Lifeboat Foundation

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.

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.

Argonne National Laboratory scientists have used anomaly detection in the ATLAS collaboration to search for new particles, identifying a promising anomaly that could indicate new physics beyond the Standard Model.

Scientists used a neural network, a type of brain-inspired machine learning algorithm, to sift through large volumes of particle collision data in a study that marks the first use of a neural network to analyze data from a collider experiment.

Particle physicists are tasked with mining this massive and growing store of collision data for evidence of undiscovered particles. In particular, they’re searching for particles not included in the Standard Model of particle physics, our current understanding of the universe’s makeup that scientists suspect is incomplete.

A recent book diagnoses a blind spot in our knowledge and calls for a revolution that includes human subject experience in the equation.

Philosopher Nick Bostrom popularized the idea superintelligent AI could erase humanity. His new book imagines a world in which algorithms have solved every problem.

Northrop Grumman and Umbra have been awarded small contracts by the Defense Advanced Research Projects Agency (DARPA) to continue to the second phase of a program designed to collect data from radar-equipped satellites flying in formation and develop innovative algorithms to process the data for military applications.

Umbra’s contract under the Distributed Radar Image Foundation Technology (DRIFT) program is for $6 million and will last for six months and Northrop Grumman’s is for $2 million and covers one year, a DARPA spokesperson said.

Scientists have introduced a groundbreaking form of quantum entanglement known as frequency-domain photon number-path entanglement. This leap in quantum physics involves an innovative tool called a frequency beam splitter, which has the unique ability to alter the frequency of individual photons with a 50% success rate.

For years, the scientific community has delved into spatial-domain photon number-path entanglement, a key player in the realms of quantum metrology and information science. This concept involves photons arranged in a special pattern, known as NOON states, where they’re either all in one pathway or another, enabling groundbreaking applications like super-resolution imaging that surpasses traditional limits, the enhancement of quantum sensors, and the development of quantum computing algorithms designed for tasks requiring exceptional phase sensitivity.

In a new paper published in Light Science & Application, a team of scientists, led by Professor Heedeuk Shin from Department of Physics, Pohang University of Science and Technology, Korea, have developed an entangled states in the frequency domain, a concept akin to spatial-domain NOON states but with a significant twist: instead of photons being divided between two paths, they’re distributed between two frequencies. This advancement has led to the successful creation of a two-photon NOON state within a single-mode fiber, showcasing an ability to perform two-photon interference with double the resolution of its single-photon counterpart, indicating remarkable stability and potential for future applications.

Sepsis is a condition in which the body responds improperly. More specifically, the infection causes the organs in the body to shut down. This is a serious illness which could lead to extremely low blood pressure or septic shock. In this case, permanent damage to the lungs, kidneys, liver, and other organs can occur. Unfortunately, if the damage is extensive enough, it could be fatal. Common symptoms associated with sepsis includes alteration of mental status, shallow breathing, sweating out of context, lightheadedness, chills, and other symptoms associated with infection or fever. Sepsis can lead to septic shock and raises the risk of death. Symptoms of septic shock include inability to stand, sleepiness, and extreme confusion. Interestingly, symptoms can vary between people, and it is important to monitor bodily changes to detect sepsis as early as possible. Bacterial, viral, or fungal infections can lead to sepsis including common infections such as pneumonia, urinary tract infections, and burns, among others. It is critical to see a doctor if you suspect you are not getting better or if your symptoms worsen. Early detection of sepsis can help improve survival rate and prevent permanent organ damage. Treatments include antibiotics, increased fluids, vasopressors to increase blood pressure, and steroids. Although scientists and physicians have worked to understand sepsis and how to treat it, other discoveries are yet to be made.

A recent study in Nature Immunology by Dr. Antoine Roquilly from Nantes University in France, demonstrated that patients that experienced sepsis build strong immune cells that aid in the prevention of tumor development. It was previously unknown how the immune landscape was shaped after a patient recovered from sepsis. Roquilly and his team wanted to understand the relationship between these exposed immune cells and the risk of developing cancer in the future.

Roquilly’s research team first analyzed big datasets that consisted of information from patients who survived sepsis. Researchers were able to determine the risk of cancer prevalence up to 10 years following the discharge from the hospital for sepsis patients. Interestingly, sepsis survivors had lower risk of developing cancer compared to those that did not have sepsis.

The open CMS detector during the second long shutdown of CERN’s accelerator complex. (Image: CERN) When we look at ourselves in a mirror, we see a virtual twin, identical in every detail except with left and right inverted. In particle physics, a transformation in which charge–parity (CP) symmetry is respected swaps a particle with the mirror image of its antimatter particle, which has opposite properties such as electric charge. The physical laws that govern nature don’t respect CP symmetry, however. If they did, the Universe would contain equal amounts of matter and antimatter, as it is believed to have done just after the Big Bang. To explain the large imbalance between matter and antimatter seen in the present-day Universe, CP symmetry has to be violated to a great extent. The Standard Model of particle physics can account for some CP violation, but it is not sufficient to explain the present-day matter–antimatter imbalance, prompting researchers to explore CP violation in all its known and unknown manifestations. One way CP violation can manifest itself is in the “mixing” of electrically neutral mesons such as the strange beauty meson, which is composed of a strange quark and a bottom antiquark. These mesons can travel macroscopic distances in the Large Hadron Collider (LHC) detectors before decaying into lighter particles, and during this journey they can turn into their corresponding antimesons and back. This phenomenon, called meson mixing, could be different for a meson turning into an antimeson versus an antimeson turning into a meson, generating CP violation. To see if that’s the case, researchers need to count how many mesons or antimesons survive a certain duration before decaying, and then repeat the measurement for a given range of durations. To do so, they have to separate mesons from antimesons, a task called flavour tagging. This task is crucial to pinning down CP violation in meson mixing and in the interference between meson mixing and decay. At a seminar held recently at CERN, the CMS collaboration at the LHC reported the first evidence of CP violation in the decay of the strange beauty meson into a pair of muons and a pair of electrically charged kaons. By deploying a new flavour-tagging algorithm on a sample of about 500 000 decays of the strange beauty meson into a pair of muons and a pair of charged kaons, collected during Run 2 of the LHC, the CMS collaboration measured with improved precision the parameter that determines CP violation in the interference between this meson’s mixing and decay. If this parameter is zero, CP symmetry is respected. The new flavour-tagging algorithm is based on a cutting-edge artificial intelligence (AI) technique called a graph neural network, which performs accurate flavour tagging by gathering information from the particles surrounding the strange beauty meson and those being produced alongside it. The collaboration then combined the result with its previous measurement of the parameter based on data from Run 1 of the LHC. The combined result is different from zero and is consistent with the Standard Model prediction and with previous measurements from CMS and the ATLAS and LHCb experiments. Notably, the combined result is comparable in precision to the world’s most precise measurement of the parameter, obtained by LHCb, a detector specifically designed to perform measurements of this kind. Moreover, the result has a statistical significance that crosses the conventional “3 sigma” threshold, providing the first evidence of CP violation in the decay of the strange beauty meson into a pair of muons and a pair of charged kaons. The result marks a milestone in CMS’s studies of CP violation. Thanks to AI, CMS has pushed the boundary of what its detector can achieve in the exploration of this fundamental matter–antimatter asymmetry. Find out more on the CMS website.