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@ARTICLE{Zhuang:473658,
      author       = {Zhuang, Yulong and Awel, Salah and Barty, Anton and Bean,
                      Richard and Bielecki, Johan and Bergemann, Martin and
                      Daurer, Benedikt and Ekeberg, Tomas and Estillore, Armando
                      D. and Fangohr, Hans and Giewekemeyer, Klaus and Hunter,
                      Mark S. and Karnevskiy, Mikhail and Kirian, Richard and
                      Kirkwood, Henry and Kim, Yoonhee and Koliyadu, Jayanath and
                      Lange, Holger and Letrun, Romain and Lübke, Jannik and
                      Mall, Abhishek and Michelat, Thomas and Morgan, Andrew J.
                      and Roth, Nils and Samanta, Amit K. and Sato, Tokushi and
                      Shen, Zhou and Sikorski, Marcin and Schulz, Florian and
                      Spence, John C. H. and Vagovic, Patrik and Wollweber, Tamme
                      and Worbs, Lena and Xavier, P. Lourdu and Yefanov, Oleksandr
                      and Maia, Filipe R. N. C. and Horke, Daniel A. and Küpper,
                      Jochen and Loh, Duane and Mancuso, Adrian and Chapman, Henry
                      N. and Ayyer, Kartik},
      title        = {{U}nsupervised learning approaches to characterizing
                      heterogeneous samples using {X}-ray single-particle imaging},
      journal      = {IUCrJ},
      volume       = {9},
      number       = {2},
      issn         = {2052-2525},
      address      = {Chester},
      reportid     = {PUBDB-2022-00171},
      pages        = {204 - 214},
      year         = {2022},
      abstract     = {One of the outstanding analytical problems in X-ray
                      single-particle imaging (SPI) is the classification of
                      structural heterogeneity, which is especially difficult
                      given the low signal-to-noise ratios of individual patterns
                      and the fact that even identical objects can yield patterns
                      that vary greatly when orientation is taken into
                      consideration. Proposed here are two methods which
                      explicitly account for this orientation-induced variation
                      and can robustly determine the structural landscape of a
                      sample ensemble. The first, termed common-line principal
                      component analysis (PCA), provides a rough classification
                      which is essentially parameter free and can be run
                      automatically on any SPI dataset. The second method,
                      utilizing variation auto-encoders (VAEs), can generate 3D
                      structures of the objects at any point in the structural
                      landscape. Both these methods are implemented in combination
                      with the noise-tolerant expand–maximize–compress (EMC)
                      algorithm and its utility is demonstrated by applying it to
                      an experimental dataset from gold nanoparticles with only a
                      few thousand photons per pattern. Both discrete structural
                      classes and continuous deformations are recovered. These
                      developments diverge from previous approaches of extracting
                      reproducible subsets of patterns from a dataset and open up
                      the possibility of moving beyond the study of homogeneous
                      sample sets to addressing open questions on topics such as
                      nanocrystal growth and dynamics, as well as phase
                      transitions which have not been externally triggered.},
      cin          = {FS-CFEL-1 / XFEL-User / MPSD / FS-CFEL-CMI},
      ddc          = {530},
      cid          = {I:(DE-H253)FS-CFEL-1-20120731 /
                      I:(DE-H253)XFEL-User-20170713 / I:(DE-H253)MPSD-20120731 /
                      I:(DE-H253)FS-CFEL-CMI-20220405},
      pnm          = {631 - Matter – Dynamics, Mechanisms and Control
                      (POF4-631) / DFG project 390715994 - EXC 2056: CUI: Advanced
                      Imaging of Matter (390715994) / DFG project 194651731 - EXC
                      1074: Hamburger Zentrum für ultraschnelle Beobachtung
                      (CUI): Struktur, Dynamik und Kontrolle von Materie auf
                      atomarer Skala (194651731) / COMOTION - Controlling the
                      Motion of Complex Molecules and Particles (614507) / IVF -
                      Impuls- und Vernetzungsfonds (IVF-20140101)},
      pid          = {G:(DE-HGF)POF4-631 / G:(GEPRIS)390715994 /
                      G:(GEPRIS)194651731 / G:(EU-Grant)614507 /
                      G:(DE-HGF)IVF-20140101},
      experiment   = {EXP:(DE-H253)XFEL-SPB-20150101},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35371510},
      UT           = {WOS:000795673400007},
      doi          = {10.1107/S2052252521012707},
      url          = {https://bib-pubdb1.desy.de/record/473658},
}