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3Dprinted bridge points the way to greener construction

Concrete is the most widely used building material on Earth, and producing it is one of the largest single sources of carbon emissions. One promising way to reduce its environmental footprint is to 3D-print concrete, laying it down bead by bead like a giant icing-piping robot. This process eliminates the labor-intensive formwork of pouring it into molds, and places the material only where a structure needs it.

But many of the most efficient designs created by computers are impossible for today’s printers to build. Engineers use a technique called topology optimization to find the strongest structure that uses the least amount of material. But those mathematically ideal designs, with their intricate, spider-web shapes, don’t account for the physical limitations of large-scale concrete printers with their thick nozzles, limited turning, and need to print in one continuous motion.

Now a team of MIT researchers has developed a way to close that gap. Their framework, described in a new article in Additive Manufacturing, bakes a printer’s real fabrication limits directly into the optimization, so the design that comes out is one a machine can build and print with little or no manual redesign. They demonstrated it by designing, printing, and load-testing a 2.3-meter concrete bridge and found that today’s printing hardware, not the concrete itself, limits how light a structure can be.

Align to the Mind

Everyone agrees on the goal. Align AI to humanity.
It is the one sentence the labs, the governments, the ethicists, and the public all say together — the rare point of consensus in a field that agrees on nothing else.

It sounds unarguable, the way for the good of all always does.
Which is exactly why it deserves a second look, because the sentences no one questions are the ones doing the most work unseen.

Ask the obvious question and it comes apart in your hands: whose humanity?
There is no single will called humanity to align anything to.
There is a planet of eight billion minds sorted into tribes that distrust one another, nations that arm against one another, faiths and ideologies that define themselves by what they oppose.

Ovaries may take on job in immune system after their tenure as reproductive organs

For most women, the body begins to change dramatically in their 40s or 50s. This transition, known as menopause, is defined as 12 consecutive months without a menstrual period, marking the end of the reproductive years. While researchers are aware of the functions the ovaries perform during active reproductive years, what happens to the organ after menopause is largely a mystery.

A recent study in Molecular Human Reproduction investigated what happens to the ovary in mice after it stops producing eggs, a period known as the post-reproductive stage, similar to menopause in humans.

Researchers found that even after the ovary can no longer support reproduction, it doesn’t simply become inactive. Instead, aging ovaries undergo remarkable changes, producing a different set of signaling molecules from those of younger ovaries.

Dendrites may be key to learning and memory, study suggests

Branchlike structures called dendrites that extend from neurons appear to make their own computations independent of the cell body, helping individual brain cells store memories of the past, respond to the present and anticipate the future, a study led by UT Southwestern Medical Center researchers suggests.

The findings, published in Science, represent a paradigm shift in current models of how learning and memory take place.

“This shifts our entire perspective. Rather than acting as simple switches, neurons behave more like sophisticated processors with internal divisions of labor, dramatically increasing the brain’s computational capacity,” said Attila Losonczy, M.D., Ph.D., professor at the Peter O’Donnell Jr. Brain Institute of Neuroscience and director of the Program in Memory Longevity (PML) at UT Southwestern.

Autonomous biomedical research with an artificial intelligence agent

Biomedical research is increasingly constrained by repetitive, fragmented workflows that slow discovery. We introduce Biomni, a general-purpose biomedical artificial intelligence agent that autonomously executes diverse research tasks. To map the biomedical action space, Biomni’s action-discovery agent mines tools, databases, and protocols from thousands of publications across 25 domains, building a unified agentic environment. Its general-purpose architecture integrates large language model reasoning with retrieval-augmented planning and code-based execution, dynamically composing workflows without predefined templates. Systematic benchmarking shows strong generalization across heterogeneous tasks—causal gene prioritization, drug repurposing, rare-disease diagnosis, microbiome analysis, and molecular cloning—without task-specific tuning.

Human-machine learning boosts noninvasive brain-computer control in untrained users

Implantable devices in the brain have been used for about 30 years to assist people with disabilities in completing motor tasks. However, the devices are simply not accessible to the vast majority of people who need help. Despite decades of work in this field, fewer than 100 people worldwide have benefited from the technology. The costs are prohibitive, and the brain surgeries are inherently risky.

That’s why Carnegie Mellon researchers, including Bin He, professor of biomedical engineering, electrical and computer engineering, and the Neuroscience Institute, have long been working on noninvasive brain-computer interfaces (BCIs) to develop technology that is less expensive, safer and more accessible to a wider population. Over the past 10 to 15 years, they have used noninvasive BCIs to fly a drone, control a robotic arm, maintain continuous control of a robotic arm and, most recently, complete fine motor tasks at the finger level. Yet the accuracy and level of control using noninvasive technology remain challenging.

Webb telescope discovers hidden planet in famous star system

Astronomers using NASA’s James Webb Space Telescope have discovered a giant planet outside our solar system, called an exoplanet, hiding within one of the most intensely studied planetary systems in the Milky Way galaxy.

The young, nearby star Beta Pictoris was already known to host two giant planets: Beta Pictoris b, one of the first exoplanets ever directly imaged, and Beta Pictoris c. The newly identified Beta Pictoris d makes it only the second planetary system known to contain at least three imaged planets.

Unlike Beta Pictoris b and c, however, Beta Pictoris d was discovered not by identifying a bright point of light but by detecting the unique chemical fingerprint of its atmosphere, a technique that could transform the search for worlds around other stars.

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