MPicker: visualizing and picking membrane proteins for cryo-electron tomography.

Advancements in cryo-electron tomography (cryoET) allow the structure of macromolecules to be determined in situ, which is crucial for studying membrane protein structures and their interactions in the cellular environment. However, membranes are often highly curved and have a strong contrast in cryoET tomograms, which masks the signals from membrane proteins. These factors pose difficulties in observing and revealing the structures of membrane proteins in situ.

Deep Spatio-Temporal Network for Low-SNR Cryo-EM Movie Frame Enhancement.

Cryo-EM in single particle analysis is known to have low SNR and requires to utilize several frames of the same particle sample to restore one high-quality image for visualizing that particle. However, the low SNR of cryo-EM movie and motion caused by beam striking make the task very challenging. Video enhancement algorithms in computer vision shed new light on tackling such tasks by utilizing deep neural networks.

Deep Learning Model for Predicting the Pathological Complete Response to Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer.

Objective: This study aimed to develop an artificial intelligence model for predicting the pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) of locally advanced rectal cancer (LARC) using digital pathological images.

Background: nCRT followed by total mesorectal excision (TME) is a standard treatment strategy for patients with LARC. Predicting the PCR to nCRT of LARC remine difficulty.

Early triage of critically ill COVID-19 patients using deep learning.

The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission.

Particle segmentation algorithm for flexible single particle reconstruction.

As single particle cryo-electron microscopy has evolved to a new era of atomic resolution, sample heterogeneity still imposes a major limit to the resolution of many macromolecular complexes, especially those with continuous conformational flexibility. Here, we describe a particle segmentation algorithm towards solving structures of molecules composed of several parts that are relatively flexible with each other. In this algorithm, the different parts of a target molecule are segmented from raw images according to their alignment information obtained from a preliminary 3D reconstruction and are subjected to single particle processing in an iterative manner.

EMBuilder: A Template Matching-based Automatic Model-building Program for High-resolution Cryo-Electron Microscopy Maps.

The resolution of electron-potential maps in single-particle cryo-electron microscopy (cryoEM) is approaching atomic or near- atomic resolution. However, no program currently exists for de novo cryoEM model building at resolutions exceeding beyond 3.5 Å.

Near-Atomic Resolution Structure Determination of a Cypovirus Capsid and Polymerase Complex Using Cryo-EM at 200kV.

Single-particle cryo-electron microscopy (cryo-EM) allows the high-resolution structural determination of biological assemblies in a near-native environment. However, all high-resolution (better than 3.5Å) cryo-EM structures reported to date were obtained by using 300kV transmission electron microscopes (TEMs).