Scientists use quantum biology, AI to sharpen genome editing tool

ORNL scientists developed a method that improves the accuracy of the CRISPR Cas9 gene editing tool used to modify microbes for renewable fuels and chemicals production. This research draws on the lab’s expertise in quantum biology, artificial intelligence and synthetic biology.
Illustration Credit: Philip Gray/ORNL, U.S. Dept. of Energy

Scientists at Oak Ridge National Laboratory used their expertise in quantum biology, artificial intelligence and bioengineering to improve how CRISPR Cas9 genome editing tools work on organisms like microbes that can be modified to produce renewable fuels and chemicals.

CRISPR is a powerful tool for bioengineering, used to modify genetic code to improve an organism’s performance or to correct mutations. The CRISPR Cas9 tool relies on a single, unique guide RNA that directs the Cas9 enzyme to bind with and cleave the corresponding targeted site in the genome. Existing models to computationally predict effective guide RNAs for CRISPR tools were built on data from only a few model species, with weak, inconsistent efficiency when applied to microbes.

“A lot of the CRISPR tools have been developed for mammalian cells, fruit flies or other model species. Few have been geared towards microbes where the chromosomal structures and sizes are very different,” said Carrie Eckert, leader of the Synthetic Biology group at ORNL. “We had observed that models for designing the CRISPR Cas9 machinery behave differently when working with microbes, and this research validates what we’d known anecdotally.”

Nanosatellite to Test Novel AI Technologies

Image Credit: Julius-Maximilians-Universität Würzburg

A new Würzburg space mission is on the home straight: The SONATE-2 nanosatellite will test novel artificial intelligence hardware and software technologies in orbit.

After more than two years of development, the nanosatellite SONATE-2 is about to be launched. The lift-off into orbit by a rocket is expected in March 2024. The satellite was designed and built by a team led by aerospace engineer Professor Hakan Kayal from Julius-Maximilians-Universität (JMU) Würzburg in Bavaria, Germany.

JMU has been developing small satellite missions for around 20 years. SONATE-2 now marks another high point.

The satellite will test novel artificial intelligence (AI) hardware and software technologies in near-Earth space. The goal is to use it to automatically detect anomalies on planets or asteroids in the future. The Federal Ministry of Economic Affairs is funding the project with 2.6 million euros.

The brain may learn about the world the same way some computational models do

Two new MIT studies offer evidence supporting the idea that the brain uses a process similar to a machine-learning approach known as “self-supervised learning.”
Illustration Credit: geralt

To make our way through the world, our brain must develop an intuitive understanding of the physical world around us, which we then use to interpret sensory information coming into the brain.

How does the brain develop that intuitive understanding? Many scientists believe that it may use a process similar to what’s known as “self-supervised learning.” This type of machine learning, originally developed as a way to create more efficient models for computer vision, allows computational models to learn about visual scenes based solely on the similarities and differences between them, with no labels or other information.

A pair of studies from researchers at the K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center at MIT offers new evidence supporting this hypothesis. The researchers found that when they trained models known as neural networks using a particular type of self-supervised learning, the resulting models generated activity patterns very similar to those seen in the brains of animals that were performing the same tasks as the models.

The findings suggest that these models are able to learn representations of the physical world that they can use to make accurate predictions about what will happen in that world, and that the mammalian brain may be using the same strategy, the researchers say.

AI Models Identify Biodiversity in Tropical Rainforests

The Banded Ground Cocoo (Neomorphus radiolosus, left) and the Purple Chested Hummingbird (Polyerata rosenbergi) are among the birds recorded in tropical reforestation plots in Ecuador.
Photo Credits: John Rogers / Martin Schaefer)

Animal sounds are a very good indicator of biodiversity in tropical reforestation areas. Researchers led by Würzburg Professor Jörg Müller demonstrate this by using sound recordings and AI models.

Tropical forests are among the most important habitats on our planet. They are characterized by extremely high species diversity and play an eminent role in the global carbon cycle and the world climate. However, many tropical forest areas have been deforested and overexploitation continues day by day.

Reforested areas in the tropics are therefore becoming increasingly important for the climate and biodiversity. How well biodiversity develops on such areas can be monitored very well with an automated analysis of animal sounds. This was reported by researchers in the journal Nature Communications.

A step towards AI-based precision medicine

Mika Gustafsson and David Martínez hope that AI-based models could eventually be used in precision medicine to develop treatments and preventive strategies tailored to the individual. 
Photo Credit: Thor Balkhed

Artificial intelligence, AI, which finds patterns in complex biological data could eventually contribute to the development of individually tailored healthcare. Researchers at LiU have developed an AI-based method applicable to various medical and biological issues. Their models can for instance accurately estimate people’s chronological age and determine whether they have been smokers or not.

There are many factors that can affect which out of all our genes are used at any given point in time. Smoking, dietary habits and environmental pollution are some such factors. This regulation of gene activity can be likened to a power switch determining which genes are switched on or off, without altering the actual genes, and is called epigenetics.

Researchers at Linköping University (LiU) have used data with epigenetic information from more than 75,000 human samples to train a large number of AI neural network models. They hope that such AI-based models could eventually be used in precision medicine to develop treatments and preventive strategies tailored to the individual. Their models are of the autoencoder type, that self-organizes the information and finds interrelation patterns in the large amount of data.

New dog, old tricks: New AI approach yields ‘athletically intelligent’ robotic dog

A doglike robot can navigate unknown obstacles using a simple algorithm that encourages forward progress with minimal effort.

Video Credit: Shanghai Qi Zhi Institute/Stanford University

With a simplified machine learning technique, AI researchers created a real-world “robodog” able to leap, climb, crawl, and squeeze past physical barriers as never before.

Someday, when quakes, fires, and floods strike, the first responders might be packs of robotic rescue dogs rushing in to help stranded souls. These battery-powered quadrupeds would use computer vision to size up obstacles and employ doglike agility skills to get past them.

Toward that noble goal, AI researchers at Stanford University and Shanghai Qi Zhi Institute say they have developed a new vision-based algorithm that helps robodogs scale high objects, leap across gaps, crawl under thresholds, and squeeze through crevices – and then bolt to the next challenge. The algorithm represents the brains of the robodog.

“The autonomy and range of complex skills that our quadruped robot learned is quite impressive,” said Chelsea Finn, assistant professor of computer science and senior author of a new peer-reviewed paper announcing the teams’ approach to the world, which will be presented at the upcoming Conference on Robot Learning. “And we have created it using low-cost, off-the-shelf robots – actually, two different off-the-shelf robots.”

Predictions of the effect of drugs on individual cells are now possible

How differently do various cancer cells respond to the effects of drugs? A new method from Zurich researchers now makes it possible to accurately predict the effect on individual cells.
Photo Credit: National Cancer Institute

Experts from ETH Zurich, the University of Zurich, and University Hospital Zurich have used machine learning to jointly create a innovative method. This new approach can predict how individual cells react to specific treatments, offering hope for more accurate diagnoses and therapeutics.

Cancer is triggered by changes in cells that lead to the proliferation of pathogenic tumor cells. In order to find the most effective combination and dosage of drugs, it is advantageous if physicians can see inside the body, so to speak, and determine what effect the drugs will have on individual cells.

An interdisciplinary research team of biomedical and computer scientists from ETH Zurich, the University of Zurich, and the University Hospital Zurich has now developed a machine learning approach that allows such cell changes and drug effects to be modelled and predicted with much greater accuracy and nuance than before.

AI copilot enhances human precision for safer aviation

With Air-Guardian, a computer program can track where a human pilot is looking (using eye-tracking technology), so it can better understand what the pilot is focusing on. This helps the computer make better decisions that are in line with what the pilot is doing or intending to do.
Illustration Credit: Alex Shipps/MIT CSAIL via Midjourney

Imagine you're in an airplane with two pilots, one human and one computer. Both have their “hands” on the controllers, but they're always looking out for different things. If they're both paying attention to the same thing, the human gets to steer. But if the human gets distracted or misses something, the computer quickly takes over.

Meet the Air-Guardian, a system developed by researchers at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). As modern pilots grapple with an onslaught of information from multiple monitors, especially during critical moments, Air-Guardian acts as a proactive copilot; a partnership between human and machine, rooted in understanding attention.

But how does it determine attention, exactly? For humans, it uses eye-tracking, and for the neural system, it relies on something called "saliency maps," which pinpoint where attention is directed. The maps serve as visual guides highlighting key regions within an image, aiding in grasping and deciphering the behavior of intricate algorithms. Air-Guardian identifies early signs of potential risks through these attention markers, instead of only intervening during safety breaches like traditional autopilot systems. 

Deciphering the secrets of the brain

Adrian Wanner is delighted with the exceptional international recognition from the US National Institute of Health (NIH).
Photo Credit: Scanderbeg Sauer Photography

PSI researchers are to receive funding from the US National Institutes of Health (NIH) as part of its “BRAIN Initiative”. Their aim is to produce a comprehensive map of a mouse’s brain.

Unlocking the secrets of the brain, especially its architecture and wiring, is one of the big challenges in modern life sciences. That is why the National Institutes of Health (NIH) in the USA, one of the world’s largest research agencies, has included this in its program. As part of the NIH BRAIN Initiative, a Swiss researcher has now been awarded a major grant of up to 2.6 million US dollars. The neurobiologist Adrian Wanner, a group leader at the Paul Scherrer Institute PSI, is the project’s principal investigator. Andreas Schaefer from the Francis Crick Institute in London is also closely involved.

The NIH’s decision to invest such a large sum in a project at a Swiss institute demonstrates the exceptional competitiveness of Swiss researchers and confirms PSI’s position as a center for world-class research. “For a young research group leader to receive such a large grant, especially from another country, is by no means commonplace; it testifies to his great scientific talent and the confidence that the international community has in Switzerland as a research location,” says Gebhard Schertler, Head of the Department of Biology and Chemistry, who is delighted with the good news from the United States. Schaefer adds, “This funding will further strengthen the existing collaboration between our groups and institutes.”

Researchers Develop AI Model to Improve Tumor Removal Accuracy During Breast Cancer Surgery

Radiology-specific
Image Credit: Courtesy of UNC School of Medicine

Kristalyn Gallagher, DO, Kevin Chen, MD, and Shawn Gomez, EngScD, in the UNC School of Medicine have developed an AI model that can predict whether or not cancerous tissue has been fully removed from the body during breast cancer surgery.

Artificial intelligence (AI) and machine learning tools have received a lot of attention recently, with the majority of discussions focusing on proper use. However, this technology has a wide range of practical applications, from predicting natural disasters to addressing racial inequalities and now, assisting in cancer surgery.

A new clinical and research partnership between the UNC Department of Surgery, the Joint UNC-NCSU Department of Biomedical Engineering, and the UNC Lineberger Comprehensive Cancer Center has created an AI model that can predict whether or not cancerous tissue has been fully removed from the body during breast cancer surgery. Their findings were published in Annals of Surgical Oncology.

New way of identifying proteins supports drug development

The illustration shows how different areas of PRC2 protein (the one on the right side) binds to survivin. The color pixel diagram shows binding strength to survivin. The bright pink pixels are the strongest binders.
Illustration Credit: Atsarina Larasati Anindya

All living cells contain proteins with different functions, depending on the type of cell. Researchers at the University of Gothenburg have discovered a way to identify proteins without even looking at their structure. Their method is faster, easier and more reliable than previous methods.

Currently, the general view is that each protein’s structure is what controls its function in cells. The atomic sequences, meaning how the atoms are arranged in the proteins, create the protein’s structure and shape. But there are many proteins that lack a well-defined structure.

Researcher Gergely Katona has developed a new method where proteins are scanned based on the number of amino acids (or the number of different atoms) they contain in order to identify them and their function instead of identifying them based on their structure. With this scanning method, the researchers were able to predict relatively reliably which combination of amino acids is needed to bind to the protein survivin. The outcome was a reliability of about 80 per cent, which is better than when you use the protein’s primary structures for identification. The results are now published in the scientific journal iScience.

AI helps show how the brain’s fluids flow

A video shows a perivascular space (area within white lines) into which the researchers injected tiny particles. The particles (shown as moving dots) are trailed by lines which indicate their direction. Having measured the position and velocity of the particles over time, the team then integrated this 2D video with physics-informed neural networks to create an unprecedented high-resolution, 3D look at the brain’s fluid flow system.
Video Credit: Douglas Kelley

New research targets diseases including Alzheimer’s.

A new artificial intelligence-based technique for measuring fluid flow around the brain’s blood vessels could have big implications for developing treatments for diseases such as Alzheimer’s.

The perivascular spaces that surround cerebral blood vessels transport water-like fluids around the brain and help sweep away waste. Alterations in the fluid flow are linked to neurological conditions, including Alzheimer’s, small vessel disease, strokes, and traumatic brain injuries but are difficult to measure in vivo.

A multidisciplinary team of mechanical engineers, neuroscientists, and computer scientists led by University of Rochester Associate Professor Douglas Kelley developed novel AI velocimetry measurements to accurately calculate brain fluid flow. The results are outlined in a study published by Proceedings of the National Academy of Sciences.

AI unlikely to gain human-like cognition, unless connected to real world through robots

Embodying AI in robots so they can interact with the world around them and evolve like the human brain does is the most likely way AI will develop human-like cognition
Photo Credit: Gerd Altmann

Connecting artificial intelligence systems to the real world through robots and designing them using principles from evolution is the most likely way AI will gain human-like cognition, according to research from the University of Sheffield.

• University of Sheffield researchers say artificial intelligence systems are unlikely to gain human-like cognition, unless they’re connected to the real world through robots and designed using principles from evolution 

• Current AI systems, such as ChatGPT, copy some processes in the human brain to use datasets to solve difficult problems, but Sheffield researchers say this form of disembodied AI is unlikely to resemble the complexities of real brain processing no matter how big these datasets become

• Biological intelligence - such as the human brain - is achieved through a specific architecture that learns and improves using its connections to the real world, but this is rarely used in the design of AI

• Embodying AI in robots so they can interact with the world around them and evolve like the human brain does is the most likely way AI will develop human-like cognition

New model offers a way to speed up drug discovery

Researchers can screen more than 100 million compounds in a single day — much more than any existing model.
Photo Credit: Myriam Zilles

Huge libraries of drug compounds may hold potential treatments for a variety of diseases, such as cancer or heart disease. Ideally, scientists would like to experimentally test each of these compounds against all possible targets, but doing that kind of screen is prohibitively time-consuming.

In recent years, researchers have begun using computational methods to screen those libraries in hopes of speeding up drug discovery. However, many of those methods also take a long time, as most of them calculate each target protein’s three-dimensional structure from its amino-acid sequence, then use those structures to predict which drug molecules it will interact with.

Researchers at MIT and Tufts University have now devised an alternative computational approach based on a type of artificial intelligence algorithm known as a large language model. These models — one well-known example is ChatGPT — can analyze huge amounts of text and figure out which words (or, in this case, amino acids) are most likely to appear together. The new model, known as ConPLex, can match target proteins with potential drug molecules without having to perform the computationally intensive step of calculating the molecules’ structures.

AI Predicts Future Pancreatic Cancer

Pancreatic cancer cells
Image Credit: National Cancer Institute

An artificial intelligence tool has successfully identified people at the highest risk for pancreatic cancer up to three years before diagnosis using solely the patients’ medical records, according to new research led by investigators at Harvard Medical School and the University of Copenhagen, in collaboration with VA Boston Healthcare System, Dana-Farber Cancer Institute, and the Harvard T.H. Chan School of Public Health.

The findings, published May 8 in Nature Medicine, suggest that AI-based population screening could be valuable in finding those at elevated risk for the disease and could expedite the diagnosis of a condition found all too often at advanced stages when treatment is less effective and outcomes are dismal, the researchers said. Pancreatic cancer is one of the deadliest cancers in the world, and its toll projected to increase.

Currently, there are no population-based tools to screen broadly for pancreatic cancer. Those with a family history and certain genetic mutations that predispose them to pancreatic cancer are screened in a targeted fashion. But such targeted screenings can miss other cases that fall outside of those categories, the researchers said.

“One of the most important decisions clinicians face day to day is who is at high risk for a disease, and who would benefit from further testing, which can also mean more invasive and more expensive procedures that carry their own risks,” said study co-senior investigator Chris Sander, faculty member in the Department of Systems Biology in the Blavatnik Institute at HMS. “An AI tool that can zero in on those at highest risk for pancreatic cancer who stand to benefit most from further tests could go a long way toward improving clinical decision-making.”

‘Raw’ data show AI signals mirror how the brain listens and learns

Researchers found strikingly similar signals between the brain and artificial neural networks. The blue line is brain wave when humans listen to a vowel. Red is the artificial neural network’s response to the exact same vowel. The two signals are raw, meaning no transformations were needed.
Illustration Credit: Courtesy Gasper Begus

New research from the University of California, Berkeley, shows that artificial intelligence (AI) systems can process signals in a way that is remarkably similar to how the brain interprets speech, a finding scientists say might help explain the black box of how AI systems operate.

Using a system of electrodes placed on participants’ heads, scientists with the Berkeley Speech and Computation Lab measured brain waves as participants listened to a single syllable — “bah.” They then compared that brain activity to the signals produced by an AI system trained to learn English.

“The shapes are remarkably similar,” said Gasper Begus, assistant professor of linguistics at UC Berkeley and lead author on the study published recently in the journal Scientific Reports. “That tells you similar things get encoded, that processing is similar. “

A side-by-side comparison graph of the two signals shows that similarity strikingly.

Study shows how machine learning can identify social grooming behavior from acceleration signals in wild baboons

Photo Credit: Charl Durand

Scientists from Swansea University and the University of Cape Town have tracked social grooming behavior in wild baboons using collar-mounted accelerometers.

The study, published in the journal Royal Society Open Science, is the first to successfully calculate grooming budgets using this method, which opens a whole avenue of future research directions.

Using collars containing accelerometers built at Swansea University, the team recorded the activities of baboons in Cape Town, South Africa, identifying and quantifying general activities such as resting, walking, foraging and running, and also the giving and receiving of grooming.

A supervised machine learning algorithm was trained on acceleration data matched to baboon video recordings and successfully recognized the giving and receiving grooming with high overall accuracy.

The team then applied their machine learning model to acceleration data collected from 12 baboons to quantify grooming and other behaviors continuously throughout the day and night-time.

Personalized Gut Microbiome Analysis for Colorectal Cancer Classification with Explainable AI

Explainable AI offers a promising solution for finding links between diseases and certain species of gut bacteria, finds a research team at Tokyo Tech. Using a concept borrowed from game theory, the researchers developed a framework that reveals which bacterial species are closely associated with colorectal cancer in individual subjects, providing a more reliable way to find and characterize disease subgroups and identify biomarkers in the gut microbiome.

The gut microbiome comprises a complex population of different bacterial species that are essential to human health. In recent years, scientists across several fields have found that changes in the gut microbiome can be linked to a wide variety of diseases, notably colorectal cancer (CRC). Multiple studies have revealed that a higher abundance of certain bacteria, such as Fusobacterium nucleatum and Parvimonas micra, is typically associated with CRC progression.

AI Tool Predicts Colon Cancer Survival, Treatment Response

New AI tool accurately predicts both overall survival and disease-free survival after colorectal cancer diagnosis.
Image Credit: bodymybody

A new artificial intelligence model designed by researchers at Harvard Medical School and National Cheng Kung University in Taiwan could bring much-needed clarity to doctors delivering prognoses and deciding on treatments for patients with colorectal cancer, the second deadliest cancer worldwide.

Solely by looking at images of tumor samples — microscopic depictions of cancer cells — the new tool accurately predicts how aggressive a colorectal tumor is, how likely the patient is to survive with and without disease recurrence, and what the optimal therapy might be for them.

Having a tool that answers such questions could help clinicians and patients navigate this wily disease, which often behaves differently even among people with similar disease profiles who receive the same treatment — and could ultimately spare some of the 1 million lives that colorectal cancer claims every year.

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.