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Category: chemistry – Page 6

Researchers will soon be able to study biological changes at scales and speeds not previously possible to significantly expand knowledge in areas such as disease progression and drug delivery.

Physicists at The University of Queensland have used “tweezers made from light” to measure activity within microscopic systems over timeframes as short as milliseconds. Professor Halina Rubinsztein-Dunlop from UQ’s School of Mathematics and Physics said the method could help biologists understand what was happening within single living cells.

“For example, they will be able to look at how a cell is dividing, how it responds to outside stimuli, or even how affect cell properties,” Professor Rubinsztein-Dunlop said.

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A research team at UNIST has identified the causes of oxygen generation in a novel cathode material called quasi-lithium and proposed a material design principle to address this issue.

Quasi-lithium materials theoretically enable batteries to store 30% to 70% more energy compared to existing technologies through high-voltage charging of over 4.5V. This advancement could allow to achieve a of up to 1,000 km on a single charge. However, during the high-voltage charging process, oxygen trapped inside the material can oxidize and be released as gas, posing a significant explosion risk.

The research team, led by Professor Hyun-Wook Lee in the School of Energy and Chemical Engineering, discovered that oxygen oxidizes near 4.25V, causing partial structural deformation and gas release.

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AlphaTensor–Quantum addresses three main challenges that go beyond the capabilities of AlphaTensor25 when applied to this problem. First, it optimizes the symmetric (rather than the standard) tensor rank; this is achieved by modifying the RL environment and actions to provide symmetric (Waring) decompositions of the tensor, which has the beneficial side effect of reducing the action search space. Second, AlphaTensor–Quantum scales up to large tensor sizes, which is a requirement as the size of the tensor corresponds directly to the number of qubits in the circuit to be optimized; this is achieved by a neural network architecture featuring symmetrization layers. Third, AlphaTensor–Quantum leverages domain knowledge that falls outside of the tensor decomposition framework; this is achieved by incorporating gadgets (constructions that can save T gates by using auxiliary ancilla qubits) through an efficient procedure embedded in the RL environment.

We demonstrate that AlphaTensor–Quantum is a powerful method for finding efficient quantum circuits. On a benchmark of arithmetic primitives, it outperforms all existing methods for T-count optimization, especially when allowed to leverage domain knowledge. For multiplication in finite fields, an operation with application in cryptography34, AlphaTensor–Quantum finds an efficient quantum algorithm with the same complexity as the classical Karatsuba method35. This is the most efficient quantum algorithm for multiplication on finite fields reported so far (naive translations of classical algorithms introduce overhead36,37 due to the reversible nature of quantum computations). We also optimize quantum primitives for other relevant problems, ranging from arithmetic computations used, for example, in Shor’s algorithm38, to Hamiltonian simulation in quantum chemistry, for example, iron–molybdenum cofactor (FeMoco) simulation39,40. AlphaTensor–Quantum recovers the best-known hand-designed solutions, demonstrating that it can effectively optimize circuits of interest in a fully automated way. We envision that this approach can accelerate discoveries in quantum computation as it saves the numerous hours of research invested in the design of optimized circuits.

AlphaTensor–Quantum can effectively exploit the domain knowledge (provided in the form of gadgets with state-of-the-art magic-state factories12), finding constructions with lower T-count. Because of its flexibility, AlphaTensor–Quantum can be readily extended in multiple ways, for example, by considering complexity metrics other than the T-count such as the cost of two-qubit Clifford gates or the qubit topology, by allowing circuit approximations, or by incorporating new domain knowledge. We expect that AlphaTensor–Quantum will become instrumental in automatic circuit optimization with new advancements in quantum computing.

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The healthcare industry faces a significant shift towards digital health technology, with a growing demand for real-time and continuous health monitoring and disease diagnostics [1, 2, 3]. The rising prevalence of chronic diseases, such as diabetes, heart disease, and cancer, coupled with an aging population, has increased the need for remote and continuous health monitoring [4, 5, 6, 7]. This has led to the emergence of artificial intelligence (AI)-based wearable sensors that can collect, analyze, and transmit real-time health data to healthcare providers so that they can make efficient decisions based on patient data. Therefore, wearable sensors have become increasingly popular due to their ability to provide a non-invasive and convenient means of monitoring patient health. These wearable sensors can track various health parameters, such as heart rate, blood pressure, oxygen saturation, skin temperature, physical activity levels, sleep patterns, and biochemical markers, such as glucose, cortisol, lactates, electrolytes, and pH and environmental parameters [1, 8, 9, 10]. Wearable health technology includes first-generation wearable technologies, such as fitness trackers, smartwatches, and current wearable sensors, and is a powerful tool in addressing healthcare challenges [2].

The data collected by wearable sensors can be analyzed using machine learning (ML) and AI algorithms to provide insights into an individual’s health status, enabling early detection of health issues and the provision of personalized healthcare [6,11]. One of the most significant advantages of AI-based wearable health technology is to promote preventive healthcare. This enables individuals and healthcare providers to proactively address symptomatic conditions before they become more severe [12,13,14,15]. Wearable devices can also encourage healthy behavior by providing incentives, reminders, and feedback to individuals, such as staying active, hydrating, eating healthily, and maintaining a healthy lifestyle by measuring hydration biomarkers and nutrients.

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Two different teams of astronomers have detected oxygen in the most distant known galaxy, JADES-GS-z14-0. The discovery, reported in two separate studies, was made possible thanks to the Atacama Large Millimeter/submillimeter Array (ALMA), in which the European Southern Observatory (ESO) is a partner. This record-breaking detection is making astronomers rethink how quickly galaxies formed in the early universe.

Discovered last year, JADES-GS-z14-0 is the most distant confirmed galaxy ever found: it is so far away, its light took 13.4 billion years to reach us, meaning we see it as it was when the universe was less than 300 million years old, about 2% of its present age.

The new oxygen detection with ALMA, a telescope array in Chile’s Atacama Desert, suggests the galaxy is much more chemically mature than expected.

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Using the Five-hundred-meter Aperture Spherical radio Telescope (FAST), Chinese astronomers have detected a new ultra-faint dwarf galaxy, which turned out to be gas-rich. The finding was reported in a research paper published March 12 on the preprint server arXiv.

The so-called ultra-faint dwarf (UFDs) are the least luminous, most –dominated, and least chemically evolved galaxies known. Therefore, they are perceived by astronomers as the best candidate fossils from the universe at its early stages.

A team of astronomers led by Jin-Long Xu of the Chinese Academy of Sciences (CAS) is carrying out a FAST extragalactic H I (neutral atomic hydrogen) survey (FASHI). One of the objectives of this survey is to search for dark and weak galaxies. Now, they report the finding of a new UFD as part of this project.

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“We have found a key to controlling the switching on and off of proteins by combining photochemistry and hydrolysis,” says KTH researcher Tove Kivijärvi.

When designing materials that aim to improve medicine, you need to be able to control the functions of the material in a very precise way. If this is achieved, cell environments similar to the human body can be created in the lab, which is important for understanding biological mechanisms, disease processes and how the body repairs itself. Biological materials can also be used to study how drugs work and to streamline drug testing and preclinical studies.

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New research from Northwestern University has systematically proven that a mild zap of electricity can strengthen a marine coastline for generations—greatly reducing the threat of erosion in the face of climate change and rising sea levels.

In the new study, researchers took inspiration from clams, mussels and other shell-dwelling sea life, which use dissolved minerals in seawater to build their shells.

Similarly, the researchers leveraged the same naturally occurring, dissolved minerals to form a natural cement between sea-soaked grains of sand. But, instead of using metabolic energy like mollusks do, the researchers used to spur the chemical reaction.

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A research team led by Rice University’s Yang Gao has uncovered new insights into the molecular mechanisms of ADAR1, a protein that regulates ribonucleic acid (RNA) induced immune responses. Their findings, published in Molecular Cell March 17, could open new pathways for treating autoimmune diseases and enhancing cancer immunotherapy.

ADAR1 converts adenosine to inosine in double-stranded RNA, a process essential for preventing unwarranted immune responses, yet the molecular basis of this editing had remained unclear. Through detailed biochemical profiling and structural analysis, researchers found that ADAR1’s editing activity depends on RNA sequence, duplex length and mismatches near the editing site. High-resolution structures of ADAR1 bound to RNA reveal its mechanisms for RNA binding, substrate selection and dimerization.

“Our study provides a comprehensive understanding of how ADAR1 recognizes and processes RNA,” said Gao, assistant professor of biosciences and a Cancer Prevention and Research Institute of Texas (CPRIT) Scholar. “These insights pave the way for novel therapeutic strategies targeting ADAR1-related diseases.”

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