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- Bellisario, AlfredoUppsala universitet,Molekylär biofysik (författare)
Deep learning assisted phase retrieval and computational methods in coherent diffractive imaging
- BokEngelska2024
Förlag, utgivningsår, omfång ...
- Uppsala :Acta Universitatis Upsaliensis,2024
- 94 s.
- electronicrdacarrier
Nummerbeteckningar
- LIBRIS-ID:oai:DiVA.org:uu-526527
- ISBN:9789151321202
- https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-526527URI
Kompletterande språkuppgifter
- Språk:engelska
- Sammanfattning på:engelska &language:-1_t
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Klassifikation
Serie
- Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology,1651-6214 ;2400
Anmärkningar
- In recent years, advances in Artificial Intelligence and experimental techniques have revolutionized the field of structural biology. X-ray crystallography and Cryo-EM have provided unprecedented insights into the structures of biomolecules, while the unexpected success of AlphaFold has opened up new avenues of investigation. However, studying the dynamics of proteins at high resolution remains a significant obstacle, especially for fast dynamics. Single-particle imaging (SPI) or Flash X-ray Imaging (FXI) is an emerging technique that may enable the mapping of the conformational landscape of biological molecules at high resolution and fast time scale. This thesis discusses the potential of SPI/FXI, its challenges, recent experimental successes, and the advancements driving its development. In particular, machine learning and neural networks could play a vital role in fostering data analysis and improving SPI/FXI data processing. In Paper I, we discuss the problem of noise and detector masks in collecting FXI data. I simulated a dataset of diffraction patterns and used it to train a Convolutional Neural Network (U-Net) to restore data by denoising and filling in detector masks. As a natural continuation of this work, I trained another machine learning model in Paper II to estimate 2D protein densities from diffraction intensities. In the final chapter, corresponding to Paper III, we discuss another experimental method, time-resolved Small Angle X-ray Scattering (SAXS), and a new algorithm recently developed for SAXS data, the DENsity from Solution Scattering (DENSS) algorithm. I discuss the potential of DENSS in time-resolved SAXS and its application for structural fitting of AsLOV2, a Light-Oxygen-Voltage (LOV) protein domain from Avena sativa.
Ämnesord och genrebeteckningar
- NATURVETENSKAP Biologi Biofysik hsv//swe
- NATURAL SCIENCES Biological Sciences Biophysics hsv//eng
- NATURVETENSKAP Fysik Atom- och molekylfysik och optik hsv//swe
- NATURAL SCIENCES Physical Sciences Atom and Molecular Physics and Optics hsv//eng
- coherent diffractive X-ray imaging
- lensless imaging
- coherent X-ray diffractive imaging
- flash diffractive imaging
- single particle imaging
- phase retrieval
- X-ray free-electron laser
- XFEL
- FEL
- CXI
- CDI
- CXDI
- FXI
- Machine learning
- Deep learning
- Reinforcement learning
- Neural networks
- Convolutional Neural Networks
- Molekylär biovetenskap
- Molecular Life Sciences
Biuppslag (personer, institutioner, konferenser, titlar ...)
- Ekeberg, Tomas,Dr.1983-Uppsala universitet,Molekylär biofysik(Swepub:uu)tomek611 (preses)
- Maia, Filipe,ProfessorUppsala universitet,Molekylär biofysik(Swepub:uu)fimai103 (preses)
- Barty, Anton,Dr.Deutsches Elektronen-Synchrotron (DESY) Notkestrasse 85, 22607 Hamburg, Germany (opponent)
- Uppsala universitetMolekylär biofysik (creator_code:org_t)
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