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Specialized transporters relay lipids to cellular targets

In addition to providing energy, lipids are also essential building blocks of our cell membranes. However, despite their importance, they remain poorly understood. A research team has revealed for the first time the secrets of their transport within cells. Each lipid uses a limited number of proteins to move from its place of production to its place of action. The team has also compiled an inventory of the proteins involved in the transport of hundreds of lipids.

These findings, published in the journal Nature, provide a better picture of the functioning of our cells, as well as of many genetic and metabolic disorders, such as diabetes and Alzheimer’s disease.

Biologists brought together more than a hundred transfer proteins with hundreds of different lipids. The aim was to obtain the most comprehensive list possible of the ‘pairs’ formed between each protein and the lipids it can carry.

To do this, two experimental methods were combined. The first, carried out in a test tube, provides a highly controlled environment, while the second, which more closely corresponds to the inside of a cell, allows researchers to verify how these bonds are formed under near-real conditions. This is a world first on such a scale and at such a level of complexity. “The ‘‘couples’’ identified show that transfer proteins are not “buses” capable of transporting most lipids, but private chauffeurs with specific characteristics,” explains the senior author.

Scientists have been able to determine, using advanced mathematical models, how three transfer proteins recognise, among all lipids, those that they actually transport. ScienceMission sciencenewshighlights.

Video: Why ‘basic science’ is the foundation of innovation

At first glance, some scientific research can seem, well, impractical. When physicists began exploring the strange, subatomic world of quantum mechanics a century ago, they weren’t trying to build better medical tools or high-speed internet. They were simply curious about how the universe worked at its most fundamental level.

Yet without that “curiosity-driven” research—often called basic science—the modern world would look unrecognizable.

“Basic science drives the really big discoveries,” says Steve Kahn, UC Berkeley’s dean of mathematical and physical sciences. “Those paradigm changes are what really drive innovation.”

Sam Altman Cornered by Discovery: Intent & Emails in Elon’s OpenAI Lawsuit

Elon Musk’s lawsuit against OpenAI and his own ambitious plans for AI and tech innovations, including new devices and massive growth for his companies, are positioning him for a major impact on the tech industry, but also come with significant challenges and risks ## Questions to inspire discussion.

Legal Risk Management.

🔍 Q: How does the discovery process threaten OpenAI regardless of lawsuit outcome?

A: Discovery forces exposure of sensitive internal information including Greg Brockman’s 2017 diary entries revealing intent to become for-profit and violating prior agreements with Elon Musk, creating reputational damage and investor uncertainty even if OpenAI wins the case.

⏱️ Q: Why is lawsuit timing particularly damaging to OpenAI’s competitive position?

A: The lawsuit hits during OpenAI’s massive capital raise preparation, forcing delays in fundraising and implementation that allow competitors like Google and Anthropic to advance while OpenAI falls behind, similar to how Meta became less relevant in the AI race.

Microsoft Just Dropped New AI That Makes Decisions Better Than Humans

Microsoft just introduced OptiMind — a new AI system that turns plain English decision problems into solver-ready optimization models. Instead of needing an expert to manually convert business intent into MILP math, OptiMind generates the full mathematical formulation plus executable Python code using GurobiPy. The result: faster, cheaper optimization workflows for logistics, scheduling, manufacturing, and supply chains — with major accuracy gains on cleaned, expert-validated benchmarks.

📩 Brand Deals & Partnerships: [email protected].
✉ General Inquiries: [email protected].

🧠 What You’ll See.
0:00 What Microsoft OptiMind Really Is.
1:43 From Text to Optimization Code (MILP + Gurobi)
2:59 OptiMind Architecture: MoE and 128K Context.
3:34 Open Source Under MIT License.
4:28 Training With Expert Hints and Clean Data.
6:02 53 Optimization Problem Classes.
8:38 Multi-Stage Solver-in-the-Loop Inference.
9:11 Self-Consistency and Auto Error Correction.
9:55 Performance vs GPT-o4 Mini and GPT-5
10:32 Limits, Safety, and Human Oversight.

🚨 Why It Matters.
Optimization is already the hidden engine behind supply chains, factories, routing, and scheduling — the problem is the translation step. Converting messy real-world requirements into correct MILP constraints takes rare experts and days of work. OptiMind targets that exact gap: natural language in, solver-ready decisions out. This is why it’s going viral — it’s not just AI text generation, it’s AI generating decisions.

#AI #Microsoft #OptiMind

Why does AI being good at math matter?

This is the second time in recent months that the AI world has got all excited about math. The rumor mill went into overdrive last November, when there were reports that the boardroom drama at OpenAI, which saw CEO Sam Altman temporarily ousted, was caused by a new powerful AI breakthrough. It was reported that the AI system in question was called Q* and could solve complex math calculations. (The company has not commented on Q*, and we still don’t know if there was any link to the Altman ouster or not.) I unpacked the drama and hype in this story.

You don’t need to be really into math to see why this stuff is potentially very exciting. Math is really, really hard for AI models. Complex math, such as geometry, requires sophisticated reasoning skills, and many AI researchers believe that the ability to crack it could herald more powerful and intelligent systems. Innovations like AlphaGeometry show that we are edging closer to machines with more human-like reasoning skills. This could allow us to build more powerful AI tools that could be used to help mathematicians solve equations and perhaps come up with better tutoring tools.

John Forbes Nash Jr

(June 13, 1928 – May 23, 2015), known and published as John Nash, was an American mathematician who made fundamental contributions to game theory, real algebraic geometry, differential geometry, and partial differential equations. [ 1 ] [ 2 ] Nash and fellow game theorists John Harsanyi and Reinhard Selten were awarded the 1994 Nobel Prize in Economics. [ 3 ] In 2015, Louis Nirenberg and he were awarded the Abel Prize for their contributions to the field of partial differential equations.

As a graduate student in the Princeton University Department of Mathematics, Nash introduced a number of concepts (including the Nash equilibrium and the Nash bargaining solution), which are now considered central to game theory and its applications in various sciences. In the 1950s, Nash discovered and proved the Nash embedding theorems by solving a system of nonlinear partial differential equations arising in Riemannian geometry. This work, also introducing a preliminary form of the Nash–Moser theorem, was later recognized by the American Mathematical Society with the Leroy P. Steele Prize for Seminal Contribution to Research. Ennio De Giorgi and Nash found, with separate methods, a body of results paving the way for a systematic understanding of elliptic and parabolic partial differential equations.

Wormholes may not exist—we’ve found they reveal something deeper about time and the universe

Wormholes are often imagined as tunnels through space or time—shortcuts across the universe. But this image rests on a misunderstanding of work by physicists Albert Einstein and Nathan Rosen.

In 1935, while studying the behavior of particles in regions of extreme gravity, Einstein and Rosen introduced what they called a “bridge”: a mathematical link between two perfectly symmetrical copies of spacetime. It was not intended as a passage for travel, but as a way to maintain consistency between gravity and quantum physics. Only later did Einstein–Rosen bridges become associated with wormholes, despite having little to do with the original idea.

But in new research published in Classical and Quantum Gravity, my colleagues and I show that the original Einstein–Rosen bridge points to something far stranger—and more fundamental—than a wormhole.

Boys and girls tend to use different strategies to solve math problems, new research shows

New studies show girls prefer step-by-step math algorithms, while boys favor creative shortcuts. This difference in approach, rather than raw ability, may explain why men continue to outnumber women in advanced STEM fields.

The Math Behind Evo Devo (TMEB #3)

The math behind Evo-devo~

Uri Alon’s Book:

Jim Collins paper:
https://www.researchgate.net/publication/12654725_Constructi…ichia_coli.
https://www.nature.com/articles/s41467-017-01498-0

The math behind fly development:
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.

Music:
City Life – Artificial. Music (No Copyright Music)
Link: https://www.youtube.com/watch?v=caT3j… ure Water by Meydän Link: • Meydän — Pure Water [Creative Commons — CC… Forever Sunrise — by Jonny Easton Link: • Forever Sunrise — Soft Inspirational Piano… Softwares used: Manim CE Keynote.
Pure Water by Meydän.
Link: https://youtu.be/BU85yzb0nMU
Forever Sunrise — by Jonny Easton.
Link: https://youtu.be/9Xndx7nhGAs.

Softwares used:

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