Aurelia Butler

In a small but multi-institutional study, an artificial intelligence-based system improved providers’ assessments of whether patients with bladder cancer had complete response to chemotherapy before a radical cystectomy (bladder removal surgery).
Yet the researchers caution that AI isn’t a replacement for human expertise and that their tool shouldn’t be used as such.
“If you use the tool smartly, it can help you,” said Lubomir Hadjiyski, Ph.D., a professor of radiology at the University of Michigan Medical School and the senior author of the study.
When patients develop bladder cancer, surgeons often remove the entire bladder in an effort to keep the cancer from returning or spreading to other organs or areas. More evidence is building, though, that surgery may not be necessary if a patient has zero evidence of disease after chemotherapy.
However, it’s difficult to determine whether the lesion left after treatment is simply tissue that’s become necrotic or scarred as a result of treatment or whether cancer remains. The researchers wondered if AI could help.
“The big question was when you have such an artificial device next to you, how is it going to affect the physician?” Hadjiyski said. “Is it going to help? Is it going to confuse them? Is it going to raise their performance or will they simply ignore it?”
Fourteen physicians from different specialties — including radiology, urology and oncology — as well as two fellows and a medical student looked at pre- and post-treatment scans of 157 bladder tumors. The providers gave ratings for three measures that assessed the level of response to chemotherapy as well as a recommendation for the next treatment to be done for each patient (radiation or surgery). More

When two people meet, they instantly size each other up, making snap judgments about everything from the other person’s age to their intelligence or trustworthiness based solely on the way they look. Those first impressions, though often inaccurate, can be extremely powerful, shaping our relationships and impacting everything from hiring decisions to criminal sentencing.
Researchers at Stevens Institute of Technology, in collaboration with Princeton University and University of Chicago, have now taught an AI algorithm to model these first impressions and accurately predict how people will be perceived based on a photograph of their face. The work appears today, in the April 21 issue of the Proceedings of the National Academy of Sciences.
“There’s a wide body of research that focuses on modeling the physical appearance of people’s faces,” said Jordan W. Suchow, a cognitive scientist and AI expert at the School of Business at Stevens. “We’re bringing that together with human judgments and using machine learning to study people’s biased first impressions of one another.”
Suchow and team, including Joshua Peterson and Thomas Griffiths at Princeton, and Stefan Uddenberg and Alex Todorov at Chicago Booth, asked thousands of people to give their first impressions of over 1,000 computer-generated photos of faces, ranked using criteria such as how intelligent, electable, religious, trustworthy, or outgoing a photograph’s subject appeared to be. The responses were then used to train a neural network to make similar snap judgments about people based solely on photographs of their faces.
“Given a photo of your face, we can use this algorithm to predict what people’s first impressions of you would be, and which stereotypes they would project onto you when they see your face,” Suchow explained.
Many of the algorithm’s findings align with common intuitions or cultural assumptions: people who smile tend to be seen as more trustworthy, for instance, while people with glasses tend to be seen as more intelligent. In other cases, it’s a little harder to understand exactly why the algorithm attributes a particular trait to a person. More

A new decision-support tool could become a game changer in the dredging of ship channels. Millions of dollars are at stake every time a major ship channel is cleaned up. Delays in dredging can cost even more by triggering increased risks, repeated maintenance and lost revenue. In either case, the task cannot be put off indefinitely.
All ship channels must be regularly cleared of sand, debris settled on the bottom (called shoal) and miscellaneous trash. That means ship channel management regularly faces the mighty task of dredging. How do they make the wisest decisions with the best timing?
“The quandary involves weighing factors for the optimal decision of channel dredging and disposal activities,” said Zheyong Bian, assistant professor of construction management at the University of Houston College of Technology and the lead author of a study published in the journal Transportation Research Part E: Logistics and Transportation Review. “Some factors are static, like geographical features of navigation channels and confined disposal facilities. Others vary substantially, such as navigability condition deterioration (shoaling), traffic, economic values, annual budget and more.”
Bian, then a doctoral student at Rutgers University, The State University of New Jersey, collaborated with project leader Yun Bai from the Center for Advanced Infrastructure and Transportation (CAIT) at Rutgers, to develop a dredging planning optimization model (DPOM) and a dynamic prioritization planning (DPP) algorithm that factor in known variables, such as the volume of debris expected and availability of nearby confined disposal facilities. It also considers grouping phases of the project and how costs can be affected by interest rates and inflation.
But — this is a key advantage, Bian stressed — the algorithm also holds the flexibility that weighs input from local professionals. In other words, it values boots-on-the-ground opinions and learns from experience.
Once all factors are included, the algorithm suggests timing, prioritization and the grouping of projects. It also projects costs, with interest and inflation included, as well as monies likely to be recouped (through repurposing of sand, for example).
In these days of tightened budgets, public funds are deployed with ever more care. The new DPOM model and DPP algorithm could strengthen cost efficiencies at ship channels around the world. The New Jersey Department of Transportation Office of Maritime Resources provided funding and data to support this study.
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Materials provided by University of Houston. Original written by Sally Strong. Note: Content may be edited for style and length. More

A new study shows that it is possible to use the genetic sequences of a person’s antibodies to predict what pathogens those antibodies will target. Reported in the journal Immunity, the new approach successfully differentiates between antibodies against influenza and those attacking SARS-CoV-2, the virus that causes COVID-19.
“Our research is in a very early stage, but this proof-of-concept study shows that we can use machine learning to connect the sequence of an antibody to its function,” said Nicholas Wu, a professor of biochemistry at the University of Illinois Urbana-Champaign who led the research with U. of I. biochemistry Ph.D. student Yiquan Wang; and Meng Yuan, a staff scientist at Scripps Research in La Jolla, California.
With enough data, scientists should be able to predict not only the virus an antibody will attack, but which features on the pathogen the antibody binds to, Wu said. For example, an antibody may attach to different parts of the spike protein on the SARS-CoV-2 virus. Knowing this will allow scientists to predict the strength of a person’s immune defense, as some targets of a pathogen are more vulnerable than others.
The new approach was made possible by the abundance of data related to antibodies against SARS-CoV-2, Wu said.
“In 20 years, scientists have discovered about 5,000 antibodies against the flu virus,” he said. “But in just two years, people have identified 8,000 antibodies for COVID. This provides an opportunity that’s never been seen before to study how antibodies work and to do this kind of prediction.”
The researchers used antibody data from 88 published studies and 13 patents. The datasets were big enough to allow the researchers to train their model to make predictions based on the antibodies’ genetic sequence.
The model was designed to distinguish whether the sequences coded for antibodies targeting regions on the influenza virus or on the SARS-CoV-2 virus. The researchers then checked the accuracy of those predictions.
“The accuracy was close to 85% overall,” Wang said.
“I was actually quite surprised that it worked so well,” Wu said.
The team is working to improve its model so that it can more precisely determine which parts of the virus the antibodies attack.
“If we can make these predictions based on antibody sequence, we might also be able to go back and design antibodies that bind to specific pathogens,” Wu said. “This is not something that we can do now, but those are some implications for future study.”
The National Institutes of Health supported this research.
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Materials provided by University of Illinois at Urbana-Champaign, News Bureau. Original written by Diana Yates. Note: Content may be edited for style and length. More

In a global first, scientists have demonstrated that molecular robots are able to accomplish cargo delivery by employing a strategy of swarming, achieving a transport efficiency five times greater than that of single robots.
Swarm robotics is a new discipline, inspired by the cooperative behavior of living organisms, that focuses on the fabrication of robots and their utilization in swarms to accomplish complex tasks. A swarm is an orderly collective behavior of multiple individuals. Macro-scale swarm robots have been developed and employed for a variety of applications, such as transporting and accumulating cargo, forming shapes, and building complex structures.
A team of researchers, led by Dr. Mousumi Akter and Associate Professor Akira Kakugo from the Faculty of Science at Hokkaido University, has succeeded in developing the world’s first working micro-sized machines utilizing the advantages of swarming. The findings were published in the journal Science Robotics. The team included Assistant Professor Daisuke Inoue, Kyushu University; Professor Henry Hess, Columbia University; Professor Hiroyuki Asanuma, Nagoya University; and Professor Akinori Kuzuya, Kansai University.
A swarm of cooperating robots gains a number of characteristics which are not found in individual robots — they can divide a workload, respond to risks, and even create complex structures in response to changes in the environment. Microrobots and machines at the micro- and nano-scale have very few practical applications due to their size; if they could cooperate in swarms, their potential uses would increase massively.
The team constructed about five million single molecular machines. These machines were composed of two biological components: microtubules linked to DNA, which allowed them to swarm; and kinesin, which were actuators capable of transporting the microtubules. The DNA was combined with a light-sensitive compound called azobenzene that functioned as a sensor, allowing for control of swarming. When exposed to visible light, changes in the structure of azobenzene caused the DNA to form double strands and led to the microtubules forming swarms. Exposure to UV light reversed this process.
The cargo used in the experiments consisted of polystyrene beads of diameters ranging from micrometers to tens of micrometers. These beads were treated with azobenzene-linked DNA; thus, the cargo was loaded when exposed to visible light and unloaded when exposed to UV light. However, the DNA and azobenzene used in the molecular machines and the cargo were different, so swarming could be controlled independently of cargo-loading.
Single machines are able to load and transport polystyrene beads up to 3 micrometers in diameter, whereas swarms of machines could transport cargo as large as 30 micrometers in diameter. Furthermore, a comparison of transport distance and transport volume showed that the swarms were up to five times more efficient at transport compared to the single machines.
By demonstrating that molecular machines can be designed to swarm and cooperate to transport cargo with high efficiency, this study has laid the groundwork for the application of microrobots to various fields. “In the near future, we expect to see microrobot swarms used in drug delivery, contaminant collection, molecular power generation devices, and micro-detection devices,” says Akira Kakugo.
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Materials provided by Hokkaido University. Note: Content may be edited for style and length. More

There’s no doubt that artificial intelligence is embedded in our everyday lives. From smartphones to ridesharing apps to mobile check deposits, AI is so pervasive that we rarely think about how it works.
For one University of Oklahoma scientist, however, artificial intelligence and machine learning are at the forefront of her work — expressly as it relates to weather. Amy McGovern, Ph.D., leads the National Science Foundation AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography at the University of Oklahoma.
An American Meteorological Fellow, McGovern has been studying severe weather phenomena since the late 1990s. During her career, she has witnessed a rapid emergence in the AI field, all while developing what she hopes are trustworthy AI methods to avert weather and climate disasters.
Lately, however, McGovern and researchers from Colorado and Washington have noticed grave disparities in AI, noting that the methods are not objective, especially when it comes to geodiversity.
“Artificial intelligence algorithms are based on mathematical formulas that are seen as objective; however, there is a bias toward areas with higher populations, as well as areas that are more affluent,” said McGovern, a professor at OU’s School of Computer Science and School of Meteorology.
“For example, if more people live in an area, there is a higher chance that someone observes and reports a hail or tornado event. This can bias the AI model to over-predict hail and tornadoes in urban areas and under-predict severe weather in rural towns,” she said.
AI tools, whether forecasting hail, wind or tornadoes, are assumed to be inherently objective. They aren’t, McGovern says.
Raising Awareness
The team recently published a paper titled “Why We Need to Focus on Developing Ethical, Responsible, and Trustworthy AI Approaches for Environmental Sciences.” Published by Cambridge University Press, the paper will appear in the inaugural issue of Environmental Data Science.
The researchers are exploring ethical AI methods, specifically in the field of environmental sciences. “Whether involved in teaching, industry or government, environmental scientists are absolutely essential for developing meaningful AI tools, and more educational resources are needed to help environmental scientists learn the basics of artificial intelligence so they can play a leading role in future developments,” McGovern said.
The group sees ethics in AI in the environmental sciences as an emerging trend in education. “With the rapid emergence of data science techniques in the sciences and the societal importance of many of these applications, there is an urgent need to prepare future scientists to be knowledgeable,” McGovern said.
AI systems can be as flawed as the people who create them and can unintentionally do more harm than good if not developed and applied responsibly, McGovern says. “We hope our work is a major step toward making AI systems more ethically informed in environmental science.”
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Materials provided by University of Oklahoma. Note: Content may be edited for style and length. More

Researchers have discovered that light — in the form of a laser — can trigger a form of magnetism in a normally nonmagnetic material. This magnetism centers on the behavior of electrons. These subatomic particles have an electronic property called “spin,” which has a potential application in quantum computing. The researchers found that electrons within the material became oriented in the same direction when illuminated by photons from a laser.
The experiment, led by scientists at the University of Washington and the University of Hong Kong, was published April 20 in Nature.
By controlling and aligning electron spins at this level of detail and accuracy, this platform could have applications in the field of quantum simulation, according to co-senior author Xiaodong Xu, a Boeing Distinguished Professor at the UW in the Department of Physics and the Department of Materials Science and Engineering.
“In this system, we can use photons essentially to control the ‘ground state’ properties — such as magnetism — of charges trapped within the semiconductor material,” said Xu, who is also a faculty researcher with the UW’s Clean Energy Institute and the Molecular Engineering & Sciences Institute. “This is a necessary level of control for developing certain types of qubits — or ‘quantum bits’ — for quantum computing and other applications.”
Xu, whose research team spearheaded the experiments, led the study with co-senior author Wang Yao, professor of physics at the University of Hong Kong, whose team worked on the theory underpinning the results. Other UW faculty members involved in this study are co-authors Di Xiao, a UW professor of physics and of materials science and engineering who also holds a joint appointment at the Pacific Northwest National Laboratory, and Daniel Gamelin, a UW professor of chemistry and director of the Molecular Engineering Materials Center.
The team worked with ultrathin sheets — each just three layers of atoms thick — of tungsten diselenide and tungsten disulfide. Both are semiconductor materials, so named because electrons move through them at a rate between that of a fully conducting metal and an insulator, with potential uses in photonics and solar cells. Researchers stacked the two sheets to form a “moiré superlattice,” a stacked structure made up of repeating units. More

We blame smartphone use for a number of negative consequences, ranging from neck pain to addictive behavior. Privat-Dozentin Dr. Julia Brailovskaia and her team set out to determine whether our lives are actually better without smartphones, or rather: how much less smartphone use per day is good for us. The psychologist from the Mental Health Research and Treatment Center at Ruhr-Universität Bochum (RUB) had around 200 test participants each do without their smartphones completely for a week, reduce their daily use by one hour or use the smartphone in the same way as before. Their findings show: in the long term, those who reduced their use fared best. The researcher report in the Journal of Experimental Psychology: Applied from 7 April 2022.
How much smartphone use is good for us?
On average, we spend more than three hours a day glued to our smartphone screens. We google, look for directions, check emails or the weather, shop, read the news, watch films, hang out on social media. It seems reasonable to suspect that all this is not good for us. Studies have shown that smartphone use is linked to problems such as less physical activity, obesity, neck pain, impaired performance, and addiction-like behavior — to name just a few. “The smartphone is both a blessing and a curse,” says Julia Brailovskaia.
Her team wanted to know: how much smartphone is good for us? To this end, the researchers compared the effect of complete smartphone abstinence versus a reduction in time spent daily looking at the screen and versus continued use without any changes. They recruited 619 people for their study and divided them randomly into three groups. 200 people put their smartphone completely aside for a week. 226 reduced the amount of time they used the device by one hour a day. 193 people didn’t change anything in their behavior.
Physical activity, cigarettes, life satisfaction, anxiety, depression
The researchers interviewed all participants about their lifestyle habits and well-being immediately after the intervention, one month and four months later. How much did they engage in physical activity? How many cigarettes did they smoke a day? How satisfied with their life did they feel? Did they show any signs of anxiety or depression? “We found that both completely giving up the smartphone and reducing its daily use by one hour had positive effects on the lifestyle and well-being of the participants,” as Julia Brailovskaia sums up the results. “In the group who reduced use, these effects even lasted longer and were thus more stable than in the abstinence group.”
It’s not necessary to do completely without
The one-week intervention changed the participants’ usage habits in the long term: even four months after the end of the experiment, the members of the abstinence group used their smartphone on average 38 minutes less per day than before. The group who had spent one hour less per day with the smartphone during the experiment used it as much as 45 minutes less per day after four months than before. At the same time, life satisfaction and time spent being physically active increased. Symptoms of depression and anxiety as well as nicotine consumption decreased. “It’s not necessary to completely give up the smartphone to feel better,” concludes Brailovskaia. “There may be an optimal daily usage time.”
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Materials provided by Ruhr-University Bochum. Original written by Meike Drießen. Note: Content may be edited for style and length. More