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

Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

262
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
262
Mean free path and Mean free time01:22

Mean free path and Mean free time

5.2K
Consider the gas molecules in a cylinder. They move in a random motion as they collide with each other and change speed and direction. The average of all the path lengths between collisions is known as the "mean free path."
5.2K
Osmoregulation in Fishes02:32

Osmoregulation in Fishes

53.2K
When cells are placed in a hypotonic (low-salt) fluid, they can swell and burst. Meanwhile, cells in a hypertonic solution—with a higher salt concentration—can shrivel and die. How do fish cells avoid these gruesome fates in hypotonic freshwater or hypertonic seawater environments?
53.2K
Path Between Thermodynamics States01:21

Path Between Thermodynamics States

4.1K
Consider the two thermodynamic processes involving an ideal gas that are represented by paths AC and ABC in Figure 1:
4.1K
Interference: Path Lengths01:10

Interference: Path Lengths

2.2K
Consider two sources of sound, that may or may not be in phase, emitting waves at a single frequency, and consider the frequencies to be the same.
Two special sources may be considered when they are in phase. This can be easily achieved by feeding the two sources from the same source. An example would be synchronizing the two speakers by feeding them with the same source, such as the sound waves produced by a tuning fork. This setup ensures that the two sources have the same frequency and are...
2.2K
Behavior of Gas Molecules: Molecular Diffusion, Mean Free Path, and Effusion03:48

Behavior of Gas Molecules: Molecular Diffusion, Mean Free Path, and Effusion

31.4K
Although gaseous molecules travel at tremendous speeds (hundreds of meters per second), they collide with other gaseous molecules and travel in many different directions before reaching the desired target. At room temperature, a gaseous molecule will experience billions of collisions per second. The mean free path is the average distance a molecule travels between collisions. The mean free path increases with decreasing pressure; in general, the mean free path for a gaseous molecule will be...
31.4K

You might also read

Related Articles

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

Sort by
Same author

A pilot study for speech assessment to detect the severity of Parkinson's disease: An ensemble approach.

Computers in biology and medicine·2024
Same author

Exploring the ventricular morphology of the heart of Brycon amazonicus (Agassiz, 1829) (Teleostei, Characiformes).

Morphologie : bulletin de l'Association des anatomistes·2024
Same author

Facial expressions to identify post-stroke: A pilot study.

Computer methods and programs in biomedicine·2024
Same author

Correction: A stomata classification and detection system in microscope images of maize cultivars.

PloS one·2024
Same author

Tabular data augmentation for video-based detection of hypomimia in Parkinson's disease.

Computer methods and programs in biomedicine·2023
Same author

Error-Correcting Mean-Teacher: Corrections instead of consistency-targets applied to semi-supervised medical image segmentation.

Computers in biology and medicine·2023
Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
See all related articles

Related Experiment Video

Updated: Feb 10, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

614

Automatic classification of fish germ cells through optimum-path forest.

João P Papa1, Mario E M Gutierrez, Rodrigo Y M Nakamura

  • 1Department of Computing, UNESP - Univ Estadual Paulista, Bauru, Brazil. papa@fc.unesp.br

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces machine learning for fish germ cell identification, crucial for monitoring spermatogenesis and improving fish reproduction. High recognition accuracies were achieved using advanced pattern recognition techniques.

More Related Videos

Simulating Impacts of Ice Storms on Forest Ecosystems
06:27

Simulating Impacts of Ice Storms on Forest Ecosystems

Published on: June 30, 2020

7.5K
Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils
09:16

Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils

Published on: November 25, 2016

17.4K

Related Experiment Videos

Last Updated: Feb 10, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

614
Simulating Impacts of Ice Storms on Forest Ecosystems
06:27

Simulating Impacts of Ice Storms on Forest Ecosystems

Published on: June 30, 2020

7.5K
Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils
09:16

Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils

Published on: November 25, 2016

17.4K

Area of Science:

  • Reproductive Biology
  • Computational Biology
  • Ichthyology

Background:

  • Spermatogenesis is vital for species reproduction.
  • Monitoring germ cells offers insights into reproductive processes.
  • Germ cell quantification aids in improving fish reproduction cycles.

Purpose of the Study:

  • To apply machine learning techniques for fish germ cell identification.
  • To establish a method for monitoring spermatogenesis in fishes.
  • To enhance the understanding and management of fish reproduction.

Main Methods:

  • Utilized state-of-the-art supervised pattern recognition techniques.
  • Developed machine learning models for germ cell identification.
  • Applied computational methods to analyze fish germ cells.

Main Results:

  • Achieved high recognition accuracies in identifying fish germ cells.
  • Demonstrated the efficacy of machine learning in this biological context.
  • Provided a novel computational tool for reproductive studies.

Conclusions:

  • Machine learning offers a powerful approach for fish germ cell quantification.
  • This method can significantly contribute to monitoring reproductive health in fish.
  • The study pioneers the use of AI in fish spermatogenesis research.