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@ARTICLE{Gao:620200,
      author       = {Gao, Yunyun and Ginn, Helen and Thorn, Andrea},
      title        = {{R}obust and automatic beamstop shadow outlier rejection:
                      combining crystallographic statistics with modern clustering
                      under a semi-supervised learning strategy},
      journal      = {Acta crystallographica / Section D},
      volume       = {80},
      number       = {10},
      issn         = {2059-7983},
      address      = {Bognor Regis},
      publisher    = {Wiley},
      reportid     = {PUBDB-2025-00076},
      pages        = {722-732},
      year         = {2024},
      abstract     = {During the automatic processing of crystallographic
                      diffraction experiments, beamstop shadows are often
                      unaccounted for or only partially masked. As a result of
                      this, outlier reflection intensities are integrated, which
                      is a known issue. Traditional statistical diagnostics have
                      only limited effectiveness in identifying these outliers,
                      here termed Not-Excluded-unMasked-Outliers (NEMOs). The
                      diagnostic tool AUSPEX allows visual inspection of NEMOs,
                      where they form a typical pattern: clusters at the
                      low-resolution end of the AUSPEX plots of intensities or
                      amplitudes versus resolution. To automate NEMO detection, a
                      new algorithm was developed by combining data statistics
                      with a density-based clustering method. This approach
                      demonstrates a promising performance in detecting NEMOs in
                      merged data sets without disrupting existing data-reduction
                      pipelines. Re-refinement results indicate that excluding the
                      identified NEMOs can effectively enhance the quality of
                      subsequent structure-determination steps. This method offers
                      a prospective automated means to assess the efficacy of a
                      beamstop mask, as well as highlighting the potential of
                      modern pattern-recognition techniques for automating outlier
                      exclusion during data processing, facilitating future
                      adaptation to evolving experimental strategies.},
      cin          = {FS-CFEL-1 / FS-CFEL-1-DNMX},
      ddc          = {530},
      cid          = {I:(DE-H253)FS-CFEL-1-20120731 /
                      I:(DE-H253)FS-CFEL-1-DNMX-20231108},
      pnm          = {633 - Life Sciences – Building Blocks of Life: Structure
                      and Function (POF4-633) / VH-NG-19-02 - Working with RoPE:
                      Representation of Protein Entities $(2023_IVF-VH-NG-19-02)$
                      / DFG project G:(GEPRIS)390715994 - EXC 2056: CUI: Advanced
                      Imaging of Matter (390715994)},
      pid          = {G:(DE-HGF)POF4-633 / $G:(DE-HGF)2023_IVF-VH-NG-19-02$ /
                      G:(GEPRIS)390715994},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39361355},
      UT           = {WOS:001329882300002},
      doi          = {10.1107/S2059798324008519},
      url          = {https://bib-pubdb1.desy.de/record/620200},
}