Thousands of scientists are already experimenting with the AI to study cancer and neurodegenerative disorders.
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SOME PHYSICISTS SUGGEST GRAVITY ISN’T A FORCE AT ALL — BUT A QUANTUM ECHO OF ENTANGLEMENT
Gravity is the most familiar force in human experience, yet it remains the least understood at a fundamental level. Despite centuries of study—from Newton’s law of universal gravitation to Einstein’s general theory of relativity—gravity stubbornly resists unification with quantum mechanics. In recent decades, this tension has led some physicists to propose a radical rethinking of gravity’s nature. According to these ideas, gravity may not be a fundamental force at all, but instead an emergent effect arising from quantum entanglement and the flow of information in spacetime.
This perspective represents a profound conceptual shift. Rather than treating gravity as something particles “exert” on one another, these theories suggest it emerges statistically, much like temperature arises from the collective motion of atoms. This article examines the scientific foundations of this idea, the key theoretical frameworks supporting it, and the evidence—both suggestive and incomplete—that motivates such claims. By analyzing gravity through quantum, thermodynamic, and informational lenses, we gain insight into one of the most ambitious research directions in modern theoretical physics.
The Standard Model of particle physics successfully describes three of the four fundamental interactions: electromagnetism, the weak force, and the strong force. Gravity, however, remains outside this framework. Attempts to quantize gravity using the same methods applied to other forces lead to mathematical infinities that cannot be renormalized.
Quantifying the compressibility of the human brain
One of the most-viewed PNAS articles in the last week is “Quantifying the compressibility of the human brain.” Explore the article here: https://ow.ly/jGEu50Y6heQ
For more trending articles, visit https://ow.ly/FjuI50Y6heP.
In the human brain, the allowed patterns of activity are constrained by the correlations between brain regions. Yet it remains unclear which correlations—and how many—are needed to predict large-scale neural activity. Here, we present an information-theoretic framework to identify the most important correlations, which provide the most accurate predictions of neural states. Applying our framework to cortical activity in humans, we find that the vast majority of variance in activity is explained by a small number of correlations. This means that the brain is highly compressible: Only a sparse network of correlations is needed to predict large-scale activity. We find that this compressibility is strikingly consistent across different individuals and cognitive tasks and that, counterintuitively, the most important correlations are not necessarily the strongest.
Researchers identify new blood markers that may detect early pancreatic cancer
NIH-funded, four-marker panel could one day help catch one of deadliest cancers at more treatable stages.
National Institutes of Health (NIH)-supported investigators have developed a blood test to find pancreatic ductal adenocarcinoma, one of the deadliest forms of cancer. The new test could improve survival rates from pancreatic cancer, which tends to be diagnosed at late stages when therapy is less likely to be effective. The findings were published in Clinical Cancer Research.
Overall, only about 1 in 10 pancreatic cancer patients survive more than five years from diagnosis. However, experts expect that when the cancer is found and treated at an earlier stage, survival would improve. While finding the cancer early is key, there are no current screening methods to do so.
From Latent Manifolds to Targeted Molecular Probes: An Interpretable, Kinome-Scale Generative Machine Learning Framework for Family-Based Kinase Ligand Design
Newlypublished by gennady verkhivker, et al.
🔍 Key findings: Novel generative framework integrates ChemVAE-based latent space modeling with chemically interpretable structural similarity metric (Kinase Likelihood Score) and Bayesian optimization for SRC kinase ligand design, demonstrating kinase scaffolds spanning 37 protein kinase families spontaneously organize into low-dimensional manifold with chemically distinct carboxyl groups revealing degeneracy in scaffold encoding — local sampling successfully converts scaffolds from other kinase families into novel SRC-like chemotypes accounting for ~40% of high-similarity cutoffs.
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Scaffold-aware artificial intelligence (AI) models enable systematic exploration of chemical space conditioned on protein-interacting ligands, yet the representational principles governing their behavior remain poorly understood. The computational representation of structurally complex kinase small molecules remains a formidable challenge due to the high conservation of ATP active site architecture across the kinome and the topological complexity of structural scaffolds in current generative AI frameworks. In this study, we present a diagnostic, modular and chemistry-first generative framework for design of targeted SRC kinase ligands by integrating ChemVAE-based latent space modeling, a chemically interpretable structural similarity metric (Kinase Likelihood Score), Bayesian optimization, and cluster-guided local neighborhood sampling.
Paragon: Space-Charge-Neutralized Reflective Electron Projection Lithography
This is best exemplified by the RCA Permanent-Magnet Electron Microscope, based on the work of John H. Reisner and collaborators.
“The permanent magnet as an energizing source for magnetic electron lenses is not new. The use of a permanent magnetic yoke for the comparatively coarse focusing of cathode-ray tubes has long been known. The advantages of permanent magnet lens energization are very appealing — excellent stability (beyond the ability of any regulator), no heating losses in energizing coils, no need for extensive current supplies and regulators — advantages which heretofore were limited to electrostatic lenses.”
The Paragon idea is that “die at once” exposure is the key to high-volume manufacturing with electron projection lithography. Anything that would “reduce system throughput and/or require registration of plural exposures” is forbidden.
Ultrafast Movie Reveals Unexpected Plasma Behavior
Using a camera with 2-picosecond time resolution, researchers show that the atoms in a laser-induced plasma are more highly ionized than theory predicts.
With an astonishing 500 billion frames per second, a new movie captures the evolution of a laser-induced plasma, revealing that its atoms have lost more electrons—and thus have stronger interactions within the plasma—than models predict [1]. The movie relies on a ten-year-old technology, called compressed ultrafast photography (CUP), that packs all the information for hundreds of movie frames into a single image. The results suggest that models of plasma formation may need revising, which could have implications for inertial-confinement-fusion experiments, such as those at the National Ignition Facility in California.
Dense plasmas occur in many astrophysical settings and laboratory experiments. Their behavior is difficult to predict, as they often change on picosecond (10−12 s) timescales. A traditional method for probing this behavior is to use a streak camera, which collects a movie on a single image by capturing a small slice of each movie frame. “It’s one picture, but every line occurs at a different time,” explains John Koulakis from UCLA. He and his colleagues have used streak cameras to study anomalous behavior in plasmas [2], but the small region of plasma visible with this technique left doubts about what they were seeing, he says.
Puzzling slow radio pulses are coming from space. A new study could finally explain them
Cosmic radio pulses repeating every few minutes or hours, known as long-period transients, have puzzled astronomers since their discovery in 2022. Our new study, published in Nature Astronomy today, might finally add some clarity.
Radio astronomers are very familiar with pulsars, a type of rapidly rotating neutron star. To us watching the skies from Earth, these objects appear to pulse because powerful radio beams from their poles sweep our telescopes—much like a cosmic lighthouse.
The slowest pulsars rotate in just a few seconds—this is known as their period. But in recent years, long-period transients have been discovered as well. These have periods from 18 minutes to more than six hours.
Why are Tatooine planets rare? General relativity explains why binary star systems rarely host planets
Astronomers have found thousands of exoplanets around single stars, but few around binary stars—even though both types of stars are equally common. Physicists can now explain the dearth.
Of the more than 4,500 stars known to have planets, one puzzling statistic stands out. Even though nearly all stars are expected to have planets and most stars form in pairs, planets that orbit both stars in a pair are rare.
Of the more than 6,000 extrasolar planets, or exoplanets, confirmed to date—most of them found by NASA’s Kepler Space Telescope and the Transiting Exoplanet Survey Satellite (TESS)—only 14 are observed to orbit binary stars. There should be hundreds. Where are all the planets with two suns, like Tatooine in Star Wars?