{
  "abstract": "Cryo-electron microscopy (cryo-EM) has emerged as a leading technology for determining the structures of biological macromolecules. However, map quality issues such as noise and loss of contrast hinder accurate map interpretation. Traditional and deep learning-based post-processing methods offer improvements but face limitations particularly in handling map heterogeneity. Here, we present EMReady2, an extension of our previous EMReady cryo-EM map improvement method. EMReady2 introduces a fast Mamba-based dual-branch UNet architecture to jointly capture local and global features. In addition, EMReady2 also uses a local resolution-guided learning strategy to address map local quality heterogeneity, and significantly extends the training set. These advances render EMReady2 applicable to a broader range of cryo-EM maps, including those containing nucleic acids, medium-resolution maps, and cryo-electron tomography (cryo-ET) maps. EMReady2 is evaluated on 136 diverse maps at 2.0-10.0 Å resolutions, and compared with existing map post-processing methods. Our results demonstrate that EMReady2 exhibits state-of-the-art performance in both map quality and map interpretability improvement while much reducing the computational cost.",
  "abstract_shown": true,
  "affiliations": [
    "School of Physics, Huazhong University of Science and Technology, Wuhan, China",
    "School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China",
    "Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA. jiahua.he@ucsf.edu",
    "School of Physics, Huazhong University of Science and Technology, Wuhan, China. jiahua.he@ucsf.edu",
    "School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China. xgwang@hust.edu.cn",
    "School of Physics, Huazhong University of Science and Technology, Wuhan, China. huangsy@hust.edu.cn"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Hong Cao"
    },
    {
      "affs": [
        1
      ],
      "name": "Yueting Zhu"
    },
    {
      "affs": [
        0
      ],
      "name": "Tao Li",
      "orcid": "0000-0002-3421-0714"
    },
    {
      "affs": [
        0
      ],
      "name": "Ji Chen"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Jiahua He",
      "orcid": "0000-0002-9075-4978"
    },
    {
      "affs": [
        4
      ],
      "name": "Xinggang Wang",
      "orcid": "0000-0001-6732-7823"
    },
    {
      "affs": [
        5
      ],
      "name": "Sheng‐You Huang",
      "orcid": "0000-0002-4209-4565"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1f1e8v1h3dhke.bib",
  "cited_by": 21,
  "doi": "10.1038/s41467-026-71794-1",
  "fulltext_url": null,
  "id": "1f1e8v1h3dhke",
  "issns": [
    "2041-1723"
  ],
  "landing_url": "https://doi.org/10.1038/s41467-026-71794-1",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1315|Advanced Electron Microscopy Techniques and Applications"
    ]
  ],
  "openalex": "W7161244936",
  "pdf_url": "https://www.nature.com/articles/s41467-026-71794-1_reference.pdf",
  "pmcid": "PMC13376884",
  "pmid": "42140917",
  "published": "2026-05-16",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/1f1e8v1h3dhke.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:26:46Z",
      "native_id": "W7161244936",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s41467-026-71794-1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1315|Advanced Electron Microscopy Techniques and Applications",
      "subfield": "structural-biology"
    }
  ],
  "title": "EMReady2: improvement of cryo-EM and cryo-ET maps by local quality-aware deep learning with Mamba",
  "title_text": "EMReady2: improvement of cryo-EM and cryo-ET maps by local quality-aware deep learning with Mamba",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:05Z",
  "url": "https://research.draconitic.com/paper/1f1e8v1h3dhke",
  "venue": "Nature Communications",
  "versions": []
}