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Johannes Gutenberg University Mainz

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  1. bioRxiv

    Single-cell perturbation prediction drops the pretraining crutch: GFM wires knowledge graphs into flow matching and trains up to 30x faster

    Across several large Perturb-seq datasets, a generative framework called Graph-guided Flow Matching (GFM) combines three biological knowledge graphs, covering Gene Ontology, protein interactions and perturbation similarity, with latent flow matching to predict single-cell transcriptomes after unseen perturbations. It outperforms a linear baseline and the deep-learning models CellFlow, GEARS and scGPT on unseen single-gene perturbations, trains up to 30 times faster, and keeps its advantage as training data shrink; on two-gene perturbations it does better when neither gene was seen in training.

  2. After reviewing 922 citations, 41 international experts extended the acute exacerbation definition from IPF to all fibrotic interstitial lung diseases and proposed a new acute respiratory worsening framework

    An international multidisciplinary working group of 41 experts systematically reviewed 922 citations up to March 17, 2025, revised the 2016 acute exacerbation (AE) definition—previously limited to idiopathic pulmonary fibrosis (IPF)—into an AE-fILD definition applicable across all fibrotic interstitial lung diseases (anchored in diffuse alveolar damage with or without organizing pneumonia), proposed the broader concept of acute respiratory worsening (ARW) to capture acute deteriorations not attributable to DAD, and summarized evidence on epidemiology, risk factors, prognosis, and management while discussing AE as a clinical trial endpoint.

  3. arXiv

    A hidden current date in system prompts shifts nine models' scores by up to 14% and reshuffles leaderboards

    Holding all other settings fixed and varying only the hidden current date injected into the system prompt across every day of 2024, this study of 9 recent LLMs and 6 datasets finds accuracy swings of up to 6% on multiple-choice QA, 14% on math reasoning, 7% on code generation, and 2.84 BLEU on machine translation, with model rankings reordering, an effect larger than batch size or numerical precision and one that chain-of-thought prompting amplifies rather than reduces.

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