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NASA Selects GIMLI Payload to Probe Hidden Lunar Caves

NASA has selected the GIMLI payload, equipped with a lander, rover, gravimeter, seismic sensors, and ground-penetrating radar, to investigate whether the Moon’s Marius Hills Pit expands into subsurface lava caves. [ https://www.labroots.com/trending/space/31165/nasa-selects-g…ar-caves-2](https://www.labroots.com/trending/space/31165/nasa-selects-g…ar-caves-2)

What new methods can be developed to explore lava caves on the Moon? This is what a new NASA-approved mission hopes to address as a team of researchers from the Planetary Science Institute (PSI) proposed a new method for exploring lunar lava caves. This proposal has the potential to help scientists, engineers, and future astronauts use new methods for exploring the Moon’s subsurface environment.

For the project, NASA selected GIMLI (Geophysical Instruments for Marius Lunar pit Investigation), whose primary mission objective will be to explore the size of the subsurface lava caves compared to the small entrance holes on the surface. GIMLI’s primary location will be the Moon’s Marius Hills Pit, with the Marius Hills consisting of large volcanic domes averaging several hundred feet in height. The pit was discovered in 2009 by discovered in 2009 by Japan’s SELENE (Kaguya) spacecraft and its opening’s width measures about 213 feet (65 meters) with an estimated depth of about 111 to 131 feet (34 to 40 meters). The primary conundrum has been trying to ascertain whether this pit expands into a larger cavern beneath the surface.

To accomplish this, GIMLI, which will comprise a lander and rover, will use a myriad of instruments to examine how deep the pit goes and whether it expands into the subsurface. These instruments include a gravimeter, seismic sensors, and a ground-penetrating radar.

Star Slowly Devours Brown Dwarf Companion 300 Light-Years Away

Astronomers discovered a binary system 300 light-years away where a red dwarf star is actively stripping material from its closely orbiting brown dwarf companion. [ https://www.labroots.com/trending/space/31171/star-slowly-de…ight-years](https://www.labroots.com/trending/space/31171/star-slowly-de…ight-years)

What can a star consuming another star teach astronomers about star formation and evolution? This is what a recent study published in Nature Astronomy hopes to address as a team of scientists investigated the unique celestial event of larger star consuming a smaller star. This study has the potential to help scientists better understand how stars interact with planetary companions and what this could mean in the search for life beyond Earth.

For the study, the researchers examined the binary star, ZTF J0440+2325, which is located about 300 light-years from Earth and is comprised of a red dwarf star and a smaller brown dwarf. Red dwarf stars are both smaller and cooler than our Sun while brown dwarfs are planet-like objects larger than planets like Jupiter but did not reach a big enough size to begin the process of nuclear fusion to become a star.

The primary motivation behind the study was to fill a longstanding knowledge gap regarding peculiar, repeating data patterns of starlight changes that astronomers observed years ago. While astronomers have grown accustomed to seeing specific data patterns for celestial events like supernova or colliding neutron stars, whose data displays a bell curve, this new data displayed more like a triangle. The researchers pondered what could be causing this peculiar data, then studied the region where the signals were originating, discovering a red dwarf star and brown dwarf orbiting each other in only 87 minutes. Additionally, they discovered the material from the brown dwarf was being consumed by the larger red dwarf, resulting in regions of the red dwarf exhibiting higher temperatures.

Designing enzymes for new-to-nature chemistry and non-natural substrates with AlphaProtein Novo

Google Deepmind just broke chemistry…

They open-sourced a model that can invent completely new biology from scratch.

It’s called “AlphaProtein Novo”

For decades, scientists have dreamed of designing enzymes—nature’s tiny molecular machines—from scratch to perform chemistry that doesn’t exist in the natural world. Traditionally, protein engineers have had to start with enzymes that already exist in nature and tweak them, but finding the right natural starting point is often difficult, and there’s no guarantee nature has an enzyme for the job you want done.

A new approach called de novo design aims to build enzymes from first principles, like designing a tool for a specific task rather than adapting whatever happens to be in the toolbox. Until now, however, designed enzymes haven’t been good enough to be practically useful.

Enter AlphaProtein Novo (AP Novo), a machine-learning pipeline that changes the game. The researchers show, for the first time, that designing enzymes from scratch can actually beat searching through nature’s existing enzyme diversity when it comes to tackling hard chemistry problems.

Using AP Novo, the team designed entirely new enzymes for two impressive tasks. First, they created “nitrene transferases”—enzymes that don’t exist in nature—to build piperidine, a molecular building block important in many medicines, with exceptional precision in producing the desired form of the molecule. Second, they designed enzymes that can break down DEHP, a harmful environmental pollutant, under harsh conditions that would destroy natural enzymes.

They also achieved top-tier performance on two well-studied benchmark reactions. By analyzing what made these designs work, they discovered that two strategies were key: using predictions from AlphaFold 3 (an AI system that predicts protein structures) to guide designs based on chemical mechanisms, and scoring candidate enzyme scaffolds by looking at how ensembles of related sequences behave.

Continue reading “Designing enzymes for new-to-nature chemistry and non-natural substrates with AlphaProtein Novo” | >

Photonic neuromorphic learning via generalized in situ physical gradient descent

Photonic neuromorphic computing (using light to do neural-network math) promises big speed/energy wins, but there’s a catch: training has almost always happened in silico. You build a digital model of the chip, train it on a GPU, then transfer the weights to the physical device. That approach:

- Requires an accurate physics model of every component (expensive to build and validate)

- Breaks down when fabrication imperfections make the real chip deviate from the model.

- Doesn’t scale well as circuits get larger and more complex.

What INSPIRE does.

INSPIRE (IN-Situ Physical gRadient dEscent) is a general on-chip training method for photonic integrated circuits. The key mechanism is on-chip synthetic time-reversal holography — essentially exploiting optical reciprocity so that the physical system itself generates the gradient information:

1. Forward pass: light propagates through the circuit carrying your input.

Continue reading “Photonic neuromorphic learning via generalized in situ physical gradient descent” | >

Nobel Prize in Medicine: Karl Deisseroth, Peter Hegemann, Georg Nagel win for work on optogenetics

American Karl Deisseroth and Germans Peter Hegemann and Georg Nagel won the 2026 Nobel Prize in Physiology or Medicine on Monday (October 5) for their discoveries on light and optogenetics that have helped understand how the brain works.

The Nobel Assembly of Sweden’s Karolinska Institute medical university said Deisseroth, 54, Hegemann, 71, and Nagel, 73, were selected for their \.

Cognex RealSense Deal Prices The Factory’s New Edge Layer

Cognex is acquiring RealSense for roughly $500 million in cash.

RealSense builds 3D depth-sensing cameras with onboard AI processors designed for the factory edge. The devices can run vision models locally instead of sending frames back to a central system.

Projected 2026 revenue is $80–90 million, putting the deal at 5.6–6.3 times revenue. That multiple is the first clean public benchmark for what edge-AI-enabled sensing hardware is worth to a strategic acquirer.

Cognex frames the move as building a full-stack visual intelligence platform — from barcode and 2D inspection through 3D depth and robotic navigation.

For plant buyers, the practical takeaway is pricing transparency. The next time a vendor quotes edge-AI sensing hardware, there is now a disclosed M&A multiple to benchmark against — not just competing list prices.

Full analysis:

#IndustrialIoT #MachineVision #EdgeAI #SmartFactory

Continue reading “Cognex RealSense Deal Prices The Factory’s New Edge Layer” | >

Machine learning and computational approaches to model therapeutic response and resistance in diffuse midline glioma

Diffuse midline glioma (DMG) remains one of the most lethal cancers affecting children, adolescents and young adults and is near-universally resistant to treatment. Histone H3-alterations establish a profoundly dysregulated epigenetic landscape that promotes extensive intratumoral heterogeneity and cellular plasticity, key drivers of therapeutic resistance and treatment failure. Although single-cell RNA sequencing and spatial transcriptomics have transformed the study of tumor evolution, the mechanisms underpinning treatment resistance in DMG remain poorly understood. This Review examines how computational and machine learning-based approaches can be leveraged to study tumor adaptation under therapeutic pressure.

We provide an overview of computational frameworks developed for the analysis of single-cell and spatial transcriptomic datasets to model four major axes of tumor evolution: i) compositional shifts, ii) functional state remodeling, iii) tumor plasticity, and iv) intercellular communication; while highlighting how these approaches provided novel insights into DMG biology and the mechanisms underlying treatment adaptation.

Computational and machine learning-based frameworks provide powerful tools for modeling the spatiotemporal dynamics of tumor evolution in high-dimensional transcriptomic data. Across the four dimensions examined, these approaches identify resistant cellular populations, characterize adaptive transcriptional programs, reconstitute cell-state transitions, and map tumor-microenvironment interactions, revealing mechanisms of therapeutic resistance and treatment adaptation.

Engineers teach spacecraft to ‘dream’ their way to the space station

Docking with the ISS may seem simple. However, actually doing so shows how difficult orbital mechanics can be. It’s like traveling down a highway at 28,000 km/hr (17,000 mph) and parallel parking in an open garage on a multibillion-dollar laboratory traveling at the same speed. If you try to accelerate forward, you actually drift up, and there’s no air friction to naturally slow you down. Oh, and if you hit the lab, everyone aboard both your craft and the station dies, and the resulting debris field could wipe out dozens of satellites and even harm people on the ground. No pressure, obviously.

For decades, aerospace engineers have docked successfully using hard-coded physics equations and human pilots to make corrections. But now, a new paper posted to the arXiv preprint server from researchers at Stanford is taking a shot at building an AI to perform a series of “mental simulations” that could fundamentally change how future spacecraft interact with each other.

Their solution is called the Out-of-this-World-Model (OWM), but before we get to what that is, it’s best to recap how we typically navigate in low Earth orbit (LEO). Traditionally, navigation computers use a type of algorithm called a guidance, navigation and control (GNC) algorithm. They also take advantage of another mathematical tool called an extended Kalman filter, which helps them take in data from GPS receivers and star trackers and output thruster burn duty cycles.

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