research at Iowa State. More

Scientists and engineers have been pushing for the past decade to leverage an elusive ferroelectric material called hafnium oxide, or hafnia, to usher in the next generation of computing memory. A team of researchers including the University of Rochester’s Sobhit Singh published a Proceedings of the National Academy of Sciences study outlining progress toward making bulk ferroelectric and antiferroelectric hafnia available for use in a variety of applications.
In a specific crystal phase, hafnia exhibits ferroelectric properties — that is, electric polarization that can be changed in one direction or another by applying an external electric field. This feature can be harnessed in data storage technology. When used in computing, ferroelectric memory has the benefit of non-volatility, meaning it retains its values even when powered off, one of several advantages over most types of memory used today.
“Hafnia is a very exciting material because of its practical applications in computer technology, especially for data storage,” says Singh, an assistant professor in the Department of Mechanical Engineering. “Currently, to store data we use magnetic forms of memory that are slow, require a lot of energy to operate, and are not very efficient. Ferroelectric forms of memory are robust, ultra-fast, cheaper to produce, and more energy-efficient.”
But Singh, who performs theoretical calculations to predict material properties at the quantum level, says that bulk hafnia is not ferroelectric at its ground state. Until recently, scientists could only get hafnia to its metastable ferroelectric state when straining it as a thin, two-dimensional film of nanometer thickness.
In 2021, Singh was part of a team of scientists at Rutgers University that got hafnia to stay at its metastable ferroelectric state by alloying the material with yttrium and rapidly cooling it. Yet this approach had some drawbacks. “It required a lot of yttrium to get to that desired metastable phase,” he says. “So, while we achieved what we were going for, at the same time we were hampering a lot of the material’s key features because we were introducing a lot of impurities and disorder in the crystal. The question became, how can we get to that metastable state with as little yttrium as possible to improve the resulting material’s properties?”
In the new study, Singh calculated that by applying significant pressure, one could stabilize bulk hafnia in its metastable ferroelectric and antiferroelectric forms — both of which are intriguing for practical applications in next-generation data and energy storage technologies. A team led by Professor Janice Musfeldt at the University of Tennessee, Knoxville, carried out the high-pressure experiments and demonstrated that, at the predicted pressure, the material converted into the metastable phase and remained there even when pressure was removed.
“This is as an excellent example of experimental-theoretical collaboration,” says Musfeldt.
The new approach required only about half as much yttrium as a stabilizer, thereby considerably improving the quality and purity of the grown hafnia crystals. Now, Singh says that he and the other scientists will push to use less and less yttrium until they figure out a way for producing ferroelectric hafnia in bulk for widespread use.
And as hafnia continues to draw increasing attention due to its intriguing ferroelectricity, Singh is organizing an invited focus session on the material at the upcoming American Physical Society’s March Meeting 2024. More

Researchers at Linköping University, Sweden, have developed a new, more environmentally friendly way to create conductive inks for use in organic electronics such as solar cells, artificial neurons, and soft sensors. The findings, published in the journal Nature Communications, pave the way for future sustainable technology.
Organic electronics are on the rise as a complement and, in some cases, a replacement to traditional silicon-based electronics. Thanks to simple manufacturing, high flexibility, and low weight combined with the electrical properties typically associated with traditional semiconductors, it can be useful for applications such as digital displays, energy storage, solar cells, sensors, and soft implants.
Organic electronics are built from semiconducting plastics, known as conjugated polymers. However, processing conjugated polymers often requires environmentally hazardous, toxic, and flammable solvents. This is a major obstacle to the wide commercial and sustainable use of organic electronics.
Now, researchers at Linköping University have developed a new sustainable method for processing these polymers from water. In addition to being more sustainable, the new inks are also highly conductive.
“Our research introduces a new approach to processing conjugated polymers using benign solvents such as water. With this method, called ground-state electron transfer, we not only get around the problem of using hazardous chemicals, but we can also demonstrate improvements in material properties and device performance,” says Simone Fabiano, senior associate professor at the Laboratory of Organic Electronics.
When researchers tested the new conductive ink as a transport layer in organic solar cells, they found that both stability and efficiency were higher than with traditional materials. They also tested the ink to create electrochemical transistors and artificial neurons, demonstrating operating frequencies similar to biological neurons.
“I believe that these results can have a transformative impact on the field of organic electronics. By enabling the processing of organic semiconductors from green and sustainable solvents like water, we can mass-produce electronic devices with minimal impact on the environment,” says Simone Fabiano, a Wallenberg Academy Fellow. More

A research team led by Lawrence Berkeley National Laboratory (Berkeley Lab) has developed “supramolecular ink,” a new technology for use in OLED (organic light-emitting diode) displays or other electronic devices. Made of inexpensive, Earth-abundant elements instead of costly scarce metals, supramolecular ink could enable more affordable and environmentally sustainable flat-panel screens and electronic devices.
“By replacing precious metals with Earth-abundant materials, our supramolecular ink technology could be a game changer for the OLED display industry,” said principal investigator Peidong Yang, a faculty senior scientist in Berkeley Lab’s Materials Sciences Division and professor of chemistry and materials science and engineering at UC Berkeley. “What’s even more exciting is that the technology could also extend its reach to organic printable films for the fabrication of wearable devices as well as luminescent art and sculpture,” he added.
If you have a relatively new smartphone or flat panel TV, there’s a good chance it features an OLED screen. OLEDs are rapidly expanding in the display market because they are lighter, thinner, use less energy, and have better picture quality than other flat-panel technologies. That’s because OLEDs contain tiny organic molecules that emit light directly, eliminating the need for the extra backlight layer that is found in a liquid crystal display (LCD). However, OLEDs can include rare, expensive metals like iridium.
But with the new material — which the Berkeley Lab team recently described in a new study published in the journal Science — electronics display manufacturers could potentially adopt a cheaper fabrication process that also requires far less energy than conventional methods.
The new material consists of powders containing hafnium (Hf) and zirconium (Zr) that can be mixed in solution at low temperatures — from room temperature up to around 176 degrees Fahrenheit (80 degrees Celsius) — to form a semiconductor “ink.”
Tiny molecular “building block” structures within the ink self-assemble in solution — a process that the researchers call supramolecular assembly. “Our approach can be compared to building with LEGO blocks,” said Cheng Zhu, the co-first author on the paper and a Ph.D. candidate in materials science and engineering at UC Berkeley. These supramolecular structures enable the material to achieve stable and high-purity synthesis at low temperatures, explained Zhu. He developed the material while working as a research affiliate in Berkeley Lab’s Materials Sciences Division and graduate student researcher in the Peidong Yang group at Berkeley Lab and UC Berkeley.
Spectroscopy experiments at UC Berkeley revealed that the supramolecular ink composites are highly efficient emitters of blue and green light — two signifiers of the material’s potential application as an energy-efficient OLED emitter in electronic displays and 3D printing.

Subsequent optical experiments revealed that the blue- and green-emitting supramolecular ink compounds exhibit what scientists call near-unity quantum efficiency. “This demonstrates their exceptional ability to convert nearly all absorbed light into visible light during the emission process,” Zhu explained.
To demonstrate the material’s color tunability and luminescence as an OLED emitter, the researchers fabricated a thin-film display prototype from the composite ink. In an exciting result, they found that the material is suitable for programmable electronic displays.
“The alphabet movie serves as a compelling example that illustrates the application of emissive thin films like supramolecular ink in the creation of fast-switching displays,” said Zhu.
Additional experiments at UC Berkeley showed that the supramolecular ink is also compatible with 3D printing technologies such as for the design of decorative OLED lighting.
Zhu added that manufacturers could also use the supramolecular ink to fabricate wearable devices or high-tech clothing that illuminates for safety in low-light conditions, or wearable devices that display information through the supramolecular light-emitting structures.
The supramolecular ink is another demonstration from the Peidong Yang lab of new sustainable materials that could enable cost-effective and energy-efficient semiconductor manufacturing. Last year, Yang and his team reported a new “multielement ink” — the first “high-entropy” semiconductor that can be processed at low temperature or room temperature.

With their demonstrated stability and shelf life, the supramolecular ink compounds could also help in the commercial advancement of ionic halide perovskites, a thin-film solar material that the display industry has been eyeing for decades. With their low-temperature synthesis in solution, ionic halide perovskites could potentially enable cheaper manufacturing processes for the manufacturing of displays. But high-performance halide perovskites contain the element lead, which is concerning for the environment and public health. In contrast, the new supramolecular ink — which belongs to the ionic halide perovskite family — offers a lead-free formulation without compromising performance.
Now that they have successfully demonstrated the supramolecular ink’s potential in OLED thin films and 3D-printable electronics, the researchers are now exploring the material’s electroluminescent potential. “This involves a focused and specialized investigation into how well our materials can emit light using electrical excitation,” Zhu said. “This step is essential to understanding our material’s full potential for creating efficient light-emitting devices.”
Other authors on the study include Jianbo Jin (co-first author), Zhen Wang, Zhenpeng Xu, Maria C. Folgueras, Yuxin Jiang, Can B. Uzundal, Han K.D. Le, Feng Wang, and Xiaoyu (Rayne) Zheng.
This work was supported by the Department of Energy’s Office of Science. More