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Corrected microbial family tree offers statistically sound model for how earliest life forms evolved

In this era of Big Data, the prevailing wisdom is that more information leads to better answers. However, a new Canadian study shows that in the hunt for life’s ancient ancestors, more data can actually lead to less truth. Published in the Proceedings of the National Academy of Sciences, the research by UdeM associate professor of computer science Miklós Csűrös reveals that standard methods for reconstructing the genomes of ancient microbes are being overwhelmed by an explosion of information.

This paradox causes current models to “hallucinate” evolutionary events—specifically, an implausibly high number of horizontal gene transfers—that are actually just statistical ghosts, the study shows.

In it, Csűrös identifies a crisis point in evolutionary biology: As researchers try to reconcile thousands of gene sequences across the entire tree of life, the actual evolutionary signal begins to vanish, replaced by mathematical noise.

How do flocking birds and schools of fish move? New research offers crystal-clear answer

Flocking birds and schools of fish are a familiar sight. While previous research has uncovered the broad dynamics driving these movements, their underlying intricacies remain a mystery. Now a study by a team of New York University mathematicians offers new insights into these phenomena. It reveals that flocks and schools behave in ways similar to a soft crystalline material, with individual birds and fish serving as “atoms” that are evenly spaced in a lattice-like formation.

The findings, reported in the journal Physical Review Fluids, offer detailed insights into the hydrodynamic and aerodynamic interactions crucial in aerospace and automotive engineering, robotics and energy harvesting.

“Our findings offer a new way to understand how animal collectives coordinate movement and respond to their environment,” says Christiana Mavroyiakoumou, a researcher at NYU’s Courant Institute School of Mathematics, Computing, and Data Science at the time of the study and now a fellow at Oxford University’s Mathematical Institute. “More specifically, lines of birds or fish behave like an elastic material with regularly spaced individuals held together by flexible, or spring-like, bonds—akin to soft crystalline substances in which atoms are arranged in an orderly, repeating pattern.”

How Divergence and Curl Were Discovered

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This video is about how Divergence and Curl, along with the theory of Vector Analysis was discovered.

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Image Credits:
https://commons.wikimedia.org/wiki/Fi…, https://creativecommons.org/licenses/.… Approaching a Black Hole: NASA’s Scientific Visualization Studio — Caltech-IPAC/Robert Hurt, Caltech-IPAC/Keith Miller, NASA/JPL/Chelsea Gohd, Global Science and Technology, Inc./Ella Kaplan, NASA/GSFC/Mark SubbaRao Many more images that are public domain from wikimedia commons _____ Sources: Vector, A Surprising Story of Space, Time, and Mathematical Transformation by Robyn Arianrhod A History of Vector Analysis by Michael J. Crose Maxwell’s Treatise on Electricity and Magnetism + A Dynamical Theory of the Electromagnetic Field Great videos by Kathy Loves Physics: • Quaternions are Amazing and so is William…, • How Maxwell’s Equations (and Quaternions)… _____ Corrections: 15:12 — on screen it should read “born in Scotland 1831″ instead of 1931 _____ Music: Epidemic Sound Animations created using Manim: https://www.manim.community/ Illustrations and Thumbnails: Christine Kosakowski This video was sponsored by Surfshark.
https://commons.wikimedia.org/wiki/Fi…, https://creativecommons.org/licenses/.
Approaching a Black Hole: NASA’s Scientific Visualization Studio — Caltech-IPAC/Robert Hurt, Caltech-IPAC/Keith Miller, NASA/JPL/Chelsea Gohd, Global Science and Technology, Inc./Ella Kaplan, NASA/GSFC/Mark SubbaRao.

Many more images that are public domain from wikimedia commons.

Penrose vs EWOG: Consciousness and Quantum Collapse

Consciousness beyond penrose quantum microtubules?utm_source=share&utm_medium=member_android&rcm=ACoAADcXNX8BNm6vE2wHF7V91czmcuYXcuPHhY4.


🧠⚛️ Beyond Penrose: Can Consciousness Be Derived from Geometry? For more than 30 years, Roger Penrose and Stuart Hameroff proposed that consciousness emerges through Objective Reduction (OR) inside neuronal microtubules. Penrose’s key equation is remarkably simple: τ_OR = ℏ / E_G where: τ_OR = collapse time ℏ = reduced Planck constant E_G = gravitational self-energy of the spacetime superposition The idea is: 🌌 Spacetime superposition ⟶ Gravitational instability ⟶ Wavefunction collapse ⟶ Conscious event But a major question remained: ❓ What is the mathematical mechanism that actually causes collapse? The EWOG framework attempts to provide one.

Maths is Cooked: AI’s Latest Breakthrough — And What’s Next

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As AI continues to improve its reasoning abilities, mathematicians are increasingly worried about the computer algorithms replacing them. In late May, those fears got even worse when OpenAI revealed that one of its general-purpose reasoning models had written a proof solving a math problem that’s sat unsolved for more than 80 years. But should they actually be worried? Let’s take a look.

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Google DeepMind AI Discovered a Mathematical Pattern Hidden in Prime Numbers

What exactly did DeepMind find?
Could this discovery help solve longstanding mathematical mysteries?
And what might it mean for cryptography, computing, and our understanding of mathematics itself?

In this video, we explore the science behind the discovery, the role of artificial intelligence in modern research, and why mathematicians around the world are paying close attention.

Whether this breakthrough leads to a revolutionary new theorem or simply a deeper understanding of prime numbers, it demonstrates the growing power of AI to accelerate scientific progress.

👇 What do YOU think?
Will AI help solve the greatest unsolved problems in mathematics?

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Most precise measurement of the force that binds nuclear matter achieved

Trinity’s Prof. Stefan Sint, along with collaborators from Germany, Spain and Italy, has published the most precise determination to date of the strong coupling constant. This parameter governs the interactions between quarks and gluons, the fundamental components of nuclear matter. The new result halves the error of all previous experimental measurements combined, setting a new benchmark for the Standard Model, which summarizes our current knowledge of elementary particle physics.

This advance will improve our understanding of how quarks and gluons behave inside protons and enable high-precision measurements of the Higgs boson and its properties. More generally, improved quantitative control of the strong interactions increases the likelihood of discovering effects of yet unknown physics at CERN’s Large Hadron Collider (LHC).

Prof. Sint from Trinity’s School of Mathematics was one of the researchers whose landmark results were published in Nature.

Alonzo Church

His revolutionary idea? Before “computer science” was even a field, Church invented the lambda calculus (λ-calculus)—an elegant, abstract system for expressing computation through pure mathematical functions. In 1936, he used it to prove that no universal algorithm could ever decide the truth of all mathematical statements, solving Hilbert’s famous Entscheidungsproblem in the negative. This became known as Church’s Theorem, and it revealed something profound: there are hard limits to what any machine can compute.

That same year, Church articulated what we now call the Church–Turing thesis: any problem that can be “effectively calculated” can be computed by a Turing machine—or equivalently, expressed in lambda calculus. When Alan Turing learned of Church’s work, he traveled to Princeton to study under him. Together, they proved their two seemingly different models of computation were fundamentally equivalent, laying the bedrock for all future computer science.


Alonzo Church was born on June 14, 1903, in Washington, D.C., where his father, Samuel Robbins Church, was a justice of the peace [ 5 ] and the judge of the Municipal Court for the District of Columbia. He was the grandson of Alonzo Webster Church (1829−1909), United States Senate Librarian from 1881 to 1901, and great-grandson of Alonzo Church, a professor of Mathematics and Astronomy and 6th President of the University of Georgia. [ 6 ] As a young boy, Church was partially blinded by an air gun accident. [ 7 ] The family later moved to Virginia after his father lost his position at the university because of failing eyesight. With help from his uncle, also named Alonzo Church, the son attended the private Ridgefield School for Boys in Ridgefield, Connecticut. [ 8 ] After graduating from Ridgefield in 1920, Church attended Princeton University, where he was an exceptional student. He published his first paper on Lorentz transformations [ 9 ] in 1924 and graduated the same year with a degree in mathematics. He stayed at Princeton for graduate work, earning a Ph. D. in mathematics in three years under Oswald Veblen.

He married Mary Julia Kuczinski in 1925. The couple had three children: Alonzo Jr. (1929), Mary Ann (1933), and Mildred (1938).

After receiving his Ph.D., he taught briefly as an instructor at the University of Chicago. [ 10 ] He received a two-year National Research Fellowship that enabled him to attend Harvard University in 1927–1928, and the University of Göttingen and University of Amsterdam the following year.

Researchers identify brain ‘entrapment’ patterns associated with depression

Researchers at the Icahn School of Medicine at Mount Sinai have identified distinctive patterns in how the brain transitions between activity states in people with depression, providing new insight into why depressive symptoms can feel persistent and difficult to overcome.

Published online in Nature Communications, the study combined advanced neuroimaging techniques with mathematical modeling to examine how the brain moves between functional activity states over time. The findings suggest that depression may involve a form of “brain-state entrapment,” in which the brain becomes more likely to enter certain patterns of activity and less likely to transition out of them.

“Many patients describe depression as feeling stuck in negative patterns of thought, mood and behavior,” said Yael Jacob, Ph.D., assistant professor of psychiatry at the Dennis S. Charney, MD, Depression and Anxiety Discovery Center at the Icahn School of Medicine at Mount Sinai and senior author of the paper. “Our findings suggest that this experience of being ‘stuck’ may reflect measurable changes in the brain’s underlying dynamics.”

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