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Category: biotech/medical – Page 11

Heat-rechargeable design powers nanoscale molecular machines

Though it might seem like science fiction, scientists are working to build nanoscale molecular machines that can be designed for myriad applications, such as “smart” medicines and materials. But like all machines, these tiny devices need a source of power, the way electronic appliances use electricity or living cells use ATP (adenosine triphosphate, the universal biological energy source).

Researchers in the laboratory of Lulu Qian, Caltech professor of bioengineering, are developing nanoscale machines made out of synthetic DNA, taking advantage of DNA’s unique chemical bonding properties to build circuits that can process signals much like miniature computers. Operating at billionth-of-a-meter scales, these molecular machines can be designed to form DNA robots that sort cargos or to function like a neural network that can learn to recognize handwritten numerical digits.

One major challenge, however, has remained: how to design and power them for multiple uses.

New AI enhances the view inside fusion energy systems

Imagine watching a favorite movie when suddenly the sound stops. The data representing the audio is missing. All that’s left are images. What if artificial intelligence (AI) could analyze each frame of the video and provide the audio automatically based on the pictures, reading lips and noting each time a foot hits the ground?

That’s the general concept behind a new AI that fills in missing data about plasma, the fuel of fusion, according to Azarakhsh Jalalvand of Princeton University. Jalalvand is the lead author on a paper about the AI, known as Diag2Diag, that was recently published in Nature Communications.

“We have found a way to take the data from a bunch of sensors in a system and generate a synthetic version of the data for a different kind of sensor in that system,” he said. The synthetic data aligns with real-world data and is more detailed than what an actual sensor could provide. This could increase the robustness of control while reducing the complexity and cost of future fusion systems. “Diag2Diag could also have applications in other systems such as spacecraft and robotic surgery by enhancing detail and recovering data from failing or degraded sensors, ensuring reliability in critical environments.”

Dark matter detector succeeds in performing measurements with nearly no radioactive interference

In their search for dark matter, scientists from the XENON Collaboration are using one of the world’s most sensitive dark matter detectors, XENONnT at the Gran Sasso Laboratory of the National Institute of Nuclear Physics INFN in Italy, to detect extremely rare particle interactions. These could provide clues about the nature of dark matter. The problem, however, is that tiny amounts of natural radioactivity generate background events that can mask these weak signals.

The XENONnT experiment has made a breakthrough by significantly reducing one of the most problematic contaminants— , a radioactive gas. For the first time, the research team has succeeded in reducing the detector’s radon-induced radioactivity to a level a billion times lower than the very low natural radioactivity of the human body.

The underlying technology, which the XENONnT consortium reports in the current issue of the Physical Review X, was developed by a team led by particle physicist Prof Christian Weinheimer from the University of Münster.

Most effective digital interventions to stop smoking identified

Smoking remains one of the most deleterious habits for human health, as it is known to increase the risk of several life-threatening diseases, including lung and throat cancers, heart disease and strokes. While most smokers are well aware of its associated health risks, ceasing this habit can be a very difficult process.

Moreover, conventional programs for cessation, such as those based on psychotherapy or , are not financially or physically accessible for all individuals who wish to stop smoking. In recent years, behavioral scientists and psychologists have been working with engineers to create digital interventions that support people in their efforts to quit this unhealthy habit.

Researchers at Sichuan University in China have carried out a and meta-analysis of past research studies investigating the effectiveness of various digital interventions for smoking cessation. The results of their analyses, presented in a paper published in Nature Human Behavior, suggest that personalized and group-customized technology-based programs could be particularly beneficial for smokers who wish to quit, with middle-aged individuals responding better than younger populations.

Democratizing AI scientists using ToolUniverse

AI scientists are emerging computational systems that serve as collaborative partners in discovery. These systems remain difficult to build because they are bespoke, tied to rigid workflows, and lack shared environments that unify tools, data, and analyses into a common ecosystem. In omics, unified ecosystems have transformed research by enabling interoperability, reuse, and community-driven development; AI scientists require comparable infrastructure. We present ToolUniverse, an ecosystem for building AI scientists from any language or reasoning model, whether open or closed. TOOLUNIVERSE standardizes how AI scientists identify and call tools, integrating more than 600 machine learning models, datasets, APIs, and scientific packages for data analysis, knowledge retrieval, and experimental design. It automatically refines tool interfaces for correct use by AI scientists, creates new tools from natural language descriptions, iteratively optimizes tool specifications, and composes tools into agentic workflows. In a case study of hypercholesterolemia, ToolUniverse was used to create an AI scientist to identify a potent analog of a drug with favorable predicted properties. The open-source ToolUniverse is available at https://aiscientist.tools.

Engineers create first artificial neurons that could directly communicate with living cells

A team of engineers at the University of Massachusetts Amherst has announced the creation of an artificial neuron with electrical functions that closely mirror those of biological ones. Building on their previous work using protein nanowires synthesized from electricity-generating bacteria, the team’s discovery means that we could see immensely efficient computers built on biological principles which could interface directly with living cells.

“Our brain processes an enormous amount of data,” says Shuai Fu, a graduate student in electrical and engineering at UMass Amherst and lead author of the study published in Nature Communications. “But its power usage is very, very low, especially compared to the amount of electricity it takes to run a Large Language Model, like ChatGPT.”

The human body is over 100 times more electrically efficient than a computer’s electrical circuit. The is composed of billions of neurons, specialized cells that send and receive all over the body. While it takes only about 20 watts for your brain to, say, write a story, an LLM might consume well over a megawatt of electricity to do the same task.

Your pancreas may be making its own version of Ozempic

Alpha cells in the pancreas can produce GLP1, not just glucagon, offering a surprising backup system for blood sugar control.

Duke University scientists have discovered that pancreatic alpha cells, long believed to only produce glucagon, actually generate powerful amounts of GLP-1 — the same hormone mimicked by popular diabetes drugs like semaglutide (Ozempic and Wegovy). Even more surprisingly, when glucagon production is blocked, alpha cells “switch gears” and boost GLP-1 output, enhancing insulin release and blood sugar control.

A new study from Duke University School of Medicine is challenging long-standing views on blood sugar regulation — and pointing to a surprising new ally in the fight against type 2 diabetes.

Venus flytrap’s touch response traced to specialized ion channel in sensory hairs

Plants lack nerves, yet they can sensitively detect touch from other organisms. In the Venus flytrap, highly sensitive sensory hairs act as tactile sensing organs; when touched twice in quick succession, they initiate the closure cascade that captures prey. However, the molecular identity of the touch sensor has remained unclear.

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