Utz H. Ermel

The paper describes a Kaggle challenge for particle picking in cryo-electron tomography, resulting in improved algorithms and highlighting the need for better annotation quality measures.

This study identifies two mechanisms by which dipeptide esters disrupt lysosomal homeostasis: amyloid fibril formation and proximity labeling via Cathepsin C.

AreTomo3 and DenoisET form an automated GPU pipeline that reconstructs cryo-electron tomograms with tile-based contrast transfer function correction, screens their quality, and denoises them during acquisition.

The paper uses advanced imaging techniques to elucidate the molecular architecture of the kidney slit diaphragm, revealing a fishnet-like structure and interactions between key proteins.

The paper determines the cryo-EM structure of a full-length human β1-adrenergic receptor in complex with Gs, revealing structural insights into ICL3's role in G protein activation.

The paper introduces open-source tools for a machine learning competition focused on particle picking in cryo-electron tomography data.

The paper introduces a phantom sample and annotated 3D volumes for a machine learning challenge to improve cryoET volume annotation.

This study localizes albumin in kidney tissue using correlative super-resolution light and electron microscopy to map its distribution and quantify concentration gradients across the filtration barrier.

The paper introduces an updated plugin, ArtiaX, for cryo-electron tomography analysis, offering tools for particle selection, camera path definition, and image processing.