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Accurate characterization of high-power laser parameters, especially the near-field and far-field distributions, is crucial for inertial confinement fusion experiments. In this paper, we propose a method for computationally reconstructing the complex amplitude of high-power laser beams using modified coherent modulation imaging. This method has the advantage of being able to simultaneously calculate both the near-field (intensity and wavefront/phase) and far-field (focal-spot) distributions using the reconstructed complex amplitude. More importantly, the focal-spot distributions at different focal planes can also be calculated. To verify the feasibility, the complex amplitude optical field of the high-power pulsed laser was measured after static aberrations calibration. Experimental results also indicate that the near-field wavefront resolution of this method is higher than that of the Hartmann measurement. In addition, the far-field focal spot exhibits a higher dynamic range (176 dB) than that of traditional direct imaging (62 dB).
This review article describes the co-evolution of structural biology as a discipline and the Protein Data Bank (PDB), established in 1971 as the first open-access data resource in biology by like-minded structural scientists. As the PDB archive grew in size and scope to encompass macromolecular crystallography, NMR spectroscopy, and cryo-electron microscopy, new technologies were developed to ingest, validate, curate, store, and distribute the information. Community engagement ensured that the needs of structural biologists (data depositors) and data consumers were met. Today, the archive houses more than 230,000 experimentally determined structures of proteins, nucleic acids, and macromolecular machines and their complexes with one another and small-molecule ligands. Aggregate costs of PDB data preservation are ~1% of the cost of structure determination. The enormous impact of PDB data on basic and applied research and education across the natural and medical sciences is presented and highlighted with illustrative examples. Enablement of de novo protein structure prediction (AlphaFold2, RoseTTAfold, OpenFold, etc.) is the most widely appreciated benefit of having a corpus of rigorously validated, expertly curated 3D biostructure data.