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

Trial and Error and Algorithm01:12

Trial and Error and Algorithm

403
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
403
Diversity of Archaea I01:30

Diversity of Archaea I

567
Archaea, a domain of single-celled microorganisms, are classified into five major phyla based on genetic and biochemical characteristics: Euryarchaeota, Crenarchaeota, Thaumarchaeota, Korarchaeota, and Nanoarchaeota. Among these, the phylum Euryarchaeota is notable for its remarkable diversity in morphology, metabolism, and ecological adaptations.Morphological and Metabolic DiversityMembers of Euryarchaeota exhibit a variety of cellular shapes, including rods and cocci. Their metabolic pathways...
567
Diversity of Archaea II01:24

Diversity of Archaea II

468
Archaea, one of the three domains of life, exhibit remarkable diversity and adaptability, thriving in both extreme and moderate environments. Historically, most identified archaea have been classified into two major phyla: Euryarchaeota and Crenarchaeota. However, recent molecular studies have expanded this classification to include three additional phyla: Thaumarchaeota, Nanoarchaeota, and Korarchaeota, each exhibiting unique characteristics and ecological roles.Thaumarchaeota: Mesophiles...
468
Diversity of Protists I01:15

Diversity of Protists I

908
Excavata is a diverse group of protists that includes both chemoorganotrophic and phototrophic species, with some thriving in anaerobic environments. Among the key groups within Excavata are diplomonads and parabasalids, which are flagellated protists that lack mitochondria and chloroplasts. These microorganisms typically inhabit anoxic environments, such as the intestines of animals, where they exist either symbiotically or as parasites, relying on fermentation for energy production. Some...
908
Diversity of Protists II01:27

Diversity of Protists II

848
Alveolates are a group of organisms recognized by the presence of alveoli, which are cytoplasmic sacs located beneath the cell membrane. While their function remains uncertain, alveoli may help regulate water balance by controlling how much water enters and leaves the cell. In dinoflagellates, these structures may serve as armor plates. There are three major types of alveolates: ciliates, which move using cilia; dinoflagellates, which use flagella for movement; and apicomplexans, which are...
848
Cell Diversity01:13

Cell Diversity

4.9K
The concept of a cell started with microscopic observations of dead cork tissue by Robert Hooke in 1665. Hooke coined the term "cell" based on the resemblance of the small subdivisions in the cork to the rooms that monks inhabited, called cells. About ten years later, Antonie van Leeuwenhoek became the first person to observe the living and moving cells under a microscope. In the century that followed, the theory that cells represented the basic unit of life developed.
Multicellular...
4.9K

You might also read

Related Articles

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

Sort by
Same author

Risk Factors for Cutaneous Immune-Related Adverse Events in the Japanese Population: A Retrospective Single-Center Cohort Study.

The Journal of dermatology·2026
Same author

Predictors of Immunosuppressive Treatment in Cutaneous Arteritis: Analysis of Clinical Characteristics in 42 Patients.

The Journal of dermatology·2026
Same author

Progressive Ocular Involvement in Toxic Epidermal Necrolysis Despite Early and Intensive Multidisciplinary Management: A Case Report.

The Journal of dermatology·2026
Same author

Peripheral blood UBA1 variant burden predicts poor outcomes in VEXAS syndrome: a nationwide prospective study.

Annals of the rheumatic diseases·2026
Same author

Detecting Distant Metastases in Prostate Cancer Using Whole-body MR Imaging Together with DWIBS (Diffusion-weighted Imaging with Background Body Signal Suppression).

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine·2026
Same author

Regional differences in clinical manifestations of antisynthetase syndrome: a comparison between Asian and European cohorts.

Arthritis research & therapy·2026

Related Experiment Video

Updated: Jan 24, 2026

Author Spotlight: Image-Based Methods to Study Membrane Trafficking Events in Stomatal Lineage Cells
11:31

Author Spotlight: Image-Based Methods to Study Membrane Trafficking Events in Stomatal Lineage Cells

Published on: May 12, 2023

1.6K

Genetic Diversity in Stomatal Density among Soybeans Elucidated Using High-throughput Technique Based on an Algorithm

Kazuma Sakoda1,2, Tomoya Watanabe1, Shun Sukemura1

  • 1Graduate School of Agriculture, Kyoto University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto, 606-8502, Japan.

Scientific Reports
|May 22, 2019
PubMed
Summary

A new deep learning method automates stomatal density (SD) evaluation in soybeans, significantly speeding up research. This high-throughput technique accurately measures SD variations across soybean accessions for improved crop breeding.

More Related Videos

Relating Stomatal Conductance to Leaf Functional Traits
11:09

Relating Stomatal Conductance to Leaf Functional Traits

Published on: October 12, 2015

19.7K
Author Spotlight: Soybean Hairy Root Transformation for the Analysis of Gene Function
07:34

Author Spotlight: Soybean Hairy Root Transformation for the Analysis of Gene Function

Published on: May 5, 2023

5.0K

Related Experiment Videos

Last Updated: Jan 24, 2026

Author Spotlight: Image-Based Methods to Study Membrane Trafficking Events in Stomatal Lineage Cells
11:31

Author Spotlight: Image-Based Methods to Study Membrane Trafficking Events in Stomatal Lineage Cells

Published on: May 12, 2023

1.6K
Relating Stomatal Conductance to Leaf Functional Traits
11:09

Relating Stomatal Conductance to Leaf Functional Traits

Published on: October 12, 2015

19.7K
Author Spotlight: Soybean Hairy Root Transformation for the Analysis of Gene Function
07:34

Author Spotlight: Soybean Hairy Root Transformation for the Analysis of Gene Function

Published on: May 5, 2023

5.0K

Area of Science:

  • Plant science
  • Agricultural technology
  • Computational biology

Background:

  • Stomatal density (SD) is crucial for improving soybean (Glycine max (L.) Merr) leaf photosynthesis.
  • Manual SD evaluation is labor-intensive and time-consuming, hindering large-scale studies.

Purpose of the Study:

  • To develop a high-throughput, automated technique for evaluating soybean stomatal density.
  • To identify variations in stomatal density among diverse soybean accessions.

Main Methods:

  • Utilized deep learning, specifically the Single Shot MultiBox Detector algorithm, for automated stomatal detection.
  • Acquired microscopic images from leaflet replicas of 90 soybean accessions.
  • Validated the automated method against manual measurements, achieving an R² of 0.90.

Main Results:

  • The developed detector achieved high-throughput and accurate stomatal recognition in microscopic images.
  • Significant variation in stomatal density was observed, ranging from 93±3 to 166±4 mm⁻² across 90 accessions.
  • The automated technique demonstrated high correlation (R²=0.90) with manual measurements.

Conclusions:

  • The deep learning-based detector offers a powerful tool for high-throughput stomatal density evaluation in large soybean populations.
  • This technique accelerates the identification of genetic variations related to stomatal density, aiding future crop breeding programs.
  • Facilitates faster selection of soybean varieties with desirable photosynthetic traits.