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The Role of Lysophosphatidic Acid in Neuropsychiatric and Neurodegenerative Disorders

Individuals suffering from diverse neuropsychiatric and neurodegenerative disorders often have comparable symptoms, which may underline the implication of shared hereditary influences and the same biological processes. Lysophosphatidic acid (LPA) is a bioactive phospholipid and a crucial regulator of the development of adult neuronal systems; hence, it may play an important role in the onset of certain diseases such as Alzheimer’s, Parkinson’s disease, and schizophrenia. During development, LPA signaling regulates many cellular processes such as proliferation, survival, migration, differentiation, cytoskeleton reorganization, and DNA synthesis. So far, six lysophosphatidic acid receptors that respond to LPA have been discovered and categorized based on their homology.

Tabby’s star may be orbited by a planetary-mass companion

By analyzing data from NASA’s Transiting Exoplanet Survey Satellite (TESS) and conducting new radial velocity measurements, U.K. and French astronomers have found evidence that the mysterious Tabby’s star may be orbited by an object about nine times more massive than Jupiter. The new findings are published on the preprint server arXiv.

KIC 8,462,852, also known as Tabby’s star (after the discoverer Tabetha S. Boyajian), is a peculiar F3V main-sequence star showcasing deep, irregular flux variations of up to 20% in its light curve. The star is about 50% larger and more massive than the sun, with an effective temperature of 6,750 K. In 2021, it was found that Tabby’s star is part of a binary system with a red dwarf companion, which is about half the size and mass of our sun.

Several hypotheses have been proposed to explain the irregular dips in the light curve of Tabby’s star, including a family of exocomets or planetesimal fragments. It was even proposed that the flux variations could be caused by extraterrestrial megastructures, which was later debunked. However, the origin of these dips remains uncertain.

Dreams drain energy: The REM sleep paradox

The brain demands a lot of energy compared with other organs. However, it can also make do when energy supplies are scarce, flexibly processing information using what is available. How the brain resourcefully allocates this limited energy across internal states remains a key question in neuroscience.

Sleep provides a useful window into answering this question. Although sleep is associated with rest, the brain remains highly active. This is especially true during rapid eye movement (REM) sleep, the stage closely linked to dreaming and memory processing. REM sleep is sometimes called “paradoxical sleep” because the body is largely still while the brain shows wake-like activity. Researchers at Tohoku University have now uncovered another paradox within REM sleep: While energy supply to the dreaming brain appears to rise, the energy molecule used directly by neurons falls. The findings are published in Communications Biology.

“Ever felt exhausted after a vivid dream?” asks Professor Ko Matsui of Tohoku University. “Sleep may appear peaceful, but the brain is highly active—especially when dreaming. We were intrigued by this paradox and wanted to look into the scientific basis behind why dreaming is somehow tiring.”

Inspired by how children learn, new AI framework learns to theorize the world from observations

A KAIST research team has developed a next-generation world model, an internal model an AI builds to understand and predict the world, that learns executable theories from observation alone.

The team led by Professor Sungjin Ahn from the School of Computing proposed a new learning paradigm called Learning-to-Theorize (L2T), which trains AI to theorize how the world works using only observed information. The team also built the Neural Theorizer (NEO), a neural network-based model that implements this paradigm.

The research was presented at the 43rd International Conference on Machine Learning (ICML 2026), held in Seoul from July 6–11. The paper, published on the arXiv preprint server, was also selected for the Best Paper Award at the Compositional Learning Workshop.

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