Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Average Acceleration01:30

Average Acceleration

14.0K
The importance of understanding acceleration spans our day-to-day experiences, as well as the vast reaches of outer space and the tiny world of subatomic physics. In everyday conversation, to accelerate means to speed up. For instance, we are familiar with the acceleration of our car; the harder we apply our foot to the gas pedal, the faster we accelerate. The greater the acceleration, the greater the change in velocity over a given time. Acceleration is widely seen in experimental physics. In...
14.0K
Average Velocity01:12

Average Velocity

23.7K
To calculate the other physical quantities in kinematics, we must introduce the time variable. The time variable allows us not only to state the position of the object during its motion, but also how fast it is moving. The speed at which an object is moving is given by the rate at which the position changes with time. For each position xi, we assign a particular time ti. If the details of the motion at each instant are not important, the rate is usually expressed as the average velocity. This...
23.7K
Average Value of a Function01:17

Average Value of a Function

64
The average value of a function over a closed interval can be interpreted geometrically as the height of a rectangle whose area equals the net area under the curve across that interval. This net area accounts for both positive and negative contributions of the function, providing a single representative value that reflects the function’s overall behaviorA practical illustration of this idea arises when monitoring the temperature inside a greenhouse over a twenty-four-hour period. Although...
64
Average Power01:13

Average Power

1.1K
In practical electrical applications, the concept of time-varying instantaneous power is not frequently utilized. Instead, focus shifts to the more practical quantity known as average power. Average power is determined by integrating the instantaneous power over a specified time period and subsequently dividing it by that duration.
1.1K
Average and Instantaneous Velocity Vectors01:12

Average and Instantaneous Velocity Vectors

8.8K
To calculate other physical quantities in kinematics, the time variable must be introduced. The time variable not only allows us to state where an object is (its position) during its motion, but also how fast it’s moving. The speed at which an object is moving is given by the rate at which the position changes with time. For each position, a particular time is assigned. If the details of the motion at each instant are not important, the rate is usually expressed as the average velocity v.
8.8K
Antibody Structure01:10

Antibody Structure

65.6K
Overview
Antibodies, also known as immunoglobulins (Ig), are essential players of the adaptive immune system. These antigen-binding proteins are produced by B cells and make up 20 percent of the total blood plasma by weight. In mammals, antibodies fall into five different classes, which each elicits a different biological response upon antigen binding.
The Y-Shaped Structure of Antibodies Consists of Four Polypeptide Chains
Antibodies consist of four polypeptide chains: two identical heavy...
65.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Structure and potential role of T6SS effector PdpC in Francisella tularensis intracellular lifestyle.

Communications biology·2026
Same author

The tumour suppressor RBM5 activates the helicase DHX15 to regulate splicing.

Research square·2026
Same author

The tumour suppressor RBM5 activates the helicase DHX15 to regulate splicing.

bioRxiv : the preprint server for biology·2026
Same author

Discovery and cryoEM structure of FPM13, a periplasmic metalloprotein unique to Francisella.

PLoS pathogens·2026
Same author

Cryo-EM structures reveal a conserved architecture for raiA noncoding RNA.

Nucleic acids research·2026
Same author

Bridging structure and function: artificial intelligence-based modelling of kidney proteins.

Nature reviews. Nephrology·2026
Same journal

Infiltrating monocytes augment alternative complement activation and exacerbate inherited retinal degeneration in a mouse model.

Research square·2026
Same journal

Eco-evolutionary dynamics of defense systems in mobile genetic elements: Cui bono?

Research square·2026
Same journal

HIV Transmission Dynamics in Greater Mexico City are Shaped by Dense Spatial Mixing.

Research square·2026
Same journal

A UCP1-IRES-Cre Knock-In Mouse Enables Specific Brown Adipocyte Targeting Without CNS Off-Target Expression.

Research square·2026
Same journal

Precision RNAi for Fibrodysplasia Ossificans Progressiva: a combinatorial, unimolecular, allele selective approach.

Research square·2026
Same journal

Perceptions of end-of-life care quality among bereaved closest contacts of community-dwelling older Australians: a cross-sectional survey of the ASPREE cohort.

Research square·2026
See all related articles

Related Experiment Video

Updated: Feb 7, 2026

Subnanometer-resolution Structural Determination of Hemagglutinin from Cryo-electron Tomography of Influenza Viruses
08:19

Subnanometer-resolution Structural Determination of Hemagglutinin from Cryo-electron Tomography of Influenza Viruses

Published on: November 7, 2025

705

IsoNet2 determines cellular structures at submolecular resolution without averaging.

Z Hong Zhou1,2, Yun-Tao Liu1,2, Hongcheng Fan1,2,3

  • 1Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA.

Research Square
|February 6, 2026
PubMed
Summary
This summary is machine-generated.

IsoNet2 is a deep learning tool that reconstructs 3D densities from cryo-electron tomograms. This method achieves high resolution for cellular structures without averaging, enabling atomic-level interpretation.

More Related Videos

Determination of Molecular Structures of HIV Envelope Glycoproteins using Cryo-Electron Tomography and Automated Sub-tomogram Averaging
07:29

Determination of Molecular Structures of HIV Envelope Glycoproteins using Cryo-Electron Tomography and Automated Sub-tomogram Averaging

Published on: December 1, 2011

42.0K
High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging
13:49

High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging

Published on: January 11, 2011

35.1K

Related Experiment Videos

Last Updated: Feb 7, 2026

Subnanometer-resolution Structural Determination of Hemagglutinin from Cryo-electron Tomography of Influenza Viruses
08:19

Subnanometer-resolution Structural Determination of Hemagglutinin from Cryo-electron Tomography of Influenza Viruses

Published on: November 7, 2025

705
Determination of Molecular Structures of HIV Envelope Glycoproteins using Cryo-Electron Tomography and Automated Sub-tomogram Averaging
07:29

Determination of Molecular Structures of HIV Envelope Glycoproteins using Cryo-Electron Tomography and Automated Sub-tomogram Averaging

Published on: December 1, 2011

42.0K
High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging
13:49

High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging

Published on: January 11, 2011

35.1K

Area of Science:

  • Structural Biology
  • Cryo-Electron Tomography (Cryo-ET)
  • Deep Learning in Microscopy

Background:

  • Cryo-electron tomography (Cryo-ET) is crucial for visualizing cellular structures at near-atomic resolution.
  • Reconstruction of high-quality 3D densities from Cryo-ET data is often limited by noise, contrast transfer function (CTF) imperfections, and missing wedge artifacts.
  • Existing methods often require averaging or manual intervention, limiting their application to individual cellular components.

Purpose of the Study:

  • To introduce IsoNet2, an end-to-end self-supervised deep learning method for direct 3D density reconstruction from Cryo-ET data.
  • To achieve high-resolution structural information without the need for particle averaging.
  • To provide a user-friendly interface for fine-tuning the method for specific datasets.

Main Methods:

  • Developed a unified deep learning network that simultaneously performs denoising, CTF correction, and missing-wedge restoration.
  • Employed a self-supervised learning approach, minimizing the need for extensive labeled data.
  • Integrated a graphical user interface (GUI) for accessible, dataset-specific fine-tuning.

Main Results:

  • Achieved approximately 20 Å resolution reconstructions directly from tomograms, without averaging.
  • Successfully resolved complex biological structures, including HIV capsid protein organization, tRNA occupancy in ribosomes, and mitochondrial respiration complexes.
  • Demonstrated the capability for atomic-level interpretation of cellular environments.

Conclusions:

  • IsoNet2 significantly advances the capabilities of Cryo-ET by enabling direct, high-resolution 3D density reconstruction.
  • The method's self-supervised nature and user-friendly GUI democratize the analysis of complex cellular architectures.
  • IsoNet2 facilitates detailed structural and functional insights into biological macromolecules within their native cellular context.