Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Subcellular Fractionation

Subcellular Fractionation

The homogenate obtained after cell lysis contains various membrane-bound organelles that can be further separated into pure fractions by subcellular fractionation. These isolates are used to study specific cellular components, analyze localized protein activity, and are even employed in diagnostics. Fractionation is typically achieved using centrifugation methods, the most common being density-gradient and differential centrifugation.
Differential Centrifugation
Differential centrifugation is...

You might also read

Related Articles

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

Sort by

Same author

Dual targeting of lipid metabolic reprogramming and immunosuppressive sentinel lymph nodes potentiates anti-metastatic therapy for triple negative breast cancer.

Bioactive materials·2025

Same author

Dynamic multistage hydrogel with auto-adjusting networks for sequential anti-infection, anti-inflammation, and angiogenesis in infected wound regeneration.

Journal of materials chemistry. B·2025

Same author

Harnessing nanoparticles for Alzheimer's disease: Innovations in drug delivery and pathology-specific treatments.

Journal of Alzheimer's disease : JAD·2025

Same author

A triple-mode strategy combining low-temperature photothermal, photodynamic, and chemodynamic therapies for treating infectious skin wounds.

Biomaterials science·2024

Same author

A Precise Segmentation Algorithm of Pumpkin Seedling Point Cloud Stem Based on CPHNet.

Plants (Basel, Switzerland)·2024

Same author

AISOA-SSformer: An Effective Image Segmentation Method for Rice Leaf Disease Based on the Transformer Architecture.

Plant phenomics (Washington, D.C.)·2024

Same journal

A coordinated, multi-subunit chitin deacetylase complex for robust evasion of wheat immunity by Fusarium graminearum.

The Plant journal : for cell and molecular biology·2026

Same journal

Maintenance of H3K9me2 heterochromatin in the pollen vegetative nucleus requires ARID1 nuclear body formation.

The Plant journal : for cell and molecular biology·2026

Same journal

Metabolic design considerations for recycling of respiratory CO<sub>2</sub> in leaves.

The Plant journal : for cell and molecular biology·2026

Same journal

The continental-island relict genus Euptelea as a window into genomic divergence, adaptation, and climate vulnerability of island endemics.

The Plant journal : for cell and molecular biology·2026

Same journal

Discovery of pseudobaptigenin synthase in fungal-infected Trifolium pratense roots.

The Plant journal : for cell and molecular biology·2026

Same journal

LMI1 and TCP4 homologs form a functional module to regulate lateral petal asymmetric bending in Delphinium anthriscifolium.

The Plant journal : for cell and molecular biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 1, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

A high-precision segmentation method for rubber tree stone cells.

Meixi Pan¹, Guoxiong Zhou¹, Yuanyuan Zhang²

¹Central South University of Forestry and Technology, Changsha, 410004, Hunan, China.

The Plant Journal : for Cell and Molecular Biology

|August 4, 2025

Summary

This summary is machine-generated.

A new image recognition network, CGWO-LWNet, accurately identifies and quantifies stone cells in rubber tree bark. This breakthrough aids research into bark cracking, hardness, and latex yield.

Keywords:

CGWO optimization algorithm Rubber tree stone cells low‐rank KAN module wave‐SC module

More Related Videos

A Technical Perspective in Modern Tree-ring Research - How to Overcome Dendroecological and Wood Anatomical Challenges

A Technical Perspective in Modern Tree-ring Research - How to Overcome Dendroecological and Wood Anatomical Challenges

Published on: March 5, 2015

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

Related Experiment Videos

Last Updated: Jul 1, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

A Technical Perspective in Modern Tree-ring Research - How to Overcome Dendroecological and Wood Anatomical Challenges

A Technical Perspective in Modern Tree-ring Research - How to Overcome Dendroecological and Wood Anatomical Challenges

Published on: March 5, 2015

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

Area of Science:

Plant biology
Biotechnology
Computer vision

Background:

Stone cells in rubber tree bark are crucial for traits like hardness and latex yield.
Current methods for stone cell analysis are slow and inaccurate, hindering research.
Understanding stone cell distribution is vital for rubber tree cultivation and genetic studies.

Purpose of the Study:

To develop an automated, accurate method for segmenting and quantifying stone cells in rubber tree bark.
To improve the analysis of stone cell distribution and its impact on rubber tree traits.
To overcome limitations of traditional segmentation networks in handling complex bark image features.

Main Methods:

Proposed CGWO-LWNet, an automatic segmentation network using image recognition.
Introduced a low-rank KAN module for enhanced feature fusion and edge segmentation.
Designed a wavelet attention mechanism (Wave-SC) for capturing stone cell distribution patterns.
Utilized a novel gray wolf constrained optimization algorithm (CGWO) for stable network training.
Created a dataset of 1084 rubber tree stone cell images for training and validation.

Main Results:

CGWO-LWNet achieved 69.1% MIoU, 81.7% DSC, and 80.4% recall on the test dataset.
Demonstrated superior accuracy with 97.8% for rubber tree bark stone cell segmentation compared to other algorithms.
The network effectively handles complex edges and regional distribution patterns of stone cells.

Conclusions:

CGWO-LWNet provides a practical and robust tool for high-precision stone cell segmentation in rubber tree bark.
Enables large-scale, accurate trait analysis related to latex yield, bark integrity, and stress resilience.
Facilitates further genetic studies on stone cell development and its influence on rubber tree productivity.