{ "story_id": "24c940878ae687adf007dfac6abdfd36", "desk": "drm3", "revision": 1, "published_at": "2026-09-03T04:00:00.000Z", "content_hash": "f640946fd0f18dc4a6d79cb183c955c2132afdca0177f356c61d295cb817cdef", "hash_basis": "sha256 over `headline\\ndek\\nprose`, plus `\\n` + the canonical citations JSON when any source is placed, plus `\\n#blog` for blogs", "basis": { "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\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. [^3]\n\nRyan Leung, a researcher at Murdoch Children's Research Institute, said on 2026-09-02 that the findings help explain how the brain builds its complex network of cells, as cells must receive the right instructions at the right time to move and develop properly. [^4]\n\nFor ballistic-to-diffusive transport in turbulent plasmas, the surrogate matches test-particle distributions and reproduces the laboratory-measured variance scale. [^5]\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. [^6]\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. 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