Lifeboat Foundation

Mathematical models suggest that with just a few more genes, it might be possible to define hundreds of cellular identities, more than enough to populate the tissues of complex organisms. It’s a finding that opens the door to experiments that could bring us closer to understanding how, eons ago, the system that builds us was built.

The Limits of Mutual Repression

Developmental biologists have illuminated many tipping points and chemical signals that prompt cells to follow one developmental pathway or another by studying natural cells. But researchers in the field of synthetic biology often take another approach, explained Michael Elowitz, a professor of biology and bioengineering at Caltech and an author of the new paper: They build a system of cell-fate control from scratch to see what it can tell us about what such systems require.

A new molecule created by a researcher at the University of Texas at Dallas kills a variety of difficult-to-treat cancers, including triple-negative breast cancer, by taking advantage of a weakness in cells that was not previously targeted by existing drugs.

The research, which was conducted using isolated cells, human cancer tissue, and mouse-grown human cancers, was recently published in Nature Cancer.

A co-corresponding author of the study and an associate professor of chemistry and biochemistry in the School of Natural Sciences and Mathematics at the University of Texas at Dallas, Dr. Jung-Mo Ahn has dedicated more than ten years of his career to developing small molecules that target protein-protein interactions in cells. He previously created potential therapeutic candidate compounds for treatment-resistant prostate cancer and breast cancer using a method called structure-based rational drug design.

Meta is developing a machine learning model that scans these citations and cross-references their content to Wikipedia articles to verify that not only the topics line up, but specific figures cited are accurate.

This isn’t just a matter of picking out numbers and making sure they match; Meta’s AI will need to “understand” the content of cited sources (though “understand” is a misnomer, as complexity theory researcher Melanie Mitchell would tell you, because AI is still in the “narrow” phase, meaning it’s a tool for highly sophisticated pattern recognition, while “understanding” is a word used for human cognition, which is still a very different thing).

Meta’s model will “understand” content not by comparing text strings and making sure they contain the same words, but by comparing mathematical representations of blocks of text, which it arrives at using natural language understanding (NLU) techniques.

Today, Oak Ridge National Laboratory’s Frontier supercomputer was crowned fastest on the planet in the semiannual Top500 list. Frontier more than doubled the speed of the last titleholder, Japan’s Fugaku supercomputer, and is the first to officially clock speeds over a quintillion calculations a second—a milestone computing has pursued for 14 years.

That’s a big number. So before we go on, it’s worth putting into more human terms.

Imagine giving all 7.9 billion people on the planet a pencil and a list of simple arithmetic or multiplication problems. Now, ask everyone to solve one problem per second for four and half years. By marshaling the math skills of the Earth’s population for a half-decade, you’ve now solved over a quintillion problems.

Tohoku University scientists in Japan have developed a mathematical description of what happens within tiny magnets as they fluctuate between states when an electric current and magnetic field are applied. Their findings, published in the journal Nature Communications, could act as the foundation for engineering more advanced computers that can quantify uncertainty while interpreting complex data.

Classical computers have gotten us this far, but there are some problems that they cannot address efficiently. Scientists have been working on addressing this by engineering computers that can utilize the laws of quantum physics to recognize patterns in complex problems. But these so-called quantum computers are still in their early stages of development and are extremely sensitive to their surroundings, requiring extremely low temperatures to function.

Now, scientists are looking at something different: a concept called probabilistic computing. This type of computer, which could function at room temperature, would be able to infer potential answers from complex input. A simplistic example of this type of problem would be to infer information about a person by looking at their purchasing behavior. Instead of the computer providing a single, discrete result, it picks out patterns and delivers a good guess of what the result might be.

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In the Existential Hope-podcast (https://www.existentialhope.com), we invite scientists to speak about long-termism. Each month, we drop a podcast episode where we interview a visionary scientist to discuss the science and technology that can accelerate humanity towards desirable outcomes.

Continue reading “Existential Hope Special with Morgan Levine | On the Future of Aging” »

Physicists at the Max Planck Institute of Quantum Optics have managed to entangle more than a dozen photons efficiently and in a defined way. They are thus creating a basis for a new type of quantum computer. Their study is published in Nature.

The phenomena of the quantum world, which often seem bizarre from the perspective of the common everyday world, have long since found their way into technology. For example, entanglement: a quantum-physical connection between particles that links them in a strange way over arbitrarily long distances. It can be used, for example, in a quantum computer—a computing machine that, unlike a conventional computer, can perform numerous mathematical operations simultaneously. However, in order to use a quantum computer profitably, a large number of entangled particles must work together. They are the basic elements for calculations, so-called qubits.

“Photons, the particles of light, are particularly well suited for this because they are robust by nature and easy to manipulate,” says Philip Thomas, a doctoral student at the Max Planck Institute of Quantum Optics (MPQ) in Garching near Munich. Together with colleagues from the Quantum Dynamics Division led by Prof. Gerhard Rempe, he has now succeeded in taking an important step towards making photons usable for technological applications such as quantum computing: For the first time, the team generated up to 14 entangled photons in a defined way and with high efficiency.

Managing road intersections in crowded and dynamic environments, such as urban areas, can be highly challenging. The poor management of traffic at these can lead to road accidents, wastage of fuel, and environmental pollution.

Researchers at the University of Maryland have recently developed GAMEOPT, a new algorithm that could help manage unsignalized road intersections with high traffic more efficiently. The research team with members, Nilesh Suriyarachchi, Rohan Chandra, John S. Baras and Dinesh Manocha introduced their method in a recent paper to be published in the proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2022). This method combines optimization techniques with ideas from game theory, a mathematical construct that represents situations in which different agents are competing with one another.

“Forty percent of all crashes, 50% of serious collisions, and 20% of fatalities occur at unsignalized intersections,” Chandra, a member of the research team, told TechXplore. “Our primary objective is to improve traffic flow and fuel efficiency in poorly regulated or unregulated traffic intersections. To achieve this objective, we propose an algorithm that combines ideas from optimization and game theory to understand how different traffic agents cooperate and negotiate with each other at traffic intersections.”

In 1,885, King Oscar II of Sweden announced a public challenge consisting of four mathematical problems. The French polymath Henri Poincaré focused on one related to the motion of celestial bodies, the so-called n-body problem. Will our solar system continue its clocklike motion indefinitely, will the planets fly off into the void, or will they collapse into a fiery solar death?

Poincaré’s solution — which indicated that at least some systems, like the sun, Earth and moon, were stable — won the prestigious prize, and an accompanying article was printed for distribution in 1889. Unfortunately, his solution was incorrect.

Poincaré admitted his error and paid to have the copies of his solution destroyed (which cost more than the prize money). A month later, he submitted a corrected version. He now saw that even a system with only three bodies could behave too unpredictably — too chaotically — to be modeled. So began the field of dynamical systems.