Computers Math

Viral respiratory infections are a significant public health concern. A study publishing January 18 in the open access journal PLOS Biology by Marta Galanti at Columbia University, New York, United States and colleagues used longitudinal cohort data to create an interactive, publicly-available website, The Virome of Manhattan Project: Virome Data Explorer to visualize cohort characteristics, infection events, and illness severity factors.
Viral respiratory infections may lead to severe outcomes. However, better understanding of host response, host genetic makeup, and bacterial coinfections is required to develop effective therapeutics. In order to contribute to epidemiological research on factors contributing to disease severity, the researchers conducted a longitudinal cohort study, surveilling respiratory viruses for 19 months between 2016-2018 in New York City. They analyzed over 800 nasopharyngeal samples with clinical data, including self-reported symptoms from 214 participants. From these data, researchers created the Virome Data Explorer, a publicly-available database. Users can access cohort data to visualize and analyze changes and patterns in infections, symptoms, and illness outcomes.
While the database shares important cohort data related to infections, symptoms, and gene activity, the project has several limitations. Adults over the age of 65 were excluded from the cohort, even though according to the authors, respiratory viruses may lead to “extremely serious complications, particularly in infants, elders, and immunocompromised hosts.” Ages of children under 10 were not stratified, obscuring symptom and illness information specific to infants, another high-risk demographic. Vaccination status, immunocompromised conditions, and medicine uptake during infection course were also not among the data collected from study participants, which may limit the applications of the Virome Data Explorer.
According to the authors, “We present a cohort study, consisting of hundreds of samples, that depicts the transcriptional changes driven by respiratory viral infection. We have compiled these data to build a publicly-available, user-friendly web-based resource where any user can compare, longitudinally over the course of 19 months, patterns of viral positivity, symptomatology and transcriptomic changes for the individuals enrolled.”
The authors add, “This is a resource paper aiming at characterizing the host response to common and often asymptomatic viral respiratory infections. We collected and made available a 2-year longitudinal dataset including molecular data and symptoms records for over 100 participants from different age groups in NYC.” More

Two insect-like robots, a mini-bug and a water strider, developed at Washington State University, are the smallest, lightest and fastest fully functional micro-robots ever known to be created.
Such miniature robots could someday be used for work in areas such as artificial pollination, search and rescue, environmental monitoring, micro-fabrication or robotic-assisted surgery. Reporting on their work in the proceedings of the IEEE Robotics and Automation Society’s International Conference on Intelligent Robots and Systems, the mini-bug weighs in at eight milligrams while the water strider weighs 55 milligrams. Both can move at about six millimeters a second.
“That is fast compared to other micro-robots at this scale although it still lags behind their biological relatives,” said Conor Trygstad, a PhD student in the School of Mechanical and Materials Engineering and lead author on the work. An ant typically weighs up to five milligrams and can move at almost a meter per second.
The key to the tiny robots is their tiny actuators that make the robots move. Trygstad used a new fabrication technique to miniaturize the actuator down to less than a milligram, the smallest ever known to have been made.
“The actuators are the smallest and fastest ever developed for micro-robotics,” said Néstor O. Pérez-Arancibia, Flaherty Associate Professor in Engineering at WSU’s School of Mechanical and Materials Engineering who led the project.
The actuator uses a material called a shape memory alloy that is able to change shapes when it’s heated. It is called ‘shape memory’ because it remembers and then returns to its original shape. Unlike a typical motor that would move a robot, these alloys don’t have any moving parts or spinning components.
“They’re very mechanically sound,” said Trygstad. “The development of the very lightweight actuator opens up new realms in micro-robotics.”
Shape memory alloys are not generally used for large-scale robotic movement because they are too slow. In the case of the WSU robots, however, the actuators are made of two tiny shape memory alloy wires that are 1/1000 of an inch in diameter. With a small amount of current, the wires can be heated up and cooled easily, allowing the robots to flap their fins or move their feet at up to 40 times per second. In preliminary tests, the actuator was also able to lift more than 150 times its own weight.

Compared to other technologies used to make robots move, the SMA technology also requires only a very small amount of electricity or heat to make them move.
“The SMA system requires a lot less sophisticated systems to power them,” said Trygstad.
Trygstad, an avid fly fisherman, has long observed water striders and would like to further study their movements. While the WSU water strider robot does a flat flapping motion to move itself, the natural insect does a more efficient rowing motion with its legs, which is one of the reasons that the real thing can move much faster.
The researchers would like to copy another insect and develop a water strider-type robot that can move across the top of the water surface as well as just under it. They are also working to use tiny batteries or catalytic combustion to make their robots fully autonomous and untethered from a power supply. More

Researchers at Kyushu University, in collaboration with Osaka University and the Fine Ceramics Center, have developed a framework that uses machine learning to speed up the discovery of materials for green energy technology. Using the new approach, the researchers identified and successfully synthesized two new candidate materials for use in solid oxide fuel cells — devices that can generate energy using fuels like hydrogen, which don’t emit carbon dioxide. Their findings, which were reported in the journal, Advanced Energy Materials, could also be used to accelerate the search for other innovative materials beyond the energy sector.
In response to a warming climate, researchers have been developing new ways to generate energy without using fossil fuels. “One path to carbon neutrality is by creating a hydrogen society. However, as well as optimizing how hydrogen is made, stored and transported, we also need to boost the power-generating efficiency of hydrogen fuel cells,” explains Professor Yoshihiro Yamazaki, of Kyushu University’s Department of Materials Science and Technology, Platform of Inter-/Transdisciplinary Energy Research (Q-PIT).
To generate an electric current, solid oxide fuel cells need to be able to efficiently conduct hydrogen ions (or protons) through a solid material, known as an electrolyte. Currently, research into new electrolyte materials has focused on oxides with very specific crystal arrangements of atoms, known as a perovskite structure.
“The first proton-conducting oxide discovered was in a perovskite structure, and new high-performing perovskites are continually being reported,” says Professor Yamazaki. “But we want to expand the discovery of solid electrolytes to non-perovskite oxides, which also have the capability of conducting protons very efficiently.”
However, discovering proton-conducting materials with alternative crystal structures via traditional “trial and error” methods has numerous limitations. For an electrolyte to gain the ability to conduct protons, small traces of another substance, known as a dopant, must be added to the base material. But with many promising base and dopant candidates — each with different atomic and electronic properties — finding the optimal combination that enhances proton conductivity becomes difficult and time-consuming.
Instead, the researchers calculated the properties of different oxides and dopants. They then used machine learning to analyze the data, identify the factors that impact the proton conductivity of a material, and predict potential combinations.
Guided by these factors, the researchers then synthesized two promising materials, each with unique crystal structures, and assessed how well they conducted protons. Remarkably, both materials demonstrated proton conductivity in just a single experiment.
One of the materials, the researchers highlighted, is the first-known proton conductor with a sillenite crystal structure. The other, which has a eulytite structure, has a high-speed proton conduction path that is distinct from the conduction paths seen in perovskites. Currently, the performance of these oxides as electrolytes is low, but with further exploration, the research team believes their conductivity can be improved.
“Our framework has the potential to greatly expand the search space for proton-conducting oxides, and therefore significantly accelerate advancements in solid oxide fuel cells. It’s a promising step forward to realizing a hydrogen society,” concludes Professor Yamazaki. “With minor modifications, this framework could also be adapted to other fields of materials science, and potentially accelerate the development of many innovative materials.” More

Quasicrystals are intermetallic materials that have garnered significant attention from researchers aiming to advance condensed matter physics understanding. Unlike normal crystals, in which atoms are arranged in an ordered repeating pattern, quasicrystals have non-repeating ordered patterns of atoms. Their unique structure leads to many exotic and interesting properties, which are particularly useful for practical applications in spintronics and magnetic refrigeration.
A unique quasicrystal variant, known as the Tsai-type icosahedral quasicrystal (iQC) and their cubic approximant crystals (ACs), display intriguing characteristics. These include long-range ferromagnetic (FM) and anti-ferromagnetic (AFM) orders, as well as unconventional quantum critical phenomenon, to name a few. Through precise compositional adjustments, these materials can also exhibit intriguing features like aging, memory, and rejuvenation, making them suitable for the development of next-generation magnetic storage devices. Despite their potential, however, the magnetic phase diagram of these materials remains largely unexplored.
To uncover more, a team of researchers, led by Professor Ryuji Tamura from the Department of Materials Science and Technology at Tokyo University of Science (TUS) in collaboration with researchers fromTohoku University recently conducted magnetization and powder neutron diffraction (PND) experiments on the non-Heisenberg Tsai-type 1/1 gold-gallium-terbium AC.
“For the first time, the phase diagrams of the non-Heisenberg Tsai-type AC have been unravelled. This will boost applied physics research on magnetic refrigeration and spintronics,” remarks Professor Tamura.
Through several experiments, the researchers developed the first comprehensive magnetic phase diagram of the non-Heisenberg Tsai-type AC, covering a broad range of electron-per-atom (e/a) ratios (a parameter crucial for understanding the fundamental nature of QCs). Additionally, measurements using the powder neutron diffraction (PND) revealed the presence of a noncoplanar whirling AFM order at an e/a ratio of 1.72 and a noncoplanar whirling FM order at the e/a ratio of 1.80. The team further elucidated the ferromagnetic and anti-ferromagnetic phase selection rule of magnetic interactions by analyzing the relative orientation of magnetic moments between nearest-neighbour and next-nearest neighbour sites.
Professor Tamura adds that their findings open up new doors for the future of condensed matter physics. “These results offer important insights into the intricate interplay between magnetic interactions in non-Heisenberg Tsai-type ACs. They lay the foundation for understanding the intriguing properties of not only non-Heisenberg ACs but also non-Heisenberg iQCs that are yet to be discovered.”
In summary, the present breakthrough propels condensed matter physics and quasicrystal research into uncharted territories, paving the way for advanced electronic devices and next-generation refrigeration technologies. More

In a groundbreaking study published on January 18, 2024, in Cancer Discovery, scientists at University of California San Diego School of Medicine leveraged a machine learning algorithm to tackle one of the biggest challenges facing cancer researchers: predicting when cancer will resist chemotherapy.
All cells, including cancer cells, rely on complex molecular machinery to replicate DNA as part of normal cell division. Most chemotherapies work by disrupting this DNA replication machinery in rapidly dividing tumor cells. While scientists recognize that a tumor’s genetic composition heavily influences its specific drug response, the vast multitude of mutations found within tumors has made prediction of drug resistance a challenging prospect.
The new algorithm overcomes this barrier by exploring how numerous genetic mutations collectively influence a tumor’s reaction to drugs that impede DNA replication. Specifically, they tested their model on cervical cancer tumors, successfully forecasting responses to cisplatin, one of the most common chemotherapy drugs. The model was able to identify tumors at most risk for treatment resistance and was also able to identify much of the underlying molecular machinery driving treatment resistance.
“Clinicians were previously aware of a few individual mutations that are associated with treatment resistance, but these isolated mutations tended to lack significant predictive value. The reason is that a much larger number of mutations can shape a tumor’s treatment response than previously appreciated,” Trey Ideker, PhD, professor in Department of Medicine at UC San Diego of Medicine, explained. “Artificial intelligence bridges that gap in our understanding, enabling us to analyze a complex array of thousands of mutations at once.”
One of the challenges in understanding how tumors respond to drugs is the inherent complexity of DNA replication — a mechanism targeted by numerous cancer drugs.
“Hundreds of proteins work together in complex arrangements to replicate DNA,” Ideker noted. “Mutations in any one part of this system can change how the entire tumor responds to chemotherapy.”
The researchers focused on the standard set of 718 genes commonly used in clinical genetic testing for cancer classification, using mutations within these genes as the initial input for their machine learning model. After training it with publicly accessible drug response data, the model pinpointed 41 molecular assemblies — groups of collaborating proteins — where genetic alterations influence drug efficacy.

“Cancer is a network-based disease driven by many interconnected components, but previous machine learning models for predicting treatment resistance don’t always reflect this,” said Ideker. “Rather than focusing on a single gene or protein, our model evaluates the broader biochemical networks vital for cancer survival.”
After training their model, the researchers put it to the test in cervical cancer, in which roughly 35% of tumors persist after treatment. The model was able to accurately identify tumors that were susceptible to therapy, which were associated with improved patient outcomes. The model also effectively pinpointed tumors likely to resist treatment.
Further still, beyond forecasting treatment responses, the model helped shed light on its decision-making process by identifying the protein assemblies driving treatment resistance in cervical cancer. The researchers emphasize that this aspect of the model — the ability to interpret its reasoning — is key to the model’s success and also for building trustworthy AI systems.
“Unraveling an AI model’s decision-making process is crucial, sometimes as important as the prediction itself,” said Ideker. “Our model’s transparency is one of its strengths, first because it builds trust in the model, and second because each of these molecular assemblies we’ve identified becomes a potential new target for chemotherapy. We’re optimistic that our model will have broad applications in not only enhancing current cancer treatment, but also in pioneering new ones.” More

When you try a new restaurant or book a hotel, do you consider the online reviews? Do you submit online reviews yourself? Do you pay attention if they are filtered and moderated? Does that impact your own online review submissions?
A research team comprising of Rensselaer Polytechnic Institute’s T. Ravichandran, Ph.D., professor in the Lally School of Management, and Jason Kuruzovich, Ph.D., associate professor in the Lally School of Management; and Lianlian Jiang, Ph.D., assistant professor in the Bauer College of Business at the University of Houston, examined these questions in recently published research. In a world where businesses thrive or die by online reviews, it is important to consider the implications of a platform’s review moderation policies, the transparency of those policies, and how that affects the reviews that are submitted.
“In 2010, Yelp debuted a video to help users understand how its review filter works and why it was necessary,” said Jiang. “Then, Yelp added a section to display filtered reviews. Previously, Yelp did not disclose information about its review filter. This change presented the perfect opportunity to examine the effect of policy transparency on submitted reviews.”
Ravichandran and team compared reviews of over 1,000 restaurants on Yelp to those same restaurants on TripAdvisor, whose practices remained unchanged and was not transparent about its review filter. They used a difference-in-difference (DID) approach. They found that the number of reviews submitted to Yelp decreased. Those that were submitted were increasingly negative and shorter in length compared to TripAdvisor. Also, the more positive a review, the shorter it was.
“Platforms are pressured to have content guidelines and take measures to prevent fraud and ensure that reviews are legitimate and helpful,” said Ravichandran. “However, most platforms are not transparent about their policies, leading consumers to suspect that reviews are manipulated to increase profit under the guise of filtering fraudulent content.”
Platforms use sophisticated software to flag and filter reviews. Once a review is flagged, it is filtered out and not displayed, and it is not factored into the overall rating for a business.
“Whether or not to be transparent about review filters is a critical decision for platforms with many considerations,” said Kuruzovich.
Users may put in less time and effort into their reviews if they suspect that they have a significant chance of being filtered, or they may do the opposite to make their reviews less likely to be filtered. Since most fake reviews are overly positive, users may assume that positive reviews are most likely to be filtered and act accordingly. However, with a transparent policy, those who submit fake reviews may be incentivized to change their ways.
“Review moderation transparency comes at a cost for platforms,” said Ravichandran. “Users reduce their contribution investment, or the amount of time and effort that they put into their reviews. This, in turn, affects the quality and characteristics of reviews. Although transparency helps to position a platform as unbiased toward advertisers, the resultant decrease in the number of reviews submitted impacts the platform’s usefulness to consumers.”
“This research informs businesses on best practices and consumer behavior in the digital world,” said Chanaka Edirisinghe, Ph.D., acting dean of the Lally School of Management. “Online reviews pose great opportunity for firms, but also raise complex questions. Platforms must earn the trust of users without sacrificing engagement.” More

An innovative study at Marshall University published in ArthroplastyToday explores the use of robotic-assisted joint replacement in revision knee scenarios, comparing the pre- and post-revision implant positions in a series of revision total knee arthroplasties (TKA) using a state-of-the-art robotic arm system.
In this retrospective study, the orthopaedic team at the Marshall University Joan C. Edwards School of Medicine and Marshall Health performed 25 revision knee replacements with a robotic assisted computer system. The procedure involved placing new implants at the end of the thighbone and top of the shinbone with the computer’s aid to ensure the knee was stable and balanced throughout the range of motion. Researchers then carefully compared the initial positions of the primary implants with the final planned positions of the robotic revision implants for each patient, assessing the differences in millimeters and degrees.
The analysis found that exceedingly small changes in implant position significantly influence the function of the knee replacement. Robotic assistance during revision surgery has the potential to measure these slight differences. In addition, the computer system can help the surgeon predict what size implant to use as well as help to balance the knee for stability.
“Robotic-assisted surgery has the potential to change the way surgeons think about revision knee replacement,” said Matthew Bullock, D.O., associate professor of orthopaedic surgery and co-author on the study. “The precision offered by robotic-assisted surgery not only enhances the surgical process but also holds promise for improved patient outcomes. Besides infection, knee replacements usually fail because they become loose from the bone or because they are unbalanced leading to pain and instability. When this happens patients can have difficulty with activities of daily living such as walking long distances or negotiating stairs.”
The study underscores the importance of aligning the prosthesis during revision surgery. The research also suggests potential advantages, including appropriately sized implants that can impact the ligament tension which is crucial for functional knee revisions.
“These findings open new doors in the realm of revision knee arthroplasty,” said Alexander Caughran, M.D., assistant professor of orthopaedic surgery and co-author on the study. “We continue to collect more data for future studies on patient outcomes after robotic revision knee replacement. We anticipate that further research and technological advancements in the realm of artificial intelligence will continue to shape the landscape of orthopaedic surgery.”
In addition to Bullock and Caughran, co-authors from Marshall University include Micah MacAskill, M.D., resident physician; Richard Peluso, M.D., resident physician; Jonathan Lash, M.D., resident physician; and Timothy Hewett, Ph.D., professor. More

Researchers at Columbia University have successfully synthesized the first 2D heavy fermion material. They introduce the new material, a layered intermetallic crystal composed of cerium, silicon, and iodine (CeSiI), in a research article published today in Nature.
Heavy fermion compounds are a class of materials with electrons that are up to 1000x heavier than usual. In these materials, electrons get tangled up with magnetic spins that slow them down and increase their effective mass. Such interactions are thought to play important roles in a number of enigmatic quantum phenomena, including superconductivity, the movement of electrical current with zero resistance.
Researchers have been exploring heavy fermions for decades, but in the form of bulky, 3D crystals. The new material synthesized by PhD student Victoria Posey in the lab of Columbia chemist Xavier Roy will allow researchers to drop a dimension.
“We’ve laid a new foundation to explore fundamental physics and to probe unique quantum phases,” said Posey.
One of the latest materials to come out of the Roy lab, CeSiI is a van der Waals crystal that can be peeled into layers that are just a few atoms thick. That makes it easier to manipulate and combine with other materials than a bulk crystal, in addition to possessing potential quantum properties that occur in 2D. “It’s amazing that Posey and the Roy lab could make a heavy fermion so small and thin,” said senior author Abhay Pasupathy, a physicist at Columbia and Brookhaven National Laboratory. “Just like we saw with the recent Nobel Prize to quantum dots, you can do many interesting things when you shrink dimensions.”
With its middle sheet of silicon sandwiched between magnetic cerium atoms, Posey and her colleagues suspected that CeSiI, first described in a paper in 1998, might have some interesting electronic properties. Its first stop (after Posey figured out how to prepare the extremely air-sensitive crystal for transport) was a Scanning Tunneling Microscope (STM) in Abhay Pasupathy’s physics lab at Columbia. With the STM, they observed a particular spectrum shape characteristic of heavy fermions. Posey then synthesized a non-magnetic equivalent to CeSiI and weighed the electrons of both materials via their heat capacities. CeSiI’s were heavier. “By comparing the two — one with magnetic spins and one without — we can confirm we’ve created a heavy fermion,” said Posey.
Samples then made their way across campus and the country for additional analyses, including to Pasupathy’s lab at Brookhaven National Laboratory for photoemission spectroscopy; to Philip Kim’s lab at Harvard for electron transport measurements; and to the National High Magnetic Field Laboratory in Florida to study its magnetic properties. Along the way, theorists Andrew Millis at Columbia and Angel Rubio at Max Planck helped explain the teams’ observations.
From here, Columbia’s researchers will do what they do best with 2D materials: stack, strain, poke, and prod them to see what unique quantum behaviors can be coaxed out of them. Pasupathy plans to add CeSiI to his arsenal of materials in the search for quantum criticality, the point where a material shifts from one unique phase to another. At the crossover, interesting phenomena like superconductivity may await.
“Manipulating CeSiI at the 2D limit will let us explore new pathways to achieve quantum criticality,” said Michael Ziebel, a postdoc in the Roy group and co-corresponding author, “and this can guide us in the design of new materials.”
Back in the chemistry department, Posey, who has perfected the air-free synthesis techniques needed, is systematically replacing the atoms in the crystal — for example, swapping silicon for other metals, like aluminum or gallium — to create related heavy fermions with their own unique properties to study. “We initially thought CeSiI was a one-off,” said Roy. “But this project has blossomed into a new kind of chemistry in my group.” More