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    "headline": "MCRI study reveals DLX2 gene controls timing of neuron formation in brain",
    "dek": "MCRI-led study published in Nature Communications shows DLX2 gene regulates when brain cells become neurons.",
    "prose": "A study led by Murdoch Children's Research Institute (MCRI) in Melbourne, published in Nature Communications, identifies the DLX/Notch axis as a critical signaling pathway controlling the timing of early brain development. [^1]\n\nRyan Leung stated that the gene DLX2 acts like a traffic controller during early brain development, ensuring young brain cells become neurons at the right time while preventing premature development into specialized support cells, based on experiments with mice genetically engineered to lack DLX1 and DLX2. [^2]\n\nA study published in Nature Communications in 2026 by researchers at the Institute of Industrial Science, The University of Tokyo, and collaborating institutions reported that controlling the electron spin orientation of hydrogen atoms affects hydrogen adsorption and abstraction reactions on a ferromagnetic nickel surface. [^3]\n\nA research team led by Assistant Professor Ryoji Miyazaki from Nara Institute of Science and Technology (NAIST), Japan, used cryo-electron microscopy to capture four distinct structural snapshots of the SurA-BAM complex, revealing how SurA delivers unfolded outer membrane proteins to the BAM complex. [^4]\n\nDavid Eisenstat, Professor and Neuro-oncology Group Leader at Murdoch Children's Research Institute, said the findings would inform future research into childhood brain cancers and neurodevelopmental disorders, and that many of the pathways disrupted in childhood brain tumors, including high-grade gliomas, are important for future research and treatment. [^5]\n\nThe researchers found that when DLX1 and DLX2 were removed in mice, there were changes in how brain cells developed and where they were located, and they also identified previously uncharacterized subregions of the developing forebrain. [^6]\n\nIn a deuterium-covered nickel surface, exposing it to the hydrogen beam triggered an abstraction reaction forming hydrogen-deuterium molecules; the reaction proceeded more efficiently when the hydrogen spin was oriented parallel to the surface, and the difference between spin orientations increased with the strength of the applied magnetic field. [^7]\n\nThe team reported that the spin-dependent adsorption effect disappeared when the experiment was performed on a nonmagnetic copper surface, indicating that the effect is associated with the magnetic nickel surface. [^8]\n\nGenerative diffusion models corrupt data with Gaussian noise and learn a reverse flow to structured states, making them useful surrogates for stochastic transport systems. [^9]\n\nThe method tracks the simulated kurtosis evolution without schedule tuning, enabling calibrated emulation and likelihood-based inference. [^10]\n\nThe proposed method prescribes the forward noising rate as the time derivative of the variance, which is often known from macroscopic theory or empirical scaling in transport systems. [^11]\n\nThe variance path is enforced by construction, while the learned score field represents how non-Gaussian structure inherited from entrance data is smoothed along that path. [^12]",
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the reaction proceeded more efficiently when the hydrogen spin was oriented parallel to the surface, and the difference between spin orientations increased with the strength of the applied magnetic field.\",\"source\":\"Phys.org\",\"instrument\":\"News\",\"claim_key\":null,\"published_at\":\"2026-09-03T09:00:07.000Z\",\"publisher_count\":1,\"sources\":[\"Phys.org\"]},{\"statement\":\"The team reported that the spin-dependent adsorption effect disappeared when the experiment was performed on a nonmagnetic copper surface, indicating that the effect is associated with the magnetic nickel surface.\",\"source\":\"Phys.org\",\"instrument\":\"News\",\"claim_key\":null,\"published_at\":\"2026-09-03T09:00:07.000Z\",\"publisher_count\":1,\"sources\":[\"Phys.org\"]},{\"statement\":\"Generative diffusion models corrupt data with Gaussian noise and learn a reverse flow to structured states, making them useful surrogates for stochastic transport systems.\",\"source\":\"arXiv.org\",\"instrument\":\"News\",\"claim_key\":null,\"published_at\":\"2026-09-03T04:00:00.000Z\",\"publisher_count\":1,\"sources\":[\"arXiv.org\"]},{\"statement\":\"The method tracks the simulated kurtosis evolution without schedule tuning, enabling calibrated emulation and likelihood-based inference.\",\"source\":\"arXiv.org\",\"instrument\":\"News\",\"claim_key\":null,\"published_at\":\"2026-09-03T04:00:00.000Z\",\"publisher_count\":1,\"sources\":[\"arXiv.org\"]},{\"statement\":\"The proposed method prescribes the forward noising rate as the time derivative of the variance, which is often known from macroscopic theory or empirical scaling in transport systems.\",\"source\":\"arXiv.org\",\"instrument\":\"News\",\"claim_key\":null,\"published_at\":\"2026-09-03T04:00:00.000Z\",\"publisher_count\":1,\"sources\":[\"arXiv.org\"]},{\"statement\":\"The variance path is enforced by construction, while the learned score field represents how non-Gaussian structure inherited from entrance data is smoothed along that path.\",\"source\":\"arXiv.org\",\"instrument\":\"News\",\"claim_key\":null,\"published_at\":\"2026-09-03T04:00:00.000Z\",\"publisher_count\":1,\"sources\":[\"arXiv.org\"]}]",
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