Computers Math

Physician-scientists in the Smidt Heart Institute at Cedars-Sinai have created an artificial intelligence (AI) tool that can effectively identify and distinguish between two life-threatening heart conditions that are often easy to miss: hypertrophic cardiomyopathy and cardiac amyloidosis. The new findings were published in JAMA Cardiology.
“These two heart conditions are challenging for even expert cardiologists to accurately identify, and so patients often go on for years to decades before receiving a correct diagnosis,” said David Ouyang, MD, a cardiologist in the Smidt Heart Institute and senior author of the study. “Our AI algorithm can pinpoint disease patterns that can’t be seen by the naked eye, and then use these patterns to predict the right diagnosis.”
The two-step, novel algorithm was used on over 34,000 cardiac ultrasound videos from Cedars-Sinai and Stanford Healthcare’s echocardiography laboratories. When applied to these clinical images, the algorithm identified specific features — related to the thickness of heart walls and the size of heart chambers — to efficiently flag certain patients as suspicious for having the potentially unrecognized cardiac diseases.
“The algorithm identified high-risk patients with more accuracy than the well-trained eye of a clinical expert,” said Ouyang. “This is because the algorithm picks up subtle cues on ultrasound videos that distinguish between heart conditions that can often look very similar to more benign conditions, as well as to each other, on initial review.”
Without comprehensive testing, cardiologists find it challenging to distinguish between similar appearing diseases and changes in heart shape and size that can sometimes be thought of as a part of normal aging. This algorithm accurately distinguishes not only abnormal from normal, but also between which underlying potentially life-threatening cardiac conditions may be present — with warning signals that are now detectable well before the disease clinically progresses to the point where it can impact health outcomes. Getting an earlier diagnosis enables patients to begin effective treatments sooner, prevent adverse clinical events, and improve their quality of life.
Cardiac amyloidosis, often called “stiff heart syndrome,” is a disorder caused by deposits of an abnormal protein (amyloid) in the heart tissue. As amyloid builds up, it takes the place of healthy heart muscle, making it difficult for the heart to work properly. Cardiac amyloidosis often goes undetected because patients might not have any symptoms, or they might experience symptoms only sporadically. More

For the HIV-1 virus, a double layer of fatty molecules called lipids not only serves as its container, but also plays a key role in the virus’s replication and infectivity. Scientists have used supercomputers to complete the first-ever biologically authentic computer model of the HIV-1 virus liposome, its complete spherical lipid bilayer.
What’s more, this study comes fresh off the heels of a new atomistic model of the HIV-1 capsid, which contains its genetic material. The scientists are hopeful this basic research into viral envelopes can help efforts to develop new HIV-1 therapeutics, as well as laying a foundation for study of other enveloped viruses such as the novel coronavirus, SARS-CoV-2.
“This work represents an investigation of the HIV-1 liposome at full-scale, and with an unprecedented level of chemical complexity,” said Alex Bryer, a PhD student in the Perilla Laboratory, Department of Chemistry and Biochemistry, University of Delaware. Bryer is the lead author of the liposome-modeling research, published January 2022 in the journal PLOS Computational Biology.
The science team developed a complex chemical model of the HIV-1 liposome that revealed key characteristics of the liposome’s asymmetry. Most such models assume a geometrically uniform structure and don’t capture the asymmetry inherent in such biological containers.
Lipid Flip-Flop
Bryer and his co-authors investigated a mechanism that’s known colloquially as “lipid flip-flop,” which is when lipids in one of the leaflets of the bilayer are moved or transported to the other leaflet. The leaflets flip-flop the lipids and exchange the molecules for various purposes such as achieving a dynamic equilibrium. More

For all its massive popularity, Minecraft — the highest-selling video game of all time — is not highly regarded among the gaming world’s snob class. The graphics are blocky, and there isn’t much of a point to it. It’s for kids.
But according to many millions of users, including some Concordia faculty and students, Minecraft’s malleability is its strength. Free from constraints and easily modifiable, the game can be used in countless ways, including as a game-based teaching method. In a period when classrooms have had to pivot online with little warning or prep time, the realm of Minecraft has provided educators with a massive sandbox in which to play, experiment and teach.
A new paper published in the journal Gamevironments by Darren Wershler, professor of English, and Bart Simon, associate professor of sociology and director of Concordia’s Milieux Institute for Arts, Culture and Technology, describes how Wershler used Minecraft to teach a class on the history and culture of modernity. The course was based entirely within the game server, with instructions, in-class communication and course work almost exclusively carried out within the Minecraft world and over the messaging app Discord. This new pedagogical framework presented the researchers with the opportunity to see how the students used the game to achieve academic goals.
“The course is not a video game studies course, and it is not a gamified version of a course on modernity,” explains Wershler, a Tier 2 Concordia University Research Chair in Media and Contemporary Literature. “It’s this other thing that sits in an uncomfortable middle and brushes up against both. The learning comes out of trying to think about those two things simultaneously.”
Familiar concepts, new learning
The students quickly adapted to their unique classroom and lost little time adapting to their new learning environment. Some took time to teach their peers who were unfamiliar with the game, providing them with instructions on how to mine resources, build homes, plant food and survive waves of attacks by hostile zombies and skeletons. Others, who usually did not identify themselves as natural-born leaders, found themselves answering questions and providing guidance because of their gaming proficiency.
The students eventually decided on group projects that would be created in the Minecraft world and touched on the issues of modernity addressed in Wershler’s half-hour podcast lectures and readings. One group tried to recreate Moshe Safdie’s futuristic Habitat 67, which, Wershler notes, fits right into the Minecraft aesthetic. Another group built an entire working city (populated by Minecraft villagers) on the model of the Nakagin Capsule Tower Building in Tokyo.
Rather than using the Creative mode that many educators favour, the game was set in the more difficult Survival mode, so students were often killed by marauding foes. The researchers downloaded fan-made modifications to enhance the game as they chose; but the mods also made the gameplay wonkier and more liable to crash at inopportune times.
“It was important that the game remained a game and that while the students were working on their projects, there were all these horrible things coming out of the wilderness to kill them,” Wershler says. “This makes them think about the fact that what they are doing requires effort and that the possibility of failure is very real.”
An adaptable build
He admits to being happily surprised with how well his students adapted to the parameters of the course he co-designed along with a dozen other interdisciplinary researchers at Concordia. Wershler has been using Minecraft in his course since 2014, but he realized this approach created a scaffold for a new style of teaching.
“Educators at the high school, college and university levels can use these principles and tools to teach a whole variety of subjects within the game,” he says. “There is no reason why we could not do this with architecture, design, engineering, computer science as well as history, cultural studies or sociology. There are countless ways to structure this to make it work.”
Story Source:
Materials provided by Concordia University. Original written by Patrick Lejtenyi. Note: Content may be edited for style and length. More

Imagine a field of wheat that extends to the horizon, being grown for flour that will be made into bread to feed cities’ worth of people. Imagine that all authority for tilling, planting, fertilising, monitoring and harvesting this field has been delegated to artificial intelligence: algorithms that control drip-irrigation systems, self-driving tractors and combine harvesters, clever enough to respond to the weather and the exact needs of the crop. Then imagine a hacker messes things up.
A new risk analysis, published today in the journal Nature Machine Intelligence, warns that the future use of artificial intelligence in agriculture comes with substantial potential risks for farms, farmers and food security that are poorly understood and under-appreciated.
“The idea of intelligent machines running farms is not science fiction. Large companies are already pioneering the next generation of autonomous ag-bots and decision support systems that will replace humans in the field,” said Dr Asaf Tzachor in the University of Cambridge’s Centre for the Study of Existential Risk (CSER), first author of the paper.
“But so far no-one seems to have asked the question ‘are there any risks associated with a rapid deployment of agricultural AI?'” he added.
Despite the huge promise of AI for improving crop management and agricultural productivity, potential risks must be addressed responsibly and new technologies properly tested in experimental settings to ensure they are safe, and secure against accidental failures, unintended consequences, and cyber-attacks, the authors say.
In their research, the authors have come up with a catalogue of risks that must be considered in the responsible development of AI for agriculture — and ways to address them. In it, they raise the alarm about cyber-attackers potentially causing disruption to commercial farms using AI, by poisoning datasets or by shutting down sprayers, autonomous drones, and robotic harvesters. To guard against this they suggest that ‘white hat hackers’ help companies uncover any security failings during the development phase, so that systems can be safeguarded against real hackers. More

An object hidden below ground has been located using quantum technology — a long-awaited milestone with profound implications for industry, human knowledge and national security.
University of Birmingham researchers from the UK National Quantum Technology Hub in Sensors and Timing have reported their achievement in Nature. It is the first in the world for a quantum gravity gradiometer outside of laboratory conditions.
The quantum gravity gradiometer, which was developed under a contract for the Ministry of Defence and in the UKRI-funded Gravity Pioneer project, was used to find a tunnel buried outdoors in real-world conditions one metre below the ground surface. It wins an international race to take the technology outside.
The sensor works by detecting variations in microgravity using the principles of quantum physics, which is based on manipulating nature at the sub-molecular level.
The success opens a commercial path to significantly improved mapping of what exists below ground level.
This will mean: Reduced costs and delays to construction, rail and road projects. Improved prediction of natural phenomena such as volcanic eruptions. Discovery of hidden natural resources and built structures. Understanding archaeological mysteries without damaging excavation.Professor Kai Bongs, Head of Cold Atom Physics at the University of Birmingham and Principal Investigator of the UK Quantum Technology Hub Sensors and Timing, said: “This is an ‘Edison moment’ in sensing that will transform society, human understanding and economies. More

Scientists at the U.S. Department of Energy’s Brookhaven National Laboratory have discovered a long-predicted magnetic state of matter called an “antiferromagnetic excitonic insulator.”
“Broadly speaking, this is a novel type of magnet,” said Brookhaven Lab physicist Mark Dean, senior author on a paper describing the research just published in Nature Communications. “Since magnetic materials lie at the heart of much of the technology around us, new types of magnets are both fundamentally fascinating and promising for future applications.”
The new magnetic state involves strong magnetic attraction between electrons in a layered material that make the electrons want to arrange their magnetic moments, or “spins,” into a regular up-down “antiferromagnetic” pattern. The idea that such antiferromagnetism could be driven by quirky electron coupling in an insulating material was first predicted in the 1960s as physicists explored the differing properties of metals, semiconductors, and insulators.
“Sixty years ago, physicists were just starting to consider how the rules of quantum mechanics apply to the electronic properties of materials,” said Daniel Mazzone, a former Brookhaven Lab physicist who led the study and is now at the Paul Scherrer Institut in Switzerland. “They were trying to work out what happens as you make the electronic ‘energy gap’ between an insulator and a conductor smaller and smaller. Do you just change a simple insulator into a simple metal where the electrons can move freely, or does something more interesting happen?”
The prediction was that, under certain conditions, you could get something more interesting: namely, the “antiferromagnetic excitonic insulator” just discovered by the Brookhaven team.
Why is this material so exotic and interesting? To understand, let’s dive into those terms and explore how this new state of matter forms. More

Permafrost — ground that has been permanently frozen for two or more years — makes up a large part of the Earth, around 15% of the Northern Hemisphere.
Permafrost is important for our climate, containing large amounts of biomass stored as methane and carbon dioxide, making tundra soil a carbon sink. However, permafrost’s innate characteristics and changing nature are not broadly understood.
As global warming heats the Earth and causes soil thawing, the permafrost carbon cycle is expected to accelerate and release soil-contained greenhouse gases into the atmosphere, creating a feedback loop that will exacerbate climate change.
Remote sensing is one way of getting a handle on the breadth, dynamics, and changes to permafrost. “It’s like a virtual passport to see this remote and difficult to reach part of the world,” says Chandi Witharana, assistant professor of Natural Resources & the Environment at the University of Connecticut. “Satellite imaging helps us monitor remote landscape in a detailed manner that we never had before.”
Over the past two decades, much of the Arctic has been mapped with extreme precision by commercial satellites. These maps are a treasure trove of data about this largely underexplored region. But the data is so large and unwieldy, it makes scholarship difficult, Witharana says.
With funding and support from the U.S. National Science Foundation (NSF) as part of the “Navigating the New Arctic” program, Witharana, as well as Kenton McHenry from the National Center for Supercomputing Applications, and Arctic researcher Anna Liljedahl of the Woodwell Climate Research Center, are making data about Arctic permafrost much more accessible. More

Insect populations are plummeting worldwide, with major consequences for our ecosystems and without us quite knowing why. A new AI method from the University of Copenhagen is set to help monitor and catalogue insect biodiversity, which until now has been quite challenging.
Insects are vital as plant pollinators, as a food source for a wide variety of animals and as decomposers of dead material in nature. But in recent decades, they have been struggling. It is estimated that 40 percent of insect species are in decline and a third of them are endangered.
Therefore, it is more important than ever to monitor insect biodiversity, so as to understand their decline and hopefully help them out. So far, this task has been difficult and resource-intensive. In part, this is due to the fact that insects are small and very dynamic. Furthermore, scientific researchers and public agencies need to set up traps, capture insects and study them under the microscope.
To overcome these hurdles, University of Copenhagen researchers have developed a method that uses the data obtained from an infrared sensor to recognize and detect the wingbeats of individual insects. The AI method is based on unsupervised machine learning — where the algorithms can group insects belonging to the same species without any human input. The results from this method could provide information about the diversity of insect species in a natural space without anyone needing to catch and count the critters by hand.
“Our method makes it much easier to keep track of how insect populations are evolving. There has been a huge loss of insect biomass in recent years. But until we know exactly why insects are in decline, it is difficult to develop the right solutions. This is where our method can contribute new and important knowledge,” states PhD student Klas Rydhmer of the Department of Geosciences and Natural Resource Management at UCPH’s Faculty of Science, who helped develop the method.
Advanced artificial intelligence
The researchers have already developed an algorithm that identifies pests in agricultural fields. But instead of identifying insects as pests, the researchers have been able to develop this new algorithm to identify and count various insect populations in nature based on the measurements obtained from the sensor. More