HydroGym offers over 60 standardized reinforcement-learning flow-control environments and demonstrates zero-shot transfer to a 3D wing, cutting local skin friction by 38% and exploration costs by four orders of magnitude.
Scientists have long been fascinated by two promising classes of antibiotics that disable RNA polymerase (RNAP). They knew that these drugs could grind gene expression to a halt in several pathogens, including the bacterium behind tuberculosis, by binding to specific locations in the RNAP. But despite decades of study, a key question remained: what process are the drugs actually targeting?
Now, a new paper in PNAS solves that mystery and simultaneously uncovers a new element of basic biology. Using these antibiotics as tools to clarify the finer points of RNAP function, the researchers discovered that the enzyme works only if a certain moving part briefly swings into place to stabilize RNA synthesis—and that these drugs disable the enzyme by preventing that motion. The findings reveal a previously unknown mechanism of RNA synthesis shared across diverse forms of life and lay the groundwork for developing next-generation antibiotics.
“It’s sort of a two-for-one,” says the senior author. “We now know how these inhibitors work, and the inhibitors also revealed a conformational change in the active site that we didn’t know was important.”
McGill University researchers have developed a more energy-efficient method of building AI systems that are better at measuring—and indicating—their own uncertainty. This will help users determine when human oversight is needed, when additional data should be collected and when a model is being asked to work beyond the conditions it was trained for, the researchers said.
“Artificial intelligence systems now play a central role in daily life, from medical diagnosis and content moderation to autonomous driving and AI agents that act on our behalf,” said Mame Diarra Touré, lead author and Ph.D. candidate in the Department of Mathematics and Statistics. “As these systems take on more responsibility, they need to become more trustworthy. They should recognize when they are uncertain, rather than giving confident answers in situations where they may be wrong.”
The research was supervised by David A. Stephens, professor in the Department of Mathematics and Statistics. “Singular Bayesian Neural Networks,” by Mame Diarra Touré and Stephens, was presented at the Forty-Third International Conference on Machine Learning (ICML 2026).
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If the Federation can replicate food, clothing, furniture, spare parts, and even medical supplies, then why doesn’t it simply use replicators to become infinitely rich?
The answer is much more interesting than simply saying “money doesn’t matter in Star Trek.”
Replicators don’t create infinite wealth — they create abundance.
Once almost anyone can produce a particular object on demand, that object stops being scarce. And when scarcity disappears, so does much of its economic value.
But replicators can’t eliminate every kind of scarcity.
You can’t replicate land. You can’t replicate a historic location. You can’t instantly replicate decades of human experience, expertise, creativity, reputation, or time.
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Deborah Mash is a neuropharmacologist who holds the first FDA licence ever granted to give ibogaine to a human being, built one of America’s largest brain banks, and discovered noribogaine — the metabolite now entering its first US clinical trial. She has spent over thirty years on a drug that appears to interrupt addiction after a single dose. This April a US president named it specifically. This is the scientist who was right three decades early.
Expect to learn what Trump’s executive order actually changes for ibogaine, why Sasha Shulgin put ibogaine in a box of its own, how noribogaine blocks nicotine, alcohol, cocaine and opioid use in animal models, why ibogaine holds the brain’s neuroplasticity window open for four weeks when ketamine manages days, why some patients get the full therapeutic benefit with no psychedelic experience at all, why the reported ibogaine deaths are almost impossible to interpret, why more than 80% of her patients said they never wanted to repeat it, and much more…
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