AI predicts enzyme function better than leading tools

An Illinois research team created an AI tool to predict an enzyme’s function from its sequence using the campus network and resource group servers. Pictured, from left: Tianhao You, Haiyang (Ocean) Cui, Huimin Zhao and Guangde Jiang.   
Photo Credit: Fred Zwicky

A new artificial intelligence tool can predict the functions of enzymes based on their amino acid sequences, even when the enzymes are unstudied or poorly understood. The researchers said the AI tool, dubbed CLEAN, outperforms the leading state-of-the-art tools in accuracy, reliability and sensitivity. Better understanding of enzymes and their functions would be a boon for research in genomics, chemistry, industrial materials, medicine, pharmaceuticals and more.

“Just like ChatGPT uses data from written language to create predictive text, we are leveraging the language of proteins to predict their activity,” said study leader Huimin Zhao, a University of Illinois Urbana-Champaign professor of chemical and biomolecular engineering. “Almost every researcher, when working with a new protein sequence, wants to know right away what the protein does. In addition, when making chemicals for any application – biology, medicine, industry – this tool will help researchers quickly identify the proper enzymes needed for the synthesis of chemicals and materials.”

The researchers will publish their findings in the journal Science and make CLEAN accessible online March 31.

Machine learning models rank predictive risks for Alzheimer’s disease

Xiaoyi Raymond Gao, PhD Associate Professor
Photo Credit: Courtesy of Ohio State University

Once adults reach age 65, the threshold age for the onset of Alzheimer’s disease, the extent of their genetic risk may outweigh age as a predictor of whether they will develop the fatal brain disorder, a new study suggests. 

The study, published recently in the journal Scientific Reports, is the first to construct machine learning models with genetic risk scores, non-genetic information and electronic health record data from nearly half a million individuals to rank risk factors in order of how strong their association is with eventual development of Alzheimer’s disease.

Researchers used the models to rank predictive risk factors for two populations from the UK Biobank: White individuals aged 40 and older, and a subset of those adults who were 65 or older. 

Results showed that age – which constitutes one-third of total risk by age 85, according to the Alzheimer’s Association – was the biggest risk factor for Alzheimer’s in the entire population, but for the older adults, genetic risk as determined by a polygenic risk score was more predictive. 

“We all know Alzheimer’s disease is a later-onset disease, so we know age is an important risk factor. But when we consider risk only for people age 65 or older, then genetic information captured by a polygenic risk score ranks higher than age,” said lead study author Xiaoyi Raymond Gao, associate professor of ophthalmology and visual sciences and of biomedical informatics in The Ohio State University College of Medicine. “That means it’s really important to consider genetic information when we work on Alzheimer’s disease.” 

Can Artificial Intelligence Predict Spatiotemporal Distribution of Dengue Fever Outbreaks with Remote Sensing Data?

Image Credit: Sophia University
Full Size Image

Researchers train machine learning model with climatic and epidemiology remote sensing data to predict the spatiotemporal distribution of disease outbreaks

Cases of dengue fever and other zoonotic diseases will keep increasing owing to climate change, and prevention via early warning is one of our best options against them. Recently, researchers combined a machine learning model with remote sensing climatic data and information on past dengue fever cases in Chinese Taiwan, with the aim of predicting likely outbreak locations. Their findings highlight the hurdles to this approach and could facilitate more accurate predictive models.

Outbreaks of zoonotic diseases, which are those transmitted from animals to humans, are globally on the rise owing to climate change. In particular, the spread of diseases transmitted by mosquitoes is very sensitive to climate change, and Chinese Taiwan has seen a worrisome increase in the number of cases of dengue fever in recent years.

Like for most known diseases, the popular saying “an ounce of prevention is worth a pound of cure” also rings true for dengue fever. Since there is still no safe and effective vaccine for all on a global scale, dengue fever prevention efforts rely on limiting places where mosquitoes can lay their eggs and giving people an early warning when an outbreak is likely to happen. However, thus far, there are no mathematical models that can accurately predict the location of dengue fever outbreaks ahead of time.

Shining a light into the ‘‘black box’’ of AI

With no insight into how Al algorithms work or what influences their results, the “black box” nature of AI technology raises important questions over trustworthiness.
Illustration Credit: Gerd Altmann

An international team led by UNIGE, HUG and NUS has developed an innovative method for evaluating AI interpretability methods, with the aim of deciphering the basis of AI reasoning and possible biases.

 Researchers from the University of Geneva (UNIGE), the Geneva University Hospitals (HUG), and the National University of Singapore (NUS) have developed a novel method for evaluating the interpretability of artificial intelligence (AI) technologies, opening the door to greater transparency and trust in AI-driven diagnostic and predictive tools. The innovative approach sheds light on the opaque workings of so-called "black box" AI algorithms, helping users understand what influences the results produced by AI and whether the results can be trusted. This is especially important in situations that have significant impacts on the health and lives of people, such as using AI in medical applications. The research carries particular relevance in the context of the forthcoming European Union Artificial Intelligence Act which aims to regulate the development and use of AI within the EU. The findings have recently been published in the journal Nature Machine Intelligence.

“Denoising” a Noisy Ocean

Study lead author Ella Kim (pink helmet) helps deploy a HARP instrument package.
Photo Credit: Ana Širović

Come mating season, fishes off the California coast sing songs of love in the evenings and before sunrise. They vocalize not so much as lone crooners but in choruses, in some cases loud enough to be heard from land. It’s a technique of romance shared by frogs, insects, whales, and other animals when the time is right.

For most of these vocal arrangements, the choruses are low-frequency. They’re hard to distinguish from the sounds of ships passing in the night among others.

Biologists, however, have long been interested in listening in on them in the name of understanding fish behavior toward an ultimate goal: They can help preserve fish populations and ocean health by identifying spawning seasons to inform fisheries management.

Now scientists at Scripps Institution of Oceanography at UC San Diego and colleagues have developed a way for computers to sift through sounds collected by field acoustic recording packages known as HARPs and process them faster than even the most trained human analysts. The method represents a major advance in the field of signal processing with uses beyond marine environments.

AI offers ‘paradigm shift’ in Stanford study of brain injury

Models discovered by the Constitutive Artificial Neural Network outperform existing models for brain tissue.
Image Credit: Ellen Kuhl

By helping researchers choose among thousands of available computational models of mechanical stress on the brain, AI is yielding powerful new insight on traumatic brain injury.

From the gridiron to the battlefield, the study of traumatic brain injury has exploded in recent years. Crucial to understanding brain injury is the ability to model the mechanical forces that compress, stretch, and twist the brain tissue and cause damage that ranges from fleeting to fatal.

Researchers at Stanford University now say they have tapped artificial intelligence to produce a profoundly more accurate model of how deformations translate into stresses in the brain and believe that their approach could reveal a more definitive understanding of when and why concussion sometimes leads to lasting brain damage, and other times not.

“The problem in brain modeling to date is that the brain is not a homogeneous tissue – it’s not the same in every part of the brain. Yet, trauma is often pervasive,” said Ellen Kuhl, professor of mechanical engineering, director of the Living Matter Lab, and senior author of a new study appearing in the journal, Acta Biomaterialia. “The brain is also ultrasoft, much like Jell-O, which makes both testing and modeling physical effects on the brain very challenging.”

Hackers could try to take over a military aircraft; can a cyber shuffle stop them?

Sandia National Laboratories cybersecurity expert Chris Jenkins sits in front of a whiteboard with the original sketch of the moving target defense idea for which he is the team lead. When the COVID-19 pandemic hit, Jenkins began working from home, and his office whiteboard remained virtually undisturbed for more than two years.
Photo Credit: Craig Fritz

A cybersecurity technique that shuffles network addresses like a blackjack dealer shuffles playing cards could effectively befuddle hackers gambling for control of a military jet, commercial airliner or spacecraft, according to new research. However, the research also shows these defenses must be designed to counter increasingly sophisticated algorithms used to break them.

Many aircraft, spacecraft and weapons systems have an onboard computer network known as military standard 1553, commonly referred to as MIL-STD-1553, or even just 1553. The network is a tried-and-true protocol for letting systems like radar, flight controls and the heads-up display talk to each other.

Securing these networks against a cyberattack is a national security imperative, said Chris Jenkins, a Sandia National Laboratories cybersecurity scientist. If a hacker were to take over 1553 midflight, he said, the pilot could lose control of critical aircraft systems, and the impact could be devastating.

Jenkins is not alone in his concerns. Many researchers across the country are designing defenses for systems that utilize the MIL-STD-1553 protocol for command and control. Recently, Jenkins and his team at Sandia partnered with researchers at Purdue University in West Lafayette, Indiana, to test an idea that could secure these critical networks.

Let's get wasted and apply some deep thinking to rubbish

Photo Credit: John Cameron

Artificial intelligence has made a giant leap into our rubbish bins thanks to new technology being deployed at the University of South Australia.

Using algorithms to analyze data from smart bin sensors, UniSA PhD student Sabbir Ahmed is designing a deep learning model to predict where waste is accumulating in cities and how often public bins should be cleared.

“Sensors in the public smart bins can give us a lot of information about how busy specific locations are, what type of rubbish is being disposed of and even how much methane gas is being produced from food waste in bins,” Ahmed says.

“All that data can be fed into a neural network model to predict where bins in parks, shopping centers and other public places are likely to fill up quickly and, conversely, which locations are rarely visited.

“This can help councils to optimize their waste management services, schedule bin clearances and even relocate rarely used bins to where they are needed most.”

Infants Outperform AI in “Commonsense Psychology”

New Study Shows How Infants Are More Adept at Spotting Motivations that Drive Human Behavior

Infants outperform artificial intelligence in detecting what motivates other people’s actions, finds a new study by a team of psychology and data science researchers. Its results, which highlight fundamental differences between cognition and computation, point to shortcomings in today’s technologies and where improvements are needed for AI to more fully replicate human behavior. 

“Adults and even infants can easily make reliable inferences about what drives other people’s actions,” explains Moira Dillon, an assistant professor in New York University’s Department of Psychology and the senior author of the paper, which appears in the journal Cognition. “Current AI finds these inferences challenging to make.”

“The novel idea of putting infants and AI head-to-head on the same tasks is allowing researchers to better describe infants’ intuitive knowledge about other people and suggest ways of integrating that knowledge into AI,” she adds.

“If AI aims to build flexible, commonsense thinkers like human adults become, then machines should draw upon the same core abilities infants possess in detecting goals and preferences,” says Brenden Lake, an assistant professor in NYU’s Center for Data Science and Department of Psychology and one of the paper’s authors.

Researchers aim to bring humans back into the loop, as AI use and misuse rises

U-M researchers aim to bring humans back into the loop, as AI use and misuse rises
Image Credit: Gerd Altmann

Artificial intelligence is dominating headlines—enabling new innovations that drive business performance—yet the negative implications for society are an afterthought.

How can humans get back into the loop in the quest toward a better society for all?

A trans-Atlantic team of researchers, including two from the University of Michigan, has reviewed information systems research on what’s known as the “Fourth Industrial Revolution” and found an overwhelming focus on technology-enabled business benefits.

The focus means far less attention is being paid to societal implications—what the researchers refer to as “the increasing risk and damage to humans.”

“We’re talking about AI the wrong way—focusing on technology not people—moving us away from the things we want, such as better medications, elder care and safety regulations, and toward the things we don’t, like harmful deepfakes, job losses and biased decision making,” said Nigel Melville, associate professor of technology and operations at U-M’s Ross School of Business and design science program director.

AI could improve mental health care

Photo Credit: SHVETS production

Patients are often asked to rate their feelings using a rating scale, when talking to psychologists or doctors about their mental health. This is currently how depression and anxiety are diagnosed. However, a new study from Lund University in Sweden shows that allowing patients to describe their experience using their own words - is potentially viewed as more precise and preferred by the patients. The Lund researchers have developed an AI-tool that could help doctors analyze their patients’ answers.

The study, published in PLOS ONE, shows that clinicians and patients differ somewhat, as clinics often prefer rating scales when diagnosing a patient (e.g., little interest in doing things: not at all, sometimes, often, daily) whereas patients prefer free language (e.g., Describe your mental health).

The researchers surveyed a group of 150 patients with self-diagnosed depression or anxiety, posing the same questions to a control group of 150 other participants.

AI with infrared imaging enables precise colon cancer diagnostics

Klaus Gerwert, Stephanie Schörner and Frederik Großerüschkamp (from left) want to improve the diagnosis of colon cancer with the help of artificial intelligence.
Photo Credit: © RUB, Marquard

Artificial intelligence and infrared imaging automatically classify tumors and are faster than previous methods.

The immense progress in the area of therapy options over the past few years has significantly improved the chances of recovery for patients with colon cancer. However, these new approaches, such as immunotherapy, require a precise diagnosis so that they can be tailored to the respective person. Researchers at the Center for Protein Diagnostics PRODI at the Ruhr University Bochum use artificial intelligence in combination with infrared imaging to optimally coordinate the therapy of colon cancer with the individual patient. The label-free and automatable method can complement existing pathological analyzes. The team around Prof. Dr. Klaus Gerwert reports in the journal "European Journal of Cancer" on January 28, 2023.

Deep insights into human tissue within an hour

The PRODI team has been developing a new method of digital imaging for several years: The so-called label-free infrared (IR) imaging measures the genomic and proteomic composition of the tissue examined, i.e. provides molecular information based on the infrared spectra. This information is decoded using artificial intelligence and displayed as false color images. For this purpose, the researchers use image analysis methods from the field of deep learning.

VISTA X-62 Advancing Autonomy and Changing the Face of Air Power

The X-62A VISTA Aircraft flying above Edwards Air Force Base, California.
Photo Credit: Kyle Brasier, U.S. Air Force

The Lockheed Martin VISTA X-62A, a one-of-a-kind training aircraft, was flown by an artificial intelligence agent for more than 17 hours recently, representing the first time AI engaged on a tactical aircraft.

VISTA, short for Variable In-flight Simulation Test Aircraft, is changing the face of air power at the U.S. Air Force Test Pilot School (USAF TPS) at Edwards Air Force Base in California.

VISTA is a one-of-a-kind training airplane developed by Lockheed Martin Skunk Works® in collaboration with Calspan Corporation for the USAF TPS. Built on open systems architecture, VISTA is fitted with software that allows it to mimic the performance characteristics of other aircraft.

"VISTA will allow us to parallelize the development and test of cutting-edge artificial intelligence techniques with new uncrewed vehicle designs," said Dr. M. Christopher Cotting, U.S. Air Force Test Pilot School director of research. "This approach, combined with focused testing on new vehicle systems as they are produced, will rapidly mature autonomy for uncrewed platforms and allow us to deliver tactically relevant capability to our warfighter."

Efficient technique improves machine-learning models’ reliability

Researchers from MIT and the MIT-IBM Watson AI Lab have developed a new technique that can enable a machine-learning model to quantify how confident it is in its predictions, but does not require vast troves of new data and is much less computationally intensive than other techniques.
Image Credit: MIT News, iStock
Creative Commons Attribution Non-Commercial No Derivatives license

Powerful machine-learning models are being used to help people tackle tough problems such as identifying disease in medical images or detecting road obstacles for autonomous vehicles. But machine-learning models can make mistakes, so in high-stakes settings it’s critical that humans know when to trust a model’s predictions.

Uncertainty quantification is one tool that improves a model’s reliability; the model produces a score along with the prediction that expresses a confidence level that the prediction is correct. While uncertainty quantification can be useful, existing methods typically require retraining the entire model to give it that ability. Training involves showing a model millions of examples so it can learn a task. Retraining then requires millions of new data inputs, which can be expensive and difficult to obtain, and also uses huge amounts of computing resources.

Researchers at MIT and the MIT-IBM Watson AI Lab have now developed a technique that enables a model to perform more effective uncertainty quantification, while using far fewer computing resources than other methods, and no additional data. Their technique, which does not require a user to retrain or modify a model, is flexible enough for many applications.

Robots and A.I. team up to discover highly selective catalysts

Close up of the semi-automated synthesis robot used to generate training data
Photo Credit: ICReDD

Researchers used a chemical synthesis robot and computationally cost effective A.I. model to successfully predict and validate highly selective catalysts.

Artificial intelligence (A.I.) has made headlines recently with the advent of ChatGPT’s language processing capabilities. Creating a similarly powerful tool for chemical reaction design remains a significant challenge, especially for complex catalytic reactions. To help address this challenge, researchers at the Institute for Chemical Reaction Design and Discovery and the Max Planck Institut für Kohlenforschung have demonstrated a machine learning method that utilizes advanced yet efficient 2D chemical descriptors to accurately predict highly selective asymmetric catalysts—without the need for quantum chemical computations.  

“There have been several advanced technologies which can “predict” catalyst structures, but those methods often required large investments of calculation resources and time; yet their accuracy was still limited,” said joint first author Nobuya Tsuji. “In this project, we have developed a predictive model which you can run even with an everyday laptop PC.”

Earth likely to cross critical climate thresholds even if emissions decline

Already, the world is 1.1 degrees Celsius hotter on average than it was before fossil fuel combustion took off in the 1800s. More extreme rainfall and flooding are among the litany of impacts from that warming.
Photo Credit: Chris Gallagher

Artificial intelligence provides new evidence our planet will cross the global warming threshold of 1.5 degrees Celsius within 10 to 15 years. Even with low emissions, we could see 2 C of warming. But a future with less warming remains within reach.

A new study has found that emission goals designed to achieve the world’s most ambitious climate target – 1.5 degrees Celsius above pre-industrial levels – may in fact be required to avoid more extreme climate change of 2 degrees Celsius.

The study, published Jan. 30 in Proceedings of the National Academy of Sciences, provides new evidence that global warming is on track to reach 1.5 degrees Celsius (2.7 Fahrenheit) above pre-industrial averages in the early 2030s, regardless of how much greenhouse gas emissions rise or fall in the coming decade.

The new “time to threshold” estimate results from an analysis that employs artificial intelligence to predict climate change using recent temperature observations from around the world.

A.I. used to predict space weather like Coronal Mass Ejections

 Dr Andy Smith of Northumbria University
Photo Credit: Northumbria University/Simon Veit-Wilson.

A physicist from Northumbria University has received over £500,000 to create AI that will safeguard the Earth from destructive space storms.

Coronal Mass Ejections, which are solar eruptions from the Sun, can send plasma hurtling towards Earth at high speeds. These space storms can cause severe disruptions to power grids and communication systems.

With our increasing reliance on technology, solar storms pose a serious threat to our everyday lives, leading to severe space weather being added to the UK National Risk Assessment for the first time in 2011.

Researcher and his team analyzed huge amounts of data from satellites and space missions over the last 20 years to gain a better understanding of the conditions under which storms are likely to occur.

Machine learning identifies drugs that could potentially help smokers quit

Penn State College of Medicine researchers helped identify eight medications that may be repurposed to help people quit smoking. A team of more than 70 researchers contributed to the analysis of genetic and smoking behavior data from more than 1.3 million people.
Image Credit: Scientific Frontline

Medications like dextromethorphan, used to treat coughs caused by cold and flu, could potentially be repurposed to help people quit smoking cigarettes, according to a study by Penn State College of Medicine and University of Minnesota researchers. They developed a novel machine learning method, where computer programs analyze data sets for patterns and trends, to identify the drugs and said that some of them are already being tested in clinical trials.

Cigarette smoking is risk factor for cardiovascular disease, cancer and respiratory diseases and accounts for nearly half a million deaths in the United States each year. While smoking behaviors can be learned and unlearned, genetics also plays a role in a person’s risk for engaging in those behaviors. The researchers found in a prior study that people with certain genes are more likely to become addicted to tobacco.

Using genetic data from more than 1.3 million people, Dajiang Liu, Ph.D., professor of public health sciences, and of biochemistry and molecular biology and Bibo Jiang, Ph.D., assistant professor of public health sciences, co-led a large multi-institution study that used machine learning to study these large data sets — which include specific data about a person’s genetics and their self-reported smoking behaviors.

MIT researchers develop an AI model that can detect future lung cancer risk

Caption:Researchers from Massachusetts General Hospital and MIT stand in front of a CT scanner at MGH, where some of the validation data was generated. Left to right: Regina Barzilay, Lecia Sequist, Florian Fintelmann, Ignacio Fuentes, Peter Mikhael, Stefan Ringer, and Jeremy Wohlwend
 Photo Credit: Guy Zylberberg.

The name Sybil has its origins in the oracles of Ancient Greece, also known as sibyls: feminine figures who were relied upon to relay divine knowledge of the unseen and the omnipotent past, present, and future. Now, the name has been excavated from antiquity and bestowed on an artificial intelligence tool for lung cancer risk assessment being developed by researchers at MIT's Abdul Latif Jameel Clinic for Machine Learning in Health, Mass General Cancer Center (MGCC), and Chang Gung Memorial Hospital (CGMH).

Lung cancer is the No. 1 deadliest cancer in the world, resulting in 1.7 million deaths worldwide in 2020, killing more people than the next three deadliest cancers combined. 

"It’s the biggest cancer killer because it’s relatively common and relatively hard to treat, especially once it has reached an advanced stage,” says Florian Fintelmann, MGCC thoracic interventional radiologist and coauthor on the new work. “In this case, it’s important to know that if you detect lung cancer early, the long-term outcome is significantly better. Your five-year survival rate is closer to 70 percent, whereas if you detect it when it’s advanced, the five-year survival rate is just short of 10 percent.” 

Why faces might not be as attention-grabbing as we think

Data from the study’s 30 participants revealed they looked at the faces of just 16 per cent of the people they walked past.
Photo Credit: John Cameron

Research combining wearable eye-tracking technology and AI body detection software suggests our eyes aren’t drawn to the faces of passers-by as much as previously thought.

Faces are key to everyday social interaction. Just a brief glance can give us important signals about someone’s emotional state, intentions and identity that helps us to navigate our social world.

But researchers studying social attention – how we notice and process the actions and behaviors of others in social contexts – have been mostly limited to lab-based studies where participants view social scenes on computer screens. Now, researchers from the School of Psychology at UNSW Science have developed a new approach that could enable more studies of social attention in natural settings.

The novel method correlates eye-movement data from wearable eye-tracking glasses with analysis from an automatic face and body detection algorithm to record when and where participants looked when fixating on other people. The methodology, detailed in the journal Scientific Reports, could have a range of future applications in settings from clinical research to sports science.