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Normal view

MMSDH facilitates ACSL4 propionylation to counteract ferroptosis upon hypoxia and impairs PDAC chemotherapy efficacy

Nature Cancer, Published online: 11 September 2026; doi:10.1038/s43018-026-01236-w

Zheng et al. describe how hypoxia-induced methylmalonate semialdehyde dehydrogenase lactylation promotes acyl-CoA synthetase long-chain family member 4 propionylation and degradation, thereby suppressing ferroptosis induced by chemotherapy, and develop a blocking peptide that increased chemotherapy efficacy in pancreatic ductal adenocarcinoma.

A helicase-fused Cas9 improves large-size fragment knockin

By fusing MCM5, a subunit of the eukaryotic MCM2–7 helicase complex, to the N terminus of spCas9 (MCCas), the MCCas fusion protein enhances large-size fragment knockin via homologous recombination, reduces insertions and deletions (indels), and enables efficient large-size fragment insertions in human cells and rabbit embryos.

Theoretical Analysis of Sparse Optimization with Reparameterization, Weight Decay, and Adaptive Learning Rate

arXiv:2605.25134v1 Announce Type: cross Abstract: Sparse optimization is a fundamental challenge in various practical applications. A popular approach to sparse optimization is $\ell_p$ regularization. However, it may encounter optimization instability due to the unbounded gradients when $0

The Devil is in the Condition Numbers: Why is GLU Better than non-GLU Structure?

arXiv:2605.20749v2 Announce Type: replace-cross Abstract: Gated Linear Units (GLU) and their variants are widely adopted in modern open-source large language model architectures and consistently outperform their non-gated counterparts, yet the underlying reasons for this advantage remain unclear. In this work, we study GLU by analyzing two-layer networks in the neural tangent kernel (NTK) regime. Our analysis reveals that the GLU structure reshapes the NTK spectrum, leading to a smaller condition number and a more compact eigenvalue distribution. Building on this finding, we further analyze the resulting training dynamics and show how the reshaped spectrum leads to faster convergence of GLU models, including a characteristic loss-crossing phenomenon observed between GLU and non-GLU models. Finally, we empirically observe that GLU has limited impact in reducing the generalization gap on various models, including ViT and GPT-2, suggesting that its primary benefit lies in accelerating optimization rather than reducing the generalization gap. The code is available at: https://github.com/Zemdalk/GLU-NTK.

A unified deep learning framework for cross-platform harmonization of multi-tracer PET quantification in neurodegenerative disease

npj Digital Medicine, Published online: 30 March 2026; doi:10.1038/s41746-026-02570-0

A unified deep learning framework for cross-platform harmonization of multi-tracer PET quantification in neurodegenerative disease
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