A selection of 10 Interesting Scientific Discoveries for May, 2024.My Patreon Page: https://www.patreon.com/johnmichaelgodierMy Event Horizon Channel: https://…
Elon Musk’s unique management style at Tesla, which involves small, highly technical teams, removing underperforming employees, and creating challenging deadlines, has been crucial to the company’s success Questions to inspire discussion Who is Andrej Karpathy? —Andrej Karpathy is a highly respected computer scientist who served as the Director of AI and Autopilot at Tesla and co-founded OpenAI.
The find, simulated with computer modeling, might explain what happens to liquid water across the universe.
“Water is really important for life,” said Eryn Cangi, co-author and a research scientist at the Laboratory for Atmospheric and Space Physics, in a press release. “We need to understand the conditions that support liquid water in the universe, and that may have produced the very dry state of Venus today.”
At one point, Venus might have hosted seas like Earth. So, what happened? The study’s scientists suspect that Venus underwent a powerful greenhouse event that raised temperatures to 900 degrees Fahrenheit. After this happened, all the planet’s water evaporated, leaving some droplets behind. Even the few drops that were left over might have vanished because of an ion, HCO+, in the planet’s atmosphere.
Google DeepMind’s newly launched AlphaFold Server is the most accurate tool in the world for predicting how proteins interact with other molecules throughout the cell. It is a free platform that scientists around the world can use for non-commercial research. With just a few clicks, biologists can harness the power of AlphaFold 3 to model structures composed of proteins, DNA, RNA and a selection of ligands, ions and chemical modifications.
AlphaFold Server will help scientists make novel hypotheses to test in the lab, speeding up workflows and enabling further innovation. Our platform gives researchers an accessible way to generate predictions, regardless of their access to computational resources or their expertise in machine learning.
Experimental protein-structure prediction can take about the length of a PhD and cost hundreds of thousands of dollars. Our previous model, AlphaFold 2, has been used to predict hundreds of millions of structures, which would have taken hundreds of millions of researcher-years at the current rate of experimental structural biology.
AlphaFold 3 model is a Google DeepMind and Isomorphic Labs collaboration.
We discuss a perturbative and non-instantaneous reheating model, adopting a generic post-inflationary scenario with an equation of state w. In particular, we explore the Higgs boson-induced reheating, assuming that it is achieved through a cubic inflaton-Higgs coupling ϕ|H|2. In the presence of such coupling, the Higgs doublet acquires a ϕ-dependent mass and a non-trivial vacuum–expectation–value that oscillates in time and breaks the Standard Model gauge symmetry. Furthermore, we demonstrate that the non-standard cosmologies and the inflaton-induced mass of the Higgs field modify the radiation production during the reheating period. This, in turn, affects the evolution of a thermal bath temperature, which has remarkable consequences for the ultraviolet freeze-in dark matter production.
Proteins are the molecular machines that sustain every cell and organism, and knowing what they look like will be critical to untangling how they function normally and malfunction in disease. Now researchers have taken a huge stride toward that goal with the development of new machine learning algorithms that can predict the folded shapes of not only proteins but other biomolecules with unprecedented accuracy.
In a paper published today in Nature, Google DeepMind and its spinoff company Isomorphic Labs announced the latest iteration of their AlphaFold program, AlphaFold3, which can predict the structures of proteins, DNA, RNA, ligands and other biomolecules, either alone or bound together in different embraces. The findings follow the tail of a similar update to another deep learning structure-prediction algorithm, called RoseTTAFold All-Atom, which was published in March in Science.
Neoplants has bioengineered a houseplant that uses bacteria to remove indoor air pollution from your home.
Our investigation into AdVon Commerce, the AI contractor at the heart of scandals at USA Today and Sports Illustrated.
In this study, graduate student Keito Kobayashi and Professor Shunsuke Fukami from Tohoku University, along with Dr. Kerem Camsari from the University of California, Santa Barbara, and their colleagues, developed a near-future heterogeneous version of a probabilistic computer tailored for executing probabilistic algorithms and facile manufacturing.
“Our constructed prototype demonstrated that excellent computational performance can be achieved by driving pseudo random number generators in a deterministic CMOS circuit with physical random numbers generated by a limited number of stochastic nanomagnets,” says Fukami. “Specifically speaking, a limited number of probabilistic bits (p-bits) with a stochastic magnetic tunnel junction (s-MTJ), should be manufacturable with a near-future integration technology.”
The researchers also clarified that the final form of the spintronics probabilistic computer, primarily composed of s-MTJs, will yield a four-order-of-magnitude reduction in area and a three-order-of-magnitude reduction in energy consumption compared to the current CMOS circuits when running probabilistic algorithms.
The findings, published in a study in Developmental Cell, reveal that intestinal smooth muscle originates in embryos and forms by the same process that is a hallmark of creating scar tissue when a wound heals.
The smooth muscle sits inside tiny finger-like projections called villi, which absorb fats—also known as lipids—from foods. Contractions of these smooth muscles squeeze absorbed dietary fats through lymphatic capillaries, called lacteals, which send the fats into the systemic blood circulation to produce energy.