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  1. Article ; Online: SpeckleNN: a unified embedding for real-time speckle pattern classification in X-ray single-particle imaging with limited labeled examples.

    Wang, Cong / Florin, Eric / Chang, Hsing Yin / Thayer, Jana / Yoon, Chun Hong

    IUCrJ

    2023  Volume 10, Issue Pt 5, Page(s) 568–578

    Abstract: With X-ray free-electron lasers (XFELs), it is possible to determine the three-dimensional structure of noncrystalline nanoscale particles using X-ray single-particle imaging (SPI) techniques at room temperature. Classifying SPI scattering patterns, or ` ... ...

    Abstract With X-ray free-electron lasers (XFELs), it is possible to determine the three-dimensional structure of noncrystalline nanoscale particles using X-ray single-particle imaging (SPI) techniques at room temperature. Classifying SPI scattering patterns, or `speckles', to extract single-hits that are needed for real-time vetoing and three-dimensional reconstruction poses a challenge for high-data-rate facilities like the European XFEL and LCLS-II-HE. Here, we introduce SpeckleNN, a unified embedding model for real-time speckle pattern classification with limited labeled examples that can scale linearly with dataset size. Trained with twin neural networks, SpeckleNN maps speckle patterns to a unified embedding vector space, where similarity is measured by Euclidean distance. We highlight its few-shot classification capability on new never-seen samples and its robust performance despite having only tens of labels per classification category even in the presence of substantial missing detector areas. Without the need for excessive manual labeling or even a full detector image, our classification method offers a great solution for real-time high-throughput SPI experiments.
    Language English
    Publishing date 2023-09-01
    Publishing country England
    Document type Journal Article
    ZDB-ID 2754953-7
    ISSN 2052-2525 ; 2052-2525
    ISSN (online) 2052-2525
    ISSN 2052-2525
    DOI 10.1107/S2052252523006115
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: PeakNet

    Wang, Cong / Li, Po-Nan / Thayer, Jana / Yoon, Chun Hong

    An Autonomous Bragg Peak Finder with Deep Neural Networks

    2023  

    Abstract: Serial crystallography at X-ray free electron laser (XFEL) and synchrotron facilities has experienced tremendous progress in recent times enabling novel scientific investigations into macromolecular structures and molecular processes. However, these ... ...

    Abstract Serial crystallography at X-ray free electron laser (XFEL) and synchrotron facilities has experienced tremendous progress in recent times enabling novel scientific investigations into macromolecular structures and molecular processes. However, these experiments generate a significant amount of data posing computational challenges in data reduction and real-time feedback. Bragg peak finding algorithm is used to identify useful images and also provide real-time feedback about hit-rate and resolution. Shot-to-shot intensity fluctuations and strong background scattering from buffer solution, injection nozzle and other shielding materials make this a time-consuming optimization problem. Here, we present PeakNet, an autonomous Bragg peak finder that utilizes deep neural networks. The development of this system 1) eliminates the need for manual algorithm parameter tuning, 2) reduces false-positive peaks by adjusting to shot-to-shot variations in strong background scattering in real-time, 3) eliminates the laborious task of manually creating bad pixel masks and the need to store these masks per event since these can be regenerated on demand. PeakNet also exhibits exceptional runtime efficiency, processing a 1920-by-1920 pixel image around 90 ms on an NVIDIA 1080 Ti GPU, with the potential for further enhancements through parallelized analysis or GPU stream processing. PeakNet is well-suited for expert-level real-time serial crystallography data analysis at high data rates.
    Keywords Physics - Instrumentation and Detectors ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-03-24
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: SpeckleNN

    Wang, Cong / Florin, Eric / Chang, Hsing-Yin / Thayer, Jana / Yoon, Chun Hong

    A unified embedding for real-time speckle pattern classification in X-ray single-particle imaging with limited labeled examples

    2023  

    Abstract: With X-ray free-electron lasers (XFELs), it is possible to determine the three-dimensional structure of noncrystalline nanoscale particles using X-ray single-particle imaging (SPI) techniques at room temperature. Classifying SPI scattering patterns, or " ... ...

    Abstract With X-ray free-electron lasers (XFELs), it is possible to determine the three-dimensional structure of noncrystalline nanoscale particles using X-ray single-particle imaging (SPI) techniques at room temperature. Classifying SPI scattering patterns, or "speckles", to extract single hits that are needed for real-time vetoing and three-dimensional reconstruction poses a challenge for high data rate facilities like European XFEL and LCLS-II-HE. Here, we introduce SpeckleNN, a unified embedding model for real-time speckle pattern classification with limited labeled examples that can scale linearly with dataset size. Trained with twin neural networks, SpeckleNN maps speckle patterns to a unified embedding vector space, where similarity is measured by Euclidean distance. We highlight its few-shot classification capability on new never-seen samples and its robust performance despite only tens of labels per classification category even in the presence of substantial missing detector areas. Without the need for excessive manual labeling or even a full detector image, our classification method offers a great solution for real-time high-throughput SPI experiments.
    Keywords Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-02-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Bi-cross validation for estimating spectral clustering hyper parameters

    Zohar, Sioan / Yoon, Chun-Hong

    2019  

    Abstract: One challenge impeding the analysis of terabyte scale x-ray scattering data from the Linac Coherent Light Source LCLS, is determining the number of clusters required for the execution of traditional clustering algorithms. Here we demonstrate that ... ...

    Abstract One challenge impeding the analysis of terabyte scale x-ray scattering data from the Linac Coherent Light Source LCLS, is determining the number of clusters required for the execution of traditional clustering algorithms. Here we demonstrate that previous work using bi-cross validation (BCV) to determine the number of singular vectors directly maps to the spectral clustering problem of estimating both the number of clusters and hyper parameter values. These results indicate that the process of estimating the number of clusters should not be divorced from the process of estimating other hyper parameters. Applying this method to LCLS x-ray scattering data enables the identification of dropped shots without manually setting boundaries on detector fluence and provides a path towards identifying rare and anomalous events.

    Comment: 5 pages, 4 figures
    Keywords Statistics - Machine Learning ; Computer Science - Machine Learning ; Physics - Accelerator Physics
    Subject code 006
    Publishing date 2019-08-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Augmenting x-ray single particle imaging reconstruction with self-supervised machine learning

    Chen, Zhantao / Wang, Cong / Gao, Mingye / Yoon, Chun Hong / Thayer, Jana B. / Turner, Joshua J.

    2023  

    Abstract: The development of X-ray Free Electron Lasers (XFELs) has opened numerous opportunities to probe atomic structure and ultrafast dynamics of various materials. Single Particle Imaging (SPI) with XFELs enables the investigation of biological particles in ... ...

    Abstract The development of X-ray Free Electron Lasers (XFELs) has opened numerous opportunities to probe atomic structure and ultrafast dynamics of various materials. Single Particle Imaging (SPI) with XFELs enables the investigation of biological particles in their natural physiological states with unparalleled temporal resolution, while circumventing the need for cryogenic conditions or crystallization. However, reconstructing real-space structures from reciprocal-space x-ray diffraction data is highly challenging due to the absence of phase and orientation information, which is further complicated by weak scattering signals and considerable fluctuations in the number of photons per pulse. In this work, we present an end-to-end, self-supervised machine learning approach to recover particle orientations and estimate reciprocal space intensities from diffraction images only. Our method demonstrates great robustness under demanding experimental conditions with significantly enhanced reconstruction capabilities compared with conventional algorithms, and signifies a paradigm shift in SPI as currently practiced at XFELs.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Image and Video Processing ; Physics - Applied Physics ; Physics - Computational Physics
    Subject code 006
    Publishing date 2023-11-28
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article: Skopi

    Peck, Ariana / Chang, Hsing-Yin / Dujardin, Antoine / Ramalingam, Deeban / Uervirojnangkoorn, Monarin / Wang, Zhaoyou / Mancuso, Adrian / Poitevin, Frédéric / Yoon, Chun Hong

    Journal of applied crystallography

    2022  Volume 55, Issue Pt 4, Page(s) 1002–1010

    Abstract: X-ray free-electron lasers (XFELs) have the ability to produce ultra-bright femtosecond X-ray pulses for coherent diffraction imaging of biomolecules. While the development of methods and algorithms for macromolecular crystallography is now mature, XFEL ... ...

    Abstract X-ray free-electron lasers (XFELs) have the ability to produce ultra-bright femtosecond X-ray pulses for coherent diffraction imaging of biomolecules. While the development of methods and algorithms for macromolecular crystallography is now mature, XFEL experiments involving aerosolized or solvated biomolecular samples offer new challenges in terms of both experimental design and data processing.
    Language English
    Publishing date 2022-07-15
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2020879-0
    ISSN 1600-5767 ; 0021-8898
    ISSN (online) 1600-5767
    ISSN 0021-8898
    DOI 10.1107/S1600576722005994
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Structural insights into functional properties of the oxidized form of cytochrome c oxidase.

    Ishigami, Izumi / Sierra, Raymond G / Su, Zhen / Peck, Ariana / Wang, Cong / Poitevin, Frederic / Lisova, Stella / Hayes, Brandon / Moss, Frank R / Boutet, Sébastien / Sublett, Robert E / Yoon, Chun Hong / Yeh, Syun-Ru / Rousseau, Denis L

    Nature communications

    2023  Volume 14, Issue 1, Page(s) 5752

    Abstract: Cytochrome c oxidase (CcO) is an essential enzyme in mitochondrial and bacterial respiration. It catalyzes the four-electron reduction of molecular oxygen to water and harnesses the chemical energy to translocate four protons across biological membranes. ...

    Abstract Cytochrome c oxidase (CcO) is an essential enzyme in mitochondrial and bacterial respiration. It catalyzes the four-electron reduction of molecular oxygen to water and harnesses the chemical energy to translocate four protons across biological membranes. The turnover of the CcO reaction involves an oxidative phase, in which the reduced enzyme (R) is oxidized to the metastable O
    MeSH term(s) Electron Transport Complex IV ; Protons ; Cell Membrane ; Crystallography, X-Ray ; Oxygen
    Chemical Substances Electron Transport Complex IV (EC 1.9.3.1) ; Protons ; Oxygen (S88TT14065)
    Language English
    Publishing date 2023-09-16
    Publishing country England
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-41533-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: Structural basis for functional properties of cytochrome

    Ishigami, Izumi / Sierra, Raymond G / Su, Zhen / Peck, Ariana / Wang, Cong / Poitevin, Frederic / Lisova, Stella / Hayes, Brandon / Moss, Frank R / Boutet, Sébastien / Sublett, Robert E / Yoon, Chun Hong / Yeh, Syun-Ru / Rousseau, Denis L

    bioRxiv : the preprint server for biology

    2023  

    Abstract: ... ...

    Abstract Cytochrome
    Language English
    Publishing date 2023-03-22
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.03.20.530986
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Book ; Online: Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics

    Chen, Zhantao / Peng, Cheng / Petsch, Alexander N. / Chitturi, Sathya R. / Okullo, Alana / Chowdhury, Sugata / Yoon, Chun Hong / Turner, Joshua J.

    2023  

    Abstract: Advanced experimental measurements are crucial for driving theoretical developments and unveiling novel phenomena in condensed matter and material physics, which often suffer from the scarcity of facility resources and increasing complexities. To address ...

    Abstract Advanced experimental measurements are crucial for driving theoretical developments and unveiling novel phenomena in condensed matter and material physics, which often suffer from the scarcity of facility resources and increasing complexities. To address the limitations, we introduce a methodology that combines machine learning with Bayesian optimal experimental design (BOED), exemplified with x-ray photon fluctuation spectroscopy (XPFS) measurements for spin fluctuations. Our method employs a neural network model for large-scale spin dynamics simulations for precise distribution and utility calculations in BOED. The capability of automatic differentiation from the neural network model is further leveraged for more robust and accurate parameter estimation. Our numerical benchmarks demonstrate the superior performance of our method in guiding XPFS experiments, predicting model parameters, and yielding more informative measurements within limited experimental time. Although focusing on XPFS and spin fluctuations, our method can be adapted to other experiments, facilitating more efficient data collection and accelerating scientific discoveries.
    Keywords Condensed Matter - Materials Science ; Computer Science - Machine Learning ; Physics - Computational Physics ; Physics - Data Analysis ; Statistics and Probability
    Subject code 612
    Publishing date 2023-06-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article: Reproducibility of protein x-ray diffuse scattering and potential utility for modeling atomic displacement parameters.

    Su, Zhen / Dasgupta, Medhanjali / Poitevin, Frédéric / Mathews, Irimpan I / van den Bedem, Henry / Wall, Michael E / Yoon, Chun Hong / Wilson, Mark A

    Structural dynamics (Melville, N.Y.)

    2021  Volume 8, Issue 4, Page(s) 44701

    Abstract: Protein structure and dynamics can be probed using x-ray crystallography. Whereas the Bragg peaks are only sensitive to the average unit-cell electron density, the signal between the Bragg peaks-diffuse scattering-is sensitive to spatial correlations in ... ...

    Abstract Protein structure and dynamics can be probed using x-ray crystallography. Whereas the Bragg peaks are only sensitive to the average unit-cell electron density, the signal between the Bragg peaks-diffuse scattering-is sensitive to spatial correlations in electron-density variations. Although diffuse scattering contains valuable information about protein dynamics, the diffuse signal is more difficult to isolate from the background compared to the Bragg signal, and the reproducibility of diffuse signal is not yet well understood. We present a systematic study of the reproducibility of diffuse scattering from isocyanide hydratase in three different protein forms. Both replicate diffuse datasets and datasets obtained from different mutants were similar in pairwise comparisons (Pearson correlation coefficient ≥0.8). The data were processed in a manner inspired by previously published methods using custom software with modular design, enabling us to perform an analysis of various data processing choices to determine how to obtain the highest quality data as assessed using unbiased measures of symmetry and reproducibility. The diffuse data were then used to characterize atomic mobility using a liquid-like motions (LLM) model. This characterization was able to discriminate between distinct anisotropic atomic displacement parameter (ADP) models arising from different anisotropic scaling choices that agreed comparably with the Bragg data. Our results emphasize the importance of data reproducibility as a model-free measure of diffuse data quality, illustrate the ability of LLM analysis of diffuse scattering to select among alternative ADP models, and offer insights into the design of successful diffuse scattering experiments.
    Language English
    Publishing date 2021-07-08
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2758684-4
    ISSN 2329-7778
    ISSN 2329-7778
    DOI 10.1063/4.0000087
    Database MEDical Literature Analysis and Retrieval System OnLINE

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