MediaWiki API result
This is the HTML representation of the JSON format. HTML is good for debugging, but is unsuitable for application use.
Specify the format parameter to change the output format. To see the non-HTML representation of the JSON format, set format=json.
See the complete documentation, or the API help for more information.
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{
"logid": 3604,
"ns": 0,
"title": "2025Yan Foundation",
"pageid": 3103,
"logpage": 3103,
"revid": 5144,
"params": {},
"type": "create",
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"user": "WikiSysop",
"timestamp": "2026-01-16T15:49:12Z",
"comment": "Created page with \"== Citation == Yan, Y., Fan, S., Yuan, F. and Shen, H. 2025. A comprehensive foundation model for cryo-EM image processing. Nature Methods. 23, (2025), 88\u201395. == Abstract == Cryogenic electron microscopy (cryo-EM) has become a premier technique for determining high-resolution structures of biological macromolecules. However, its broad application is constrained by the demand for specialized expertise. Here, to address this limitation, we introduce the Cryo-EM Image...\""
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{
"logid": 3603,
"ns": 0,
"title": "2025Ma DeepTracer",
"pageid": 3102,
"logpage": 3102,
"revid": 5142,
"params": {},
"type": "create",
"action": "create",
"user": "WikiSysop",
"timestamp": "2026-01-16T15:37:19Z",
"comment": "Created page with \"== Citation == Ma, X. and Si, D. 2025. Beyond current boundaries: Integrating deep learning and AlphaFold for enhanced protein structure prediction from low-resolution cryo-EM maps. Computational Biology and Chemistry. 119, (2025), 108494. == Abstract == Constructing atomic models from cryo-electron microscopy (cryo-EM) maps is a crucial yet intricate task in structural biology. While advancements in deep learning, such as convolutional neural networks (CNNs) and grap...\""
},
{
"logid": 3602,
"ns": 0,
"title": "2025Matinyan TRPX",
"pageid": 3101,
"logpage": 3101,
"revid": 5140,
"params": {},
"type": "create",
"action": "create",
"user": "WikiSysop",
"timestamp": "2026-01-09T10:16:46Z",
"comment": "Created page with \"== Citation == Matinyan, S., Filipcik, P., Waterman, D., Owen, C. and Abrahams, J. 2025. TRPXv2. 0: superfast, parallel compression of diffraction patterns and images, with native Python and HDF5 support. Ultramicroscopy. (2025), 114298. == Abstract == Scientific data in structural biology are being produced faster and in larger volumes than can be comfortably stored, processed, or shared. To address this challenge, we introduced the next generation TERSE/PROLIX (TRPX...\""
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{
"logid": 3601,
"ns": 0,
"title": "2026Heymann Ewald",
"pageid": 3100,
"logpage": 3100,
"revid": 5138,
"params": {},
"type": "create",
"action": "create",
"user": "WikiSysop",
"timestamp": "2026-01-09T08:31:28Z",
"comment": "Created page with \"== Citation == Heymann, J.B. 2026. The relationship between the Ewald sphere and exit wave explored using focal series electron micrographs. IUCrJ. 13, 1 (2026). == Abstract == CryoEM reconstructions must be performed along the Ewald spheres to achieve resolutions beyond the projection approximation limit. The linear theory of image formation models the scattering from the specimen and focusing by the objective lens as two conjugate Ewald spheres that correspond to th...\""
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{
"logid": 3600,
"ns": 0,
"title": "2026Mulvaney CASP16",
"pageid": 3099,
"logpage": 3099,
"revid": 5136,
"params": {},
"type": "create",
"action": "create",
"user": "WikiSysop",
"timestamp": "2026-01-07T08:14:42Z",
"comment": "Created page with \"== Citation == Mulvaney, T., Kryshtafovych, A. and Topf, M. 2026. Cryo-EM Analysis in CASP16. Proteins: Structure, Function, and Bioinformatics. (2026). == Abstract == Since CASP13, experimentalists have been encouraged to provide their cryo-EM data along with the derived atomic models to the CASP organizers to aid assessment. In CASP16, 38 cryo-EM datasets were provided for assessment, which represented most cryo-EM targets. The corresponding targets typically compri...\""
},
{
"logid": 3599,
"ns": 0,
"title": "2025Tang CryoLike",
"pageid": 3098,
"logpage": 3098,
"revid": 5134,
"params": {},
"type": "create",
"action": "create",
"user": "WikiSysop",
"timestamp": "2026-01-07T08:06:14Z",
"comment": "Created page with \"== Citation == Tang, W.S., Soules, J., Rangan, A. and Cossio, P. 2025. CryoLike: a Python package for cryo-electron microscopy image-to-structure likelihood calculations. Biological Crystallography. 81, 12 (2025). == Abstract == Extracting conformational heterogeneity from cryo-electron microscopy (cryo-EM) images is particularly challenging for flexible biomolecules, where traditional 3D classification approaches often fail. Over the past few decades, advancements in...\""
},
{
"logid": 3598,
"ns": 0,
"title": "2025Li EMProt",
"pageid": 3097,
"logpage": 3097,
"revid": 5132,
"params": {},
"type": "create",
"action": "create",
"user": "WikiSysop",
"timestamp": "2025-12-30T09:25:23Z",
"comment": "Created page with \"== Citation == Li, T., Chen, J., Li, H., Cao, H. and Huang, S.-Y. 2025. EMProt improves structure determination from cryo-EM maps. Nature Structural & Molecular Biology. (2025), 1\u201310. == Abstract == Cryo-electron microscopy (cryo-EM) has become the mainstream technique for macromolecular structure determination. However, because of intrinsic resolution heterogeneity, accurate modeling of all-atom structure from cryo-EM maps remains challenging even for maps at near-...\""
},
{
"logid": 3597,
"ns": 0,
"title": "2025Leone Review",
"pageid": 3096,
"logpage": 3096,
"revid": 5130,
"params": {},
"type": "create",
"action": "create",
"user": "WikiSysop",
"timestamp": "2025-12-30T08:48:07Z",
"comment": "Created page with \"== Citation == Leone, V. and Marinelli, F. 2025. From snapshots to ensembles: Integrating experimental data and dynamics. Current Opinion in Structural Biology. 95, (2025), 103155. == Abstract == Protein function arises from the interplay of structure, dynamics, and biomolecular interactions. Despite advances in cryo-EM and AI-based structure prediction, capturing dynamic and energetic features remains a challenge. Biophysical methods like NMR, EPR, HDX-MS, SAXS, and...\""
},
{
"logid": 3596,
"ns": 0,
"title": "2025Sharma DataCollection",
"pageid": 3095,
"logpage": 3095,
"revid": 5128,
"params": {},
"type": "create",
"action": "create",
"user": "Vilas",
"timestamp": "2025-12-29T14:39:14Z",
"comment": "Created page with \"== Citation == K. Sharma, M.J. Borgnia, \"Advances in automation for cryo-electron tomography data collection\", Current Opinion in Structural Biology, Volume 95, 103192, 2025 == Abstract == Cryo-electron microscopy has become the preferred method for determining structures of macromolecular complexes both in isolation, using single particle analysis, and in their cellular contexts, using cryo-electron tomography (Cryo-ET) combined with subvolume averaging (SVA). Collec...\""
},
{
"logid": 3595,
"ns": 0,
"title": "2025Bartesaghi StrategiesHet3D",
"pageid": 3094,
"logpage": 3094,
"revid": 5126,
"params": {},
"type": "create",
"action": "create",
"user": "Vilas",
"timestamp": "2025-12-29T14:29:39Z",
"comment": "Created page with \"== Citation == A. Bartesaghi, \"Strategies for studying discrete heterogeneity in situ using cryo-electron tomography\", Current Opinion in Structural Biology, Volume 95, 103186, 2025 == Abstract == Structural variability plays a crucial role in enabling biological function, as the ability of proteins to adopt multiple conformations allows them to perform diverse cellular tasks. Cryo-electron tomography combined with subtomogram averaging and classification has emerged...\""
}
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}