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Tissue aging involves complex changes in vascular, immune, and stromal compartments that collectively reshape the local tissue ecosystem and may contribute to age-related diseases, including cancer, fibrotic diseases, and cardiovascular disease. However, it remains unclear whether reproducible, cell-specific vascular niche states emerge during aging and whether these states can be therapeutically targeted.
MIT engineers have developed a flexible, shape-sensing sheet that digitally reconstructs its own form as it bends and twists. The sensor is similar in principle to some motion-capture garments, which track a person’s physical movements to mimic them in a virtual avatar. Those designs use rigid sensors that are stitched into a suit at strategic locations.
In contrast, the team’s new design uses multiple soft optical fibers that zigzag throughout a soft and stretchable sheet. When the sheet bends, the fibers bend in kind, changing the pattern of light that travels through them. The researchers developed an algorithm to interpret the bending light patterns on the fly to create a digital image that moves the same way the sheet is moving. The team also showed that the design is resistant to damage: Even when a few fibers are cut or disconnected, the remaining fibers can still accurately reconstruct the sheet’s overall shape.
“Our setup is targeting fully soft and stretchable sensing surfaces so it’s potentially safer for interacting with a human or a soft environment,” says Qifan Yu, a mechanical engineering graduate student at MIT.
The density of DREADDs expression in the zona incerta compared to the LH is rather surprising considering this has not been consistently reported in the tracing literature. Maybe a few related comments could be added.
Reviewer 2:
This study examines male rats for their responses to a Pavlovian conditioned approach paradigm, and the influence of the neuronal pathway from the lateral hypothalamus (LH) to the paraventricular nucleus of the thalamus (PVT) on these responses. From the Pavlovian conditioned approach paradigm, rats can be classified as sign-trackers (STs), goal-trackers (GTs), or intermediate responders (IRs), based on their level of approach and interaction with the cue compared to the location of reward delivery. With chemogenetic inhibition of the LH-PVT pathway, the authors find that goal-tracking but not sign-tracking can be attenuated, and that this primarily occurs in the IR population. In a control experiment, they find that injection of the DREADD actuator, CNO, does not significantly alter behavior. The authors conclude that the LH-PVT pathway is a selective contributor to reward-directed conditioned responding and a circuit substrate for behavioral flexibility.
Now, researchers at Pioneer Labs in Emeryville, California, have developed a microbe they suggest may be a useful, safe first step toward terraforming Mars.
“On Mars, nature will do the same jobs for us that she does on Earth — create fertile soil, shelter, oxygen and food,” Erika DeBenedictis, CEO of Pioneer Labs, told Space.com.
Odyssey-3: a new step toward world models for physical AI
Odyssey has unveiled Odyssey-3, its latest foundation world model, designed to learn how objects move, interact, and respond to actions over time. Unlike conventional video generation that primarily produces visual sequences, Odyssey-3 aims to generate interactive environments that evolve in response to human or AI actions, potentially providing a more useful foundation for physical AI.
Built as an autoregressive diffusion transformer, the model learns patterns of physics, dynamics, and cause-and-effect from video, annotated events, gameplay, and simulated physical interactions. Its applications include generating real-time environments, creating training grounds for AI agents, and adapting learned representations to control physical systems.
Odyssey reports that Odyssey-3 Pro achieved a score of 66.1 on the Physics-IQ Verified video-to-video benchmark, which the company describes as a state-of-the-art result. Its evaluations on WorldMark also placed it first in three of four environment categories. In demonstrations, policies built using Odyssey-3 have been applied to robot-arm manipulation, humanoid tasks, and autonomous driving. The company reports that its driving policy was trained using just 20 hours of driving data while keeping the model’s backbone frozen.
The broader ambition is to move world models beyond visual prediction toward systems that can help machines anticipate how their environments change and learn how to act within them. If these capabilities generalize reliably beyond demonstrations and benchmarks, world models could become useful infrastructure for robotics, autonomous systems, and training increasingly capable AI agents.
The important caveat: generating physically plausible video is not the same as possessing a complete or accurate model of the real world. Benchmark results and demonstrations are promising, but robust transfer to unfamiliar conditions, reliable long-horizon predictions, and safe real-world control still require independent validation.
#worldmodels #robotics #AutonomousSystems #ArtificialIntelligence
In other words, Paper2Agent is like a translator between a human-written paper and a chatbot.
That might sound redundant. After all, it’s already possible to upload a paper to ChatGPT, Claude, or another chatbot and ask questions. The difference is in the training: A chatbot can summarize a paper’s results, but it doesn’t have a deeper understanding of how those results came to be or whether the underlying analysis holds up.
By trying to replicate results based on the paper, Paper2Agent’s AI gets a sort of hands-on experience, potentially making it less prone to hallucination. The agents can “provide much more in-depth insights to the readers,” Zou told Nature.