A triple drug approach that blocks the KRAS pathway at three points eliminated pancreatic tumors and prevented resistance in mouse models.
Existing treatments for pancreatic cancer often stop working within a few months because tumors quickly develop resistance to the drugs. Researchers at Spain’s National Cancer Research Centre (CNIO) report that they have prevented this resistance in animal studies by using a three-drug combination therapy.
The researchers say their findings “pave the way for the design of combined therapies that may improve survival,” although they caution that this progress will not immediately translate into new treatments for patients. Mariano Barbacid, head of the Experimental Oncology Group at CNIO, emphasizes that “we are not yet in a position to carry out clinical trials with this triple therapy.”
In 2013, physicist Alex Wissner-Gross published a single equation for intelligence in [ITALIC] Physical Review Letters [/ITALIC]: # F = T∇Sτ
The force of an intelligent system equals its temperature — computational capacity, raw horsepower — multiplied by the gradient of its future option-space. Intelligence is not a mysterious property of carbon-based brains.
It is a physical force: the tendency of any sufficiently energetic system to maximize the number of future states accessible to it.
The equation was elegant. Correct. And incomplete.
It describes the force. It does not describe the geometry of the space through which that force navigates.
A gradient without a metric is a direction without distance — it tells the system where to push but not what distortion it will encounter on the way there.
We spent three years building the geometry. We tested it across 69 billion simulations. What we found changes everything. ## The Missing Geometry — From Force to Navigation.
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One of the most perplexing questions in the foundations of physics is how our shared sense of reality emerges out of quantum mechanics. This is because in quantum mechanics, it seems, different observers can arrive at different conclusions about what is real and what not. A group of physicists now used an approach called “Quantum Darwinism” to solve this tricky problem. At least they say they solved it. I am not so sure. Let’s have a look.
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/ @sabinehossenfelder 📚 Buy my book ➜ https://amzn.to/3HSAWJW #science #sciencenews #quantum #physics This video discusses the concept of “reality” in quantum physics, touching on how different observers can reach different conclusions. It features a presentation of a scientific paper on the “Metrological approach to the emergence of classical objectivity,” suggesting a potential solution to a long-standing problem in quantum mechanics. We explore how the “observer effect” and individual “consciousness” play a crucial role in shaping our understanding of “reality does not exist” within the realm of “quantum physics explained.” This deep dive connects the fundamental principles of “quantum mechanics” with profound questions in “philosophy.”
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#science #sciencenews #quantum #physics.
This video discusses the concept of \.
In statistics, Markov chain Monte Carlo (MCMC) is a class of algorithms used to draw samples from a probability distribution. Given a probability distribution, one can construct a Markov chain whose elements’ distribution approximates it – that is, the Markov chain’s equilibrium distribution matches the target distribution. The more steps that are included, the more closely the distribution of the sample matches the actual desired distribution.
Markov chain Monte Carlo methods are used to study probability distributions that are too complex or too high dimensional to study with analytic techniques alone. Various algorithms exist for constructing such Markov chains, including the Metropolis–Hastings algorithm.
These protein-like polymers may help target intractable cancer-causing proteins.
Researchers engineered polymers to target two notorious cancer-causing proteins by Sarah Braner.
Curiosity-driven research has long sparked technological transformations. A century ago, curiosity about atoms led to quantum mechanics, and eventually the transistor at the heart of modern computing. Conversely, the steam engine was a practical breakthrough, but it took fundamental research in thermodynamics to fully harness its power.
Today, artificial intelligence and science find themselves at a similar inflection point. The current AI revolution has been fueled by decades of research in the mathematical and physical sciences (MPS), which provided the challenging problems, datasets, and insights that made modern AI possible. The 2024 Nobel Prizes in physics and chemistry, recognizing foundational AI methods rooted in physics and AI applications for protein design, made this connection impossible to miss.
In 2025, MIT hosted a Workshop on the Future of AI+MPS, funded by the National Science Foundation with support from the MIT School of Science and the MIT departments of Physics, Chemistry, and Mathematics. The workshop brought together leading AI and science researchers to chart how the MPS domains can best capitalize on — and contribute to — the future of AI. Now a white paper, with recommendations for funding agencies, institutions, and researchers, has been published in Machine Learning: Science and Technology. In this interview, Jesse Thaler, MIT professor of physics and chair of the workshop, describes key themes and how MIT is positioning itself to lead in AI and science.
