EMPIAR-11601 - Porcine uroplakin complex [multiple data sets in TIFF format]

EMPIAR-11601

Porcine uroplakin complex [multiple data sets in TIFF format]

Publication:

Unveiling Liquid-Crystalline Lipids in the Urothelial Membrane through Cryo-EM

Yanagisawa H, Kita Y, Oda T, Kikkawa M

(---)

Related EMDB entry:

EMD-36340

Deposited:

2023-05-22

Released:

2023-09-14

Last modified:

2023-09-14

Imageset size:

1.43 TB

Imageset DOI:

10.6019/EMPIAR-11601

Experimental metadata:

Download xml json

Deposited via PDBj

Contains:

micrographs - multiframe

Image Sets

1. Sep20-2022

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

2059

Image size:

(5760, 4092)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(1.35, 1.35)

Details:

Please use CountRef_uplk_146-1_0.0_Sep19_15.13.58_X-1Y-1-0.mrc as a gain reference without rotation or flipping.
The images were recorded with 3x3 image shifts but the beam tilt was not refined per direction.
The "XnnYmm" part of the file name represents the image shift direction.

Cs 2.7 mm
300 kV
1.0 e/A2/frame

Files:

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2. Oct10-2022

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

2252

Image size:

(5760, 4092)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(1.35, 1.35)

Details:

Please use CountRef_uplk_30-1_-0.0_Oct10_14.32.17_X-1Y-1-0.mrc as a gain reference without rotation or flipping.
The images were recorded with 3x3 image shifts but the beam tilt was not refined per direction.
The "XnnYmm" part of the file name represents the image shift direction.

Cs 2.7 mm
300 kV
1.0 e/A2/frame

Files:

Archives and downloads the selected files into an uncompressed zip file.

We recommend using rsync, aspera, globus, etc.

Download a list of selected files.

It is possible to download files by specifying the file list with rsync command, etc.

rsync -av --files-from=(path-to-filelist) rsync://empiar.pdbj.org/empiar/archive (path-to-destination)

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Citations

prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation.

He L, Bartesaghi A. (2026)

CryoSIP: unleashing protein high-resolution Cryo-EM via semantic-instance collaborative picking.

Deng Y, Wang S, Xiang M, Li Y, Zhuo L, Cao D, Fu X, Zou Q. (2026)

Chart builder: an interactive tool for user driven data visualization in the electron microscopy data bank.

Fonseca N, Duraisamy AK, Wang Z, Somasundharam S, Tayebinia M, de Oliveira LC, Ma M, Turner J, Patwardhan A, Kleywegt GJ, Hartley M, Morris KL. (2026)

CryoVirusDB: An Annotated Dataset for AI-Based Virus Particle Identification in Cryo-EM Micrographs.

Gyawali R, Dhakal A, Wang L, Cheng J. (2026)

Deep-learning methods for contrast enhancement and artifact reduction in cryo-electron tomography: a systematic analysis of the state of the art and proposed improvements.

Jones HN, Deshmukh A, Pande K. (2026)

CryoGS: High-Quality Cryo-EM Homogeneous Reconstruction by Gaussian Splatting

Zeng B, Liu S, Cheng S, Xu G, Fan H. (2026)

Point cloud deformation modeling for particle selection following cryo-EM 2D classification.

Wang X, Mo Z, Li F, Zhang F, Wan X. (2026)

ICECREAM: high-fidelity equivariant cryo-electron tomography.

Kishore V, Debarnot V, Righetto RD, Engel BD, Dokmanić I. (2026)

CryoSift: an accessible and automated CNN-driven tool for cryo-EM 2D class selection.

Schäfer JH, Calza A, Hom K, Damodar P, Peng R, Bogdanović N, Lander GC, Stagg SM, Cianfrocco MA. (2025)