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Small neuromorphic device ‘sees,’ makes ultra-fast decisions and creates memories

Researchers in Australia have created a small neuromorphic (brain-like) device that “sees” and stores memories in a similar way to human brains. It’s a step towards future devices that can make rapid, complex decisions — in self-driving cars, for example.

This invention is based on a single chip enabled by a sensing “doped indium oxide” element, which is thousands of times thinner than a human hair and requires no external parts to operate.

Rapid decision-making

The prototype device captures visual information, pre-packages and transmits it (like an optical nerve) for storage, and classifies it, using a memory system similar to a human brain’s.

Collectively, these functions could enable ultra-fast live decision making, the team says.

RMIT University engineers in Australia led the work, with contributions from researchers at Deakin University and the University of Melbourne.

Team leader Professor Sumeet Walia, from RMIT’s School of Engineering, said the new device can perform all necessary functions: sensing, creating and processing information, and retaining memories, rather than relying on external energy-intensive computation, which prevents real-time decision-making.

“We’ve made real-time decision-making a possibility with our invention. It doesn’t need to process large amounts of irrelevant data and it’s not being slowed down by data transfer to separate processors,” said Walia.

Their findings and analysis are published in the journal Advanced Functional Materials.

Potential applications

The team’s device mimicks the retina’s capabilities to identify objects, colors and other visual features and to store and process visual information, Walia explained.

The team used ultraviolet light as part of their experiments, and is are now working to expand this technology to visible and infrared light, with many possible applications, such as autonomous operations in dangerous environments, shelf-life assessments of food and advanced forensics.

“Imagine a self-driving car that can see and recognize objects on the road in the same way that a human driver can. Or being able to able to rapidly detect and track space junk.”

Walia said neuromorphic systems could also adapt to new situations over time, becoming more efficient with more experience. “Traditional computer vision systems are typically programmed with specific rules and can’t adapt as easily,” he said.

“Neuromorphic robots have the potential to run autonomously for long periods, in dangerous situations where workers are exposed to possible cave-ins, explosions and toxic air.”

Citation: Mazumder, A., Nguyen, C. K., Aung, T., Low, M. X., Rahman, M. A., Russo, S. P., Tawfik, S. A., Wang, S., Bullock, J., Krishnamurthi, V., Syed, N., Ranjan, A., Zavabeti, A., Abidi, I. H., Guo, X., Li, Y., Ahmed, T., Daeneke, T., Al-Hourani, A., . . . Walia, S. Long Duration Persistent Photocurrent in 3 nm Thin Doped Indium Oxide for Integrated Light Sensing and In-Sensor Neuromorphic Computation. Advanced Functional Materials, 2303641. https://doi.org/10.1002/adfm.20

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First human ‘synthetic embryos’ created

Magdalena Zernicka-Goetz, Professor of Development and Stem Cells at the University of Cambridge, announced June 14 at the International Society for Stem Cell Research annual meeting in Boston that her team had grown the first human “synthetic embryos” (made from stem cells) *

She also allowed them to grow to a stage equivalent to just past 14 days old (an internationally recognized ethical limit called the “14-day rule,” based on ethical issues regarding the possibility of growing into a human fetus). “In real embryos this [is equivalent to] a stage between day 7/8 and day 14,” notes Żernicka-Goetz.

Moral quandaries

“Just as there are real possibilities for gaining knowledge from synthetic human-like embryos, there are also real moral quandaries,” says Kathryn MacKay, Senior Lecturer in Bioethics, University of Sydney, writing in The Conversation.

“One of these quandaries arises around whether their creation really gets us away from the use of human embryos.

“Robin Lovell-Badge, the head of stem cell biology and developmental genetics at the Francis Crick Institute in London UK, reportedly said that if these human-like embryos can really model human development in the early stages of pregnancy, then we will not have to use human embryos for research.

“At the moment, it is unclear if this is the case for two reasons.

“First, the embryos were created from human embryonic stem cells, so it seems they do still need human embryos for their creation. Perhaps more light will be shed on this when Żernicka-Goetz’s research is published.

“Second, there are questions about the extent to which these human-like embryos really can model human development.

“At the moment, animal models of similar synthetic embryos suggest they are not capable of developing into a full living being. Studies in mice and monkeys have so far shown that the synthetic embryos die a short while after being implanted into a female’s womb, which means they are not viable.

“There could be significant limits to the usefulness of these synthetic embryos for learning about human developmental issues, if human-like synthetic embryos aren’t capable of developing into full human babies and do not form important body structures like a beating heart and a brain.

“One of the reasons researchers want to use these embryos is for research into miscarriage and developmental anomalies. This is very important, but will these synthetic embryos be “close enough” to real human embryos to reveal useful answers?

“Scientists may still rely on the use of human embryos if we do need human embryos for the creation of these models, or there are research questions that these synthetic embryos can’t address.

*What are synthetic embryos, exactly?

“The term is somewhat misleading as these structures aren’t really synthetic, nor are they exactly the same as embryos,” observes Clare Wilson in New Scientist. “They are similar to early embryos, a tiny ball of cells arising from a sperm fertilising an egg, but created from stem cells grown in the lab.”

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AI algorithms find three drugs that could combat aging

Three “senolytics”—compounds in drugs that could help stave off the effects of aging (such as cancer, type-2 diabetes, osteoarthritis and viral infection)—have been discovered by researchers at the University of Edinburgh.

These algorithms were trained on already-published data, making them cost-effective, the researchers note.

Lab tests in human cells revealed that three of the compounds—ginkgetin, periplocin and oleandri—were able to remove senescent* cells without damaging healthy cells.

All three are natural products found in traditional herbal medicines, the team says. Oleandri was found to be more effective than the best-performing known senolytic drug of its kind.

*Cellular senescence is “a stress response involved in aging and diverse disease processes, including cancer, type-2 diabetes, osteoarthritis and viral infection,” the researchers explain.

Citation: Quintanilla, A., Elliott, R. J., Dawson, J. C., Sun, J., Campa, V. M., Carragher, N. O., Acosta, J. C., & Oyarzún, D. A. (2023). Discovery of senolytics using machine learning. Nature Communications, 14(1), 1-15. https://doi.org/10.1038/s41467-023-39120-1 (open-access)

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Taurine may be a key to longer and healthier life

A deficiency of taurine—a nutrient produced in the body and found in many foods—is a driver of aging in animals. That’s a key finding of a new study led by Columbia University researchers, involving dozens of aging researchers around the world. It was published June 8 in the journal Science.

“This study suggests that taurine could be an elixir of life within us that helps us live longer and healthier lives,” says the study’s leader, Vijay Yadav, PhD, assistant professor of genetics & development at Columbia University Vagelos College of Physicians and Surgeons.

In mice and monkeys, supplementing with taurine increased lifespan

Experts found that at age 2 (60 in human years), mice supplemented with taurine for one year were healthier than untreated mice in almost every way.

They saw similar health effects in middle-aged rhesus monkeys, which were given daily taurine supplements for six months. Taurine prevented weight gain, reduced fasting blood glucose and markers of liver damage, increased bone density in the spine and legs, and improved the health of their immune systems.

Promising anti-aging strategy for humans

The researchers don’t know yet if taurine supplements will improve health or increase longevity in humans, but two experiments they conducted suggest taurine has potential. “Taurine abundance goes down with age, so restoring taurine to a youthful level in old age may be a promising anti-aging strategy.”

Other potential anti-aging drugs—including metformin, rapamycin, and NAD analogs—are also being considered for testing in clinical trials.

Foods high in taurine

According to WebMD, turkey (dark meat) has the highest taurine content of any animal meat. Other foods with high taurine include tuna (dark meat), tilapia fish (dark meat), octopus, chicken (dark meat), seaweed, and beef. Sources for this data include the American Heart Association, Harvard Medical School, and Frontiers in Physiology.

Citation: Singh, P., Gollapalli, K., Mangiola, S., Schranner, D., Yusuf, M. A., Chamoli, M., Shi, S. L., Bastos, B. L., Nair, T., Riermeier, A., Vayndorf, E. M., Wu, J. Z., Nilakhe, A., Nguyen, C. Q., Muir, M., Kiflezghi, M. G., Foulger, A., Junker, A., Devine, J., Yadav, V. K. (2023). Taurine deficiency as a driver of aging. Science. https://www.science.org/doi/10.1126/science.abn9257

What are your thoughts on taurine as a key to a longer and healthier life? Other ideas? Please comment below!

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Chatbot designs robot, raises questions

Researchers from TU Delft and EPFL claim they’re the first to co-design a robot with ChapGPT.

“We wanted ChatGPT to design not just a robot, but one that is actually useful,” says TU Delft assistant professor Cosimo Della Santina. So with PhD student Francesco Stella and Josie Hughes from EPFL, and following ChatGPT’s advice, the team chose food supply as their challenge. Specifically, creating a mobile tomato-harvesting robot.

The researchers followed all of ChatGPT’s design decisions. “ChatGPT extends the designer’s knowledge to other areas of expertise,” said Della Santina. “For example, the chat taught us which crop would be most economically valuable to automate, to “make the gripper out of silicone or rubber to avoid crushing tomatoes” and recommended that “a Dynamixel motor is the best way to drive the robot.”

ChatGPT as researcher/designer — is that a good thing?

In a paper just published in Nature Machine Intelligence, the researchers explore the varying degrees of cooperation between humans and Large Language Models (LLM). In this case, the LLM acts as both researcher and engineer, while the human acts as manager, in charge of specifying the design objectives. So that raises the question: is that desirable?

“In fact, LLM output can be misleading if it is not verified or validated,” Della Santina says. “AI bots are designed to generate the ‘most probable’ answer to a question, so there is a risk of misinformation and bias in the robotic field.” Working with LLMs also raises other important issues, such as plagiarism, traceability and intellectual property, she notes.

Della Santina, Stella and Hughes are continuing their study of LLMs to design new robots, including designing the robot’s own body. “Ultimately, an open question for the future of our field is how LLMs can be used to assist robot developers without limiting the creativity and innovation needed for robotics to rise to the challenges of the 21st century,” says Stella.

Citation: Stella, F., Della Santina, C., & Hughes, J. (2023). How can LLMs transform the robotic design process? Nature Machine Intelligence, 1-4. https://doi.org/10.1038/s42256-023-00669-7

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Generative AI Model Radically Improves Electrocardiogram Diagnoses

Researchers at the Mount Sinai Health System have developed an innovative generative AI transformer model that improves the accuracy and effectiveness of ECG (electrocardiogram)-related diagnoses.

In a study published in the June 6 online issue of npj Digital Medicine, the team reported that its new deep-learning model, HeartBEiT, surpassed established commonly used AI methods for ECG analysis — convolutional neural networks (CNNs), used for computer-vision tasks.

Researchers pre-trained HeartBEiT on 8.5 million ECGs from 2.1 million patients collected over four decades from four hospitals within the Mount Sinai Health System. Then they tested the model’s performance against CNN architectures commonly used in cardiac diagnostic areas.

The study found that HeartBEiT had “significantly higher performance at lower sample sizes, along with better ‘explainability’ and can perform as well as, if not better than, these methods, using a tenth of the data,” according to Akhil Vaid, MD, Instructor of Data-Driven and Digital Medicine (D3M) at the Icahn School of Medicine at Mount Sinai.

The researchers tested the model on three tasks: learning if a patient is having a heart attack; if they have a genetic disorder called hypertrophic cardiomyopathy; and how effectively their heart is functioning. “In each case, our model performed better than all other tested baselines.”

This study was funded by the National Heart, Lung, and Blood Institute of the NIH and by the National Center for Advancing Translational Sciences of the NIH.

Citation: Vaid, A., Jiang, J., Sawant, A., Lerakis, S., Argulian, E., Ahuja, Y., Lampert, J., Charney, A., Greenspan, H., Narula, J., Glicksberg, B., & Nadkarni, G. N. (2023). A foundational vision transformer improves diagnostic performance for electrocardiograms. Npj Digital Medicine, 6(1), 1-8. https://doi.org/10.1038/s41746-023-00840-9 (open-access)

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In a first, a space solar power system has wirelessly transmitted power in space

A Caltech space solar power prototype, launched into orbit in January, is now operational, and Caltech engineers have demonstrated its ability to beam detectable solar power to Earth for the first time.

A Caltech ground station on Earth confirmed the transmission, according to Ali Hajimiri, Bren Professor of Electrical Engineering and Medical Engineering and co-director of the Caltech Space Solar Power Project (SSPP).

“When fully realized, Caltech’s SSPP will deploy a constellation of modular spacecraft which will collect sunlight, transform it into electricity, then convert it to microwaves that will be transmitted wirelessly over long distances to wherever it is needed — including locations that currently have no access to reliable power,” he said.

The ultimate goal: “A world powered by uninterruptible renewable energy.”

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Accelerated wound-healing ink uses 3D-printing pen

Researchers in China and Singapore have developed a wound-healing ink that can actively encourage the body to heal. The ink can be spread into a cut of any shape using a standard 3D-printing pen. In mice, the technology nearly completely repaired wounds in just 12 days.

This method accelerates the natural healing process, in which extracellular vesicles (EVs) secreted from white blood cells play important roles in promoting blood vessel formation and reducing inflammation during healing.

PAINT treatment process

(credit: ACS Applied Materials & Interfaces)

The team developed a system called PAINT (portable bioactive ink for tissue healing) using EVs secreted from macrophages combined with sodium alginate. These components were combined in a 3D-printing pen, mixing at the pen’s tip and forming a sturdy gel at the site of injury within three minutes.

The EVs promoted blood vessel formation and reduced inflammatory markers in human epithelial cells, shifting them into the “proliferative” (growth) phase of healing.

Citation. Paintable Bioactive Extracellular Vesicle Ink for Wound Healing. ACS Appl. Mater. Interfaces 2023, 15, 21, 25427–25436. May 19, 2023. DOI: 10.1021/acsami.3c03630

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Using generative AI to create better, more potent medicines

Researchers at The Ohio State University have created an AI framework called G2Retro that automatically generates reactions for any given molecule. 

G2Retro covers an enormous range of possible chemical reactions and can accurately and quickly determine which reactions might work best.

The study was published May 30 in the journal Communications Chemistry.

The researchers trained G2Retro on a dataset that contains 40,000 chemical reactions collected between 1976 and 2016. The framework “learns” from graph-based representations of given molecules, and uses deep neural networks to generate possible reactant structures that could be used to synthesize them.

Hundreds of new reaction predictions

Given a molecule, G2Retro can come up with hundreds of new reaction predictions in only a few minutes, said Xia Ning, PhD, lead author of the study, an associate professor of computer science and engineering at Ohio State and an associate professor of biomedical informatics in the College of Medicine.

“G2Retro is able to supply multiple synthesis routes and options, as well as a way to rank different options for each molecule,” said Dr. Ning. “This is not going to replace current lab-based experiments, but it will offer more and better drug options so experiments can be prioritized and focused much faster.”

This research was supported by the National Science Foundation and Ohio State’s President’s Research Excellence Program.

Citation: Chen, Z., Ayinde, O. R., Fuchs, J. R., Sun, H., & Ning, X. (2023). G2Retro as a two-step graph generative models for retrosynthesis prediction. Communications Chemistry, 6(1), 1-19. https://doi.org/10.1038/s42004-023-00897-3 (open-access).

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Ultra-low-cost blood-pressure monitor clips on a smartphone

Engineers at the University of California San Diego have developed a simple, low-cost clip that uses a smartphone’s camera and flash to instantly monitor blood pressure at the user’s fingertip.

The clip works with a custom smartphone app and could cost only 10 cents apiece when manufactured at scale.

“Because of their low cost, these clips could be handed out to anyone who needs them but cannot go to a clinic regularly,” said study senior author Edward Wang, a professor of electrical and computer engineering at UC San Diego and director of the Digital Health Lab. “A blood pressure monitoring clip could be given to you at your checkup, much like how you get a pack of floss and toothbrush at your dental visit.”

The research was published today in Nature Scientific Reports.

When the user presses on the clip, the smartphone’s flash lights up the fingertip. That light is then projected through a pinhole-sized channel to the camera as an image of a red circle. The smartphone app extracts the circle, measures the amount that the user’s fingertip applies, and its algorithm converts this data into systolic and diastolic blood pressure readings (credit: Digital Health Lab / UC San Diego)

Citation: Xuan, Y., Barry, C., De Souza, J., Wen, J. H., Antipa, N., Moore, A. A., & Wang, E. J. (2023). Ultra-low-cost mechanical smartphone attachment for no-calibration blood pressure measurement. Nature Scientific Reports, 13(1), 1-11. https://doi.org/10.1038/s41598-023-34431-1 (open access)

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