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Related Concept Videos

Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Color Vision01:24

Color Vision

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Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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What is a Sensory System?01:31

What is a Sensory System?

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Sensory systems detect stimuli—such as light and sound waves—and transduce them into neural signals that can be interpreted by the nervous system. In addition to external stimuli detected by the senses, some sensory systems detect internal stimuli—such as the proprioceptors in muscles and tendons that send feedback about limb position.
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Predator-Prey Interactions02:39

Predator-Prey Interactions

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Predators consume prey for energy. Predators that acquire prey and prey that avoid predation both increase their chances of survival and reproduction (i.e., fitness). Routine predator-prey interactions elicit mutual adaptations that improve predator offenses, such as claws, teeth, and speed, as well as prey defenses, including crypsis, aposematism, and mimicry. Thus, predator-prey interactions resemble an evolutionary arms race.
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Related Experiment Video

Updated: Jan 31, 2026

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision
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Toxicogenomics: A 2020 Vision.

Zhichao Liu1, Ruili Huang2, Ruth Roberts3

  • 1National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas 72079, USA.

Trends in Pharmacological Sciences
|December 31, 2018
PubMed
Summary
This summary is machine-generated.

Toxicogenomics (TGx) offers promising animal-free approaches for regulatory toxicology. Advanced genomics and machine learning in TGx systems enhance risk assessment and predictive modeling for safer chemical evaluations.

Keywords:
adverse outcome pathwaysdeep learningregulatory sciencesreproducibilitytoxicogenomics

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Area of Science:

  • Toxicology
  • Genomics
  • Computational Biology

Background:

  • Toxicogenomics (TGx) has advanced toxicology and shows potential for animal-free regulatory science.
  • In vitro TGx systems are crucial for modern risk assessment strategies.

Purpose of the Study:

  • To discuss the impact of in vitro TGx systems on regulatory risk assessment.
  • To highlight the role of advanced genomics and machine learning in TGx.
  • To explore TGx's potential in facilitating Adverse Outcome Pathways (AOP) development and read-across strategies.

Main Methods:

  • Review of in vitro toxicogenomics systems.
  • Emphasis on the advancement of genomics technologies.
  • Application of machine learning, particularly deep learning, for predictive modeling.

Main Results:

  • Genomics technologies provide novel features for enhanced risk assessment.
  • Reproducibility is critical for regulatory acceptance of TGx.
  • Machine learning aids in developing robust TGx-based predictive models.

Conclusions:

  • TGx approaches, including AOP development and read-across, can significantly enhance regulatory applications.
  • Current efforts focus on developing TGx for risk assessment, with ongoing challenges to address.