arXiv:2510.20486v2 Announce Type: replace-cross Abstract: Imbalanced labels can cause frequent samples to dominate AI-based quantitative remote sensing, degrading rare-event retrieval. In rain-rate retrieval based on satellite infrared brightness temperatures, this imbalance leads to systematic underestimation of rare high-intensity rainfall. In this study, Hurdle-Retrieval Model Imbalanced Learning (RMIL) is proposed. Following a divide-and-conquer strategy, Hurdle-RMIL separates zero inflation from the long-tailed distribution of positive rain. A hurdle model handles zero inflation, whereas RMIL exploits invariance under fixed observation conditions of the rainfall-to-satellite forward process to derive a Bayes-based transformation linking conditional distributions under naturally long-tailed and hypothetical balanced rainfall. This transformation enables the balanced-distribution model to be learned from natural samples without constructing a balanced dataset. Comparisons with conventional learning, classification-regression modeling, cost-sensitive learning, and generative learning using test data from multiple regions in China show that Hurdle-RMIL mitigates systematic underestimation and improves detection of rare high-intensity and extreme rainfall without markedly degrading lower-threshold accuracy. At 0.1-10 mm per hour, its root mean square error remains close to those of the best baselines, and it yields the highest equitable threat score (ETS) at most evaluated thresholds, with its advantage becoming more pronounced at high thresholds. At 30 mm per hour, its ETS is 0.051 versus 0.015 for the best baseline, and its mean error is -25.41 mm per hour versus -28.98 mm per hour. Case studies further show improved representations of rainfall intensity and spatial extent, demonstrating that Hurdle-RMIL effectively addresses rainfall-distribution imbalance and improves the retrieval of rare high-intensity rainfall.

Oncogenesis, Published online: 21 August 2026; doi:10.1038/s41389-026-00650-0

CD44v5 promotes triple-negative breast cancer cisplatin resistance by enhancing IL-4/IL-4Rα/STAT3 pathway and stabilizing membranous SLC7A11

Chemically induced proximity of lysine acetyltransferases (KATs) with BCL6 reprograms epigenetic signaling to eliminate lymphoma tumors. Structural and mechanistic studies demonstrate that fortuitous protein-protein contacts convert proximity induction into targeted changes in chromatin, revealing a key mechanism by which small molecules can co-opt oncogenic transcriptional regulators to elicit malignant cell death.

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10488-6

Author Correction: Oncogene ablation-resistant pancreatic cancer cells depend on mitochondrial function

Cell Death Discovery, Published online: 16 March 2026; doi:10.1038/s41420-026-03025-x

Lysine attenuates acute lung injury by restoring α-tubulin acetylation and ciliary activity

arXiv:2602.19114v1 Announce Type: cross Abstract: This paper introduces the Kaiwu-PyTorch-Plugin (KPP) to bridge Deep Learning and Photonic Quantum Computing across multiple dimensions. KPP integrates the Coherent Ising Machine into the PyTorch ecosystem, addressing classical inefficiencies in Energy-Based Models. The framework facilitates quantum integration in three key aspects: accelerating Boltzmann sampling, optimizing training data via Active Sampling, and constructing hybrid architectures like QBM-VAE and Q-Diffusion. Empirical results on single-cell and OpenWebText datasets demonstrate KPPs ability to achieve SOTA performance, validating a comprehensive quantum-classical paradigm.