Much as a pilot might practice maneuvers in a flight simulator, scientists might soon be able to perform experiments on a realistic simulation of the mouse brain. In a new study, Stanford Medicine researchers and collaborators used an artificial intelligence model to build a “digital twin” of the part of the mouse brain that processes visual information.

The digital twin was trained on large datasets of brain activity collected from the visual cortex of real mice as they watched movie clips. It could then predict the response of tens of thousands of neurons to new videos and images.

Digital twins could make studying the inner workings of the brain easier and more efficient.

Angiography is a widely used medical imaging technique that allows medical researchers and doctors to capture the vascular network (i.e., blood vessels) using contrast agents, substances that enhance the visibility of specific structures inside the body when exposed to X-rays or other imaging approaches. Conventional angiography techniques rely on contrast agents that are distributed through blood vessels, leveraging the natural flow of blood in the body.

Despite their widespread use, these approaches have significant limitations. For instance, they struggle to visualize upstream regions (i.e., regions in blood vessels that are against the direction of the blood flow) or areas that are blocked by materials (e.g., blood clots). This inability to visualize some regions limits the use of angiography for diagnosing and planning the treatment of some vascular conditions, including narrowed vessels, blood clots and abnormal connections between vessels.

Researchers at the Shenzhen Institute of Artificial Intelligence and Robotics for Society and the Chinese University of Hong Kong recently introduced a new method for exploring and reconstructing vascular networks utilizing swarms of magnetic microrobots. Their proposed approach, outlined in a paper published in Nature Machine Intelligence, enables the 3D imaging of vascular networks, including upstream regions and blocked areas.

Rapidly increasing data traffic is placing ever greater demands on the capacity of communication systems. In an article titled “Ultra-broadband optical amplification using nonlinear integrated waveguides” published in Nature, a research team from Chalmers University of Technology, in Sweden, introduce a new amplifier that enables the transmission of 10 times more data per second than those of current fiber-optic systems.

This amplifier, which fits on a small chip, holds significant potential for various critical laser systems, including those used in medical diagnostics and treatment.

The advancement of AI technology, the growing popularity of streaming services, and the proliferation of new smart devices are among the factors driving the expected doubling of data traffic by 2030. This surge is heightening the demand for communication systems capable of managing vast amounts of information.