Computers Math

In humans, adenoviruses can infect the cells of the respiratory tract, while herpes viruses can infect those of the skin and nervous system. In most cases, this does not lead to the production of new virus particles, as the viruses are suppressed by the immune system. However, adenoviruses and herpes viruses can cause persistent infections that the immune system is unable to completely suppress and that produce viral particles for years. These same viruses can also cause sudden, violent infections where affected cells release large amounts of viruses, such that the infection spreads rapidly. This can lead to serious acute diseases of the lungs or nervous system.
Automatic detection of virus-infected cells
The research group of Urs Greber, Professor at the Department of Molecular Life Sciences at the University of Zurich (UZH), has now shown for the first time that a machine-learning algorithm can recognize the cells infected with herpes or adenoviruses based solely on the fluorescence of the cell nucleus. “Our method not only reliably identifies virus-infected cells, but also accurately detects virulent infections in advance,” Greber says. The study authors believe that their development has many applications — including predicting how human cells react to other viruses or microorganisms. “The method opens up new ways to better understand infections and to discover new active agents against pathogens such as viruses or bacteria,” Greber adds.
The analysis method is based on combining fluorescence microscopy in living cells with deep-learning processes. The herpes and adenoviruses formed inside an infected cell change the organization of the nucleus, and these changes can be observed under a microscope. The group developed a deep-learning algorithm — an artificial neural network — to automatically detect these changes. The network is trained with a large set of microscopy images through which it learns to identify patterns that are characteristic of infected or uninfected cells. “After training and validation are complete, the neural network automatically detects virus-infected cells,” explains Greber.
Reliably predicting severe acute infections
The research team has also demonstrated that the algorithm is capable of identifying acute and severe infections with 95 percent accuracy and up to 24 hours in advance. Images of living cells from lytic infections, in which the virus particles multiply rapidly and the cells dissolve, as well as images of persistent infections, in which viruses are produced continuously but only in small quantities, served as training material. Despite the great precision of the method, it is not yet clear which features of infected cell nuclei are recognized by the artificial neural network to distinguish the two phases of infection. However, even without this knowledge, the researchers are now able to study the biology of infected cells in greater detail.
The group has already discovered some differences: The internal pressure of the nucleus is greater during virulent infections than during persistent phases. Furthermore, in a cell with lytic infection, viral proteins accumulate more rapidly in the nucleus. “We suspect that distinct cellular processes determine whether or not a cell disintegrates after it is infected. We can now investigate these and other questions,” says Greber.
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New technology could help cities around the world improve people’s lives while saving billions of dollars. The free, open-source software developed by the Stanford Natural Capital Project creates maps to visualize the links between nature and human wellbeing. City planners and developers can use the software to visualize where investments in nature, such as parks and marshlands, can maximize benefits to people, like protection from flooding and improved health.
“This software helps design cities that are better for both people and nature,” said Anne Guerry, Chief Strategy Officer and Lead Scientist at the Natural Capital Project. “Urban nature is a multitasking benefactor — the trees on your street can lower temperatures so your apartment is cooler on hot summer days. At the same time, they’re soaking up the carbon emissions that cause climate change, creating a free, accessible place to stay healthy through physical activity and just making your city a more pleasant place to be.”
By 2050, experts expect over 70 percent of the world’s people to live in cities — in the United States, more than 80 percent already do. As the global community becomes more urban, developers and city planners are increasingly interested in green infrastructure, such as tree-lined paths and community gardens, that provide a stream of benefits to people. But if planners don’t have detailed information about where a path might encourage the most people to exercise or how a community garden might buffer a neighborhood from flood risk while helping people recharge mentally, they can’t strategically invest in nature.
“We’re answering three crucial questions with this software: where in a city is nature providing what benefits to people, how much of each benefit is it providing and who is receiving those benefits?” said Perrine Hamel, lead author on a new paper about the software published in Urban Sustainability and Livable Cities Program Lead at the Stanford Natural Capital Project at the time of research.
The software, called Urban InVEST, is the first of its kind for cities and allows for the combination of environmental data, like temperature patterns, with social demographics and economic data, like income levels. Users can input their city’s datasets into the software or access a diversity of open global data sources, from NASA satellites to local weather stations. The new software joins the Natural Capital Project’s existing InVEST software suite, a set of tools designed for experts to map and model the benefits that nature provides to people.
To test Urban InVEST, the team applied the software in multiple cities around the world: Paris, France; Lausanne, Switzerland; Shenzhen and Guangzhou, China; and several U.S. cities, including San Francisco and Minneapolis. In many cases, they worked with local partners to understand priority questions — in Paris, candidates in a municipal election were campaigning on the need for urban greenery, while in Minneapolis, planners were deciding how to repurpose underused golf course land. More

Geologic activity on Earth appears to follow a 27.5-million-year cycle, giving the planet a “pulse,” according to a new study published in the journal Geoscience Frontiers.
“Many geologists believe that geological events are random over time. But our study provides statistical evidence for a common cycle, suggesting that these geologic events are correlated and not random,” said Michael Rampino, a geologist and professor in New York University’s Department of Biology, as well as the study’s lead author.
Over the past five decades, researchers have proposed cycles of major geological events — including volcanic activity and mass extinctions on land and sea — ranging from roughly 26 to 36 million years. But early work on these correlations in the geological record was hampered by limitations in the age-dating of geologic events, which prevented scientists from conducting quantitative investigations.
However, there have been significant improvements in radio-isotopic dating techniques and changes in the geologic timescale, leading to new data on the timing of past events. Using the latest age-dating data available, Rampino and his colleagues compiled updated records of major geological events over the last 260 million years and conducted new analyses.
The team analyzed the ages of 89 well-dated major geological events of the last 260 million years. These events include marine and land extinctions, major volcanic outpourings of lava called flood-basalt eruptions, events when oceans were depleted of oxygen, sea-level fluctuations, and changes or reorganization in the Earth’s tectonic plates.
They found that these global geologic events are generally clustered at 10 different timepoints over the 260 million years, grouped in peaks or pulses of roughly 27.5 million years apart. The most recent cluster of geological events was approximately 7 million years ago, suggesting that the next pulse of major geological activity is more than 20 million years in the future.
The researchers posit that these pulses may be a function of cycles of activity in the Earth’s interior — geophysical processes related to the dynamics of plate tectonics and climate. However, similar cycles in the Earth’s orbit in space might also be pacing these events.
“Whatever the origins of these cyclical episodes, our findings support the case for a largely periodic, coordinated, and intermittently catastrophic geologic record, which is a departure from the views held by many geologists,” explained Rampino.
In addition to Rampino, study authors include Yuhong Zhu of NYU’s Center for Data Science and Ken Caldeira of the Carnegie Institution for Science.
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So far, quantum computers have been one-of-a-kind devices that fill entire laboratories. Now, physicists at the University of Innsbruck have built a prototype of an ion trap quantum computer that can be used in industry. It fits into two 19-inch server racks like those found in data centers throughout the world.
Over the past three decades, fundamental groundwork for building quantum computers has been pioneered at the University of Innsbruck, Austria. As part of the EU Flagship Quantum Technologies, researchers at the Department of Experimental Physics in Innsbruck have now built a demonstrator for a compact ion trap quantum computer. “Our quantum computing experiments usually fill 30- to 50-square-meter laboratories,” says Thomas Monz of the University of Innsbruck. “We were now looking to fit the technologies developed here in Innsbruck into the smallest possible space while meeting standards commonly used in industry.” The new device aims to show that quantum computers will soon be ready for use in data centers. “We were able to show that compactness does not have to come at the expense of functionality,” adds Christian Marciniak from the Innsbruck team.
The individual building blocks of the world’s first compact quantum computer had to be significantly reduced in size. For example, the centerpiece of the quantum computer, the ion trap installed in a vacuum chamber, takes up only a fraction of the space previously required. It was provided to the researchers by Alpine Quantum Technologies (AQT), a spin-off of the University of Innsbruck and the Austrian Academy of Sciences which aims to build a commercial quantum computer. Other components were contributed by the Fraunhofer Institute for Applied Optics and Precision Engineering in Jena and laser specialist TOPTICA Photonics in Munich, Germany.
Up to 50 quantum bits
The compact quantum computer can be operated autonomously and will soon be programmable online. A particular challenge was to ensure the stability of the quantum computer. Quantum devices are very sensitive and in the laboratory they are protected from external disturbances with the help of elaborate measures. Amazingly, the Innsbruck team succeeded in applying this quality standard to the compact device as well, thus ensuring safe and uninterrupted operation.
In addition to stability, a decisive factor for the industrial use of a quantum computer is the number of available quantum bits. Thus, in its recent funding campaign, the German government has set the goal of initially building demonstration quantum computers that have 24 fully functional qubits. The Innsbruck quantum physicists have already achieved this goal. They were able to individually control and successfully entangle up to 24 ions with the new device. “By next year, we want to be able to provide a device with up to 50 individually controllable quantum bits,” says Thomas Monz, already looking to the future.
The project is financially supported by the Austrian Science Fund FWF, the Research Funding Agency FFG, the European Union, and the Federation of Austrian Industries Tyrol, among others.
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To the human eye, most stationary objects appear to be just that — still, and completely at rest. Yet if we were handed a quantum lens, allowing us to see objects at the scale of individual atoms, what was an apple sitting idly on our desk would appear as a teeming collection of vibrating particles, very much in motion.
In the last few decades, physicists have found ways to super-cool objects so that their atoms are at a near standstill, or in their “motional ground state.” To date, physicists have wrestled small objects such as clouds of millions of atoms, or nanogram-scale objects, into such pure quantum states.
Now for the first time, scientists at MIT and elsewhere have cooled a large, human-scale object to close to its motional ground state. The object isn’t tangible in the sense of being situated at one location, but is the combined motion of four separate objects, each weighing about 40 kilograms. The “object” that the researchers cooled has an estimated mass of about 10 kilograms, and comprises about 1×1026, or nearly 1 octillion, atoms.
The researchers took advantage of the ability of the Laser Interfrometer Gravitational-wave Observatory (LIGO) to measure the motion of the masses with extreme precision and super-cool the collective motion of the masses to 77 nanokelvins, just shy of the object’s predicted ground state of 10 nanokelvins.
Their results, appearing today in Science, represent the largest object to be cooled to close to its motional ground state. The scientists say they now have a chance to observe the effect of gravity on a massive quantum object.
“Nobody has ever observed how gravity acts on massive quantum states,” says Vivishek Sudhir, assistant professor of mechanical engineering at MIT, who directed the project. “We’ve demonstrated how to prepare kilogram-scale objects in quantum states. This finally opens the door to an experimental study of how gravity might affect large quantum objects, something hitherto only dreamed of.”
The study’s authors are members of the LIGO Laboratory, and include lead author and graduate student Chris Whittle, postdoc Evan Hall, research scientist Sheila Dwyer, Dean of the School of Science and the Curtis and Kathleen Marble Professor of Astrophysics Nergis Mavalvala, and assistant professor of mechanical engineering Vivishek Sudhir. More

Using neural networks, researchers from the University of Copenhagen have developed a new method to search the human genome for beneficial mutations from Neanderthals and other archaic humans. These humans are known to have interbred with modern humans, but the overall fate of the genetic material inherited from them is still largely unknown. Among others, the researchers found previously unreported mutations involved in core pathways in metabolism, blood-related diseases and immunity.
Thousands of years ago, archaic humans such as Neanderthals and Denisovans went extinct. But before that, they interbred with the ancestors of present-day humans, who still to this day carry genetic mutations from the extinct species.
Over 40 percent of the Neanderthal genome is thought to have survived in different present-day humans of non-African descent, but spread out so that any individual genome is only composed of up to two percent Neanderthal material. Some human populations also carry genetic material from Denisovans — a mysterious group of archaic humans that may have lived in Eastern Eurasia and Oceania thousands of years ago.
The introduction of beneficial genetic material into our gene pool, a process known as adaptive introgression, often happened because it was advantageous to humans after they expanded across the globe. To name a few examples, scientists believe some of the mutations affected skin development and metabolism. But many mutations are yet still undiscovered.
Now, researchers from GLOBE Institute at the University of Copenhagen have developed a new method using deep learning techniques to search the human genome for undiscovered mutations.
“We developed a deep learning method called ‘genomatnn’ that jointly models introgression, which is the transfer of genetic information between species, and natural selection. The model was developed in order to identify regions in the human genome where this introgression could have happened,” says Associate Professor Fernando Racimo, GLOBE Institute, corresponding author of the new study. More

A team of scientists headed by SANKEN (The Institute of Scientific and Industrial Research) at Osaka University demonstrated that single virus particles passing through a nanopore could be accurately identified using machine learning. The test platform they created was so sensitive that the coronaviruses responsible for the common cold, SARS, MERS, and COVID could be distinguished from each other. This work may lead to rapid, portable, and accurate screening tests for COVID and other viral diseases.
The global coronavirus pandemic has revealed the crucial need for rapid pathogen screening. However, the current gold-standard for detecting RNA viruses — including SARS-CoV-2, the virus that causes COVID — is reverse transcription-polymerase chain reaction (RT-PCR) testing. While accurate, this method is relatively slow, which hinders the timely interventions required to control an outbreak.
Now, scientists led by Osaka University have developed an intelligent nanopore system that can be used for the detection of SARS-CoV-2 virus particles. Using machine-learning methods, the platform can accurately discriminate between similarly sized coronaviruses responsible for different respiratory diseases. “Our innovative technology has high sensitivity and can even electrically identify single virus particles,” first author Professor Masateru Taniguchi says. Using this platform, the researchers were able to achieve a sensitivity of 90% and a specificity of 96% for SARS-CoV-2 detection in just five minutes using clinical saliva samples.
To fabricate the device, nanopores just 300 nanometers in diameter were bored into a silicon nitride membrane. When a virus was pulled through a nanopore by the electrophoretic force, the opening became partially blocked. This temporarily decreased the ionic flow inside the nanopore, which was detected as a change in the electrical current. The current as a function of time provided information on the volume, structure, and surface charge of the target being analyzed. However, to interpret the subtle signals, which could be as small as a few nanoamps, machine learning was needed. The team used 40 PCR-positive and 40 PCR-negative saliva samples to train the algorithm.
“We expect that this research will enable rapid point-of-care and screening tests for SARS-CoV-2 without the need for RNA extraction,” Professor Masateru Taniguchi explains. “A user-friendly and non-invasive method such as this is more amenable to immediate diagnosis in hospitals and screening in places where large crowds are gathered.” The complete test platform consists of machine learning software on a server, a portable high-precision current measuring instrument, and cost-effective semiconducting nanopore modules. By using a machine-learning method, the researchers expect that this system can be adapted for use in the detection of emerging infectious diseases in the future. The team hopes that this approach will revolutionize public health and disease control.
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Electrons are ubiquitous among atoms, subatomic tokens of energy that can independently change how a system behaves — but they also can change each other. An international research collaboration found that collectively measuring electrons revealed unique and unanticipated findings. The researchers published their results on May 17 in Physical Review Letters.
“It is not feasible to obtain the solution just by tracing the behavior of each individual electron,” said paper author Myung Joon Han, professor of physics at KAIST. “Instead, one should describe or track all the entangled electrons at once. This requires a clever way of treating this entanglement.”
Professor Han and the researchers used a recently developed “many-particle” theory to account for the entangled nature of electrons in solids, which approximates how electrons locally interact with one another to predict their global activity.
Through this approach, the researchers examined systems with two orbitals — the space in which electrons can inhabit. They found that the electrons locked into parallel arrangements within atom sites in solids. This phenomenon, known as Hund’s coupling, results in a Hund’s metal. This metallic phase, which can give rise to such properties as superconductivity, was thought only to exist in three-orbital systems.
“Our finding overturns a conventional viewpoint that at least three orbitals are needed for Hund’s metallicity to emerge,” Professor Han said, noting that two-orbital systems have not been a focus of attention for many physicists. “In addition to this finding of a Hund’s metal, we identified various metallic regimes that can naturally occur in generic, correlated electron materials.”
The researchers found four different correlated metals. One stems from the proximity to a Mott insulator, a state of a solid material that should be conductive but actually prevents conduction due to how the electrons interact. The other three metals form as electrons align their magnetic moments — or phases of producing a magnetic field — at various distances from the Mott insulator. Beyond identifying the metal phases, the researchers also suggested classification criteria to define each metal phase in other systems.
“This research will help scientists better characterize and understand the deeper nature of so-called ‘strongly correlated materials,’ in which the standard theory of solids breaks down due to the presence of strong Coulomb interactions between electrons,” Professor Han said, referring to the force with which the electrons attract or repel each other. These interactions are not typically present in solid materials but appear in materials with metallic phases.
The revelation of metals in two-orbital systems and the ability to determine whole system electron behavior could lead to even more discoveries, according to Professor Han.
“This will ultimately enable us to manipulate and control a variety of electron correlation phenomena,” Professor Han said.
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