AIMC Topic: Electrons

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms.

Journal of visualized experiments : JoVE
Cryo-electron tomography (cryo-ET) allows researchers to image cells in their native, hydrated state at the highest resolution currently possible. The technique has several limitations, however, that make analyzing the data it generates time-intensiv...

Concluding remarks: Challenges and future developments in biological electron cryo-microscopy.

Faraday discussions
During the past 10 years, biological electron cryo-microscopy (cryoEM) has undergone a process of rapid transformation. Many things we could only dream about a decade ago have now become almost routine. Nevertheless, a number of challenges remain, to...

Machine Learning Diffusion Monte Carlo Energies.

Journal of chemical theory and computation
We present two machine learning methodologies that are capable of predicting diffusion Monte Carlo (DMC) energies with small data sets (≈60 DMC calculations in total). The first uses voxel deep neural networks (VDNNs) to predict DMC energy densities ...

New venues in electron density analysis.

Physical chemistry chemical physics : PCCP
We provide a comprehensive overview of the chemical information from electron density: not only how to extract information, but also how to obtain and how to assess the quality of the electron density itself. After introducing several indexes derived...

Direct mapping from PET coincidence data to proton-dose and positron activity using a deep learning approach.

Physics in medicine and biology
. Obtaining the intrinsic dose distributions in particle therapy is a challenging problem that needs to be addressed by imaging algorithms to take advantage of secondary particle detectors. In this work, we investigate the utility of deep learning me...

Volumetric macromolecule identification in cryo-electron tomograms using capsule networks.

BMC bioinformatics
BACKGROUND: Despite recent advances in cellular cryo-electron tomography (CET), developing automated tools for macromolecule identification in submolecular resolution remains challenging due to the lack of annotated data and high structural complexit...

Rapidly predicting Kohn-Sham total energy using data-centric AI.

Scientific reports
Predicting material properties by solving the Kohn-Sham (KS) equation, which is the basis of modern computational approaches to electronic structures, has provided significant improvements in materials sciences. Despite its contributions, both DFT an...

MemBrain: A deep learning-aided pipeline for detection of membrane proteins in Cryo-electron tomograms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cryo-electron tomography (cryo-ET) is an imaging technique that enables 3D visualization of the native cellular environment at sub-nanometer resolution, providing unpreceded insights into the molecular organization of cells....

Deep learning-based in vivo dose verification from proton-induced secondary-electron-bremsstrahlung images with various count level.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Proton-induced secondary-electron-bremsstrahlung (SEB) imaging is a promising method for estimating the ranges of particle beam. However, SEB images do not directly represent dose distributions of particle beams. In addition, the ranges esti...

Observing Noncovalent Interactions in Experimental Electron Density for Macromolecular Systems: A Novel Perspective for Protein-Ligand Interaction Research.

Journal of chemical information and modeling
We report for the first time the use of experimental electron density (ED) in the Protein Data Bank for modeling of noncovalent interactions (NCIs) for protein-ligand complexes. Our methodology is based on reduced electron density gradient (RDG) theo...