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Related Concept Videos

Natural Selection and Adaptation01:15

Natural Selection and Adaptation

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Natural selection, a fundamental concept in evolutionary biology, is the mechanism by which evolution is driven, favoring organisms that are best adapted to their environments. This process enhances their chances of survival and reproduction. Adaptation, a key outcome of this process, involves genetic modifications that optimize an organism's functionality under specific environmental challenges, such as extreme cold or thinner air at high altitudes.
Beyond physical adaptations,...
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What is Natural Selection?01:32

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Natural selection is an evolutionary process in which individuals with survival-promoting traits reproduce at higher rates. These favorable traits become more common within a population or species. Naturally selected traits initially arise via random genetic mutations. In order for selection to occur, there must be variation within a population, the trait controlling the variation must be heritable, and there must be an evolutionary advantage for variation in the trait.
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Computed Tomography01:10

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Types of Selection01:46

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Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Related Experiment Video

Updated: Feb 2, 2026

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
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Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

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Wavelet de-noising method with adaptive threshold selection for photoacoustic tomography.

Meng Zhou, Haibo Xia, Hengrong Lan

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |November 17, 2018
    PubMed
    Summary

    This study introduces an adaptive wavelet threshold de-noising (aWTD) algorithm to improve signal quality in photoacoustic tomography. The method enhances signal-to-noise ratio and image contrast without compromising speed or fidelity.

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    Area of Science:

    • Biomedical Imaging
    • Acoustic Physics
    • Signal Processing

    Background:

    • Photoacoustic (PA) tomography images optical absorption in deep tissues using sound waves.
    • Low light-to-sound conversion efficiency in the PA effect results in poor signal-to-noise ratio (SNR), especially with low laser power and deep tissue imaging.
    • Traditional SNR improvement via data averaging limits imaging speed.

    Purpose of the Study:

    • To introduce a novel adaptive wavelet threshold de-noising (aWTD) algorithm for photoacoustic tomography.
    • To enhance the signal-to-noise ratio (SNR) of PA signals.
    • To improve PA image quality, including contrast, without sacrificing signal fidelity or imaging speed.

    Main Methods:

    • Development and application of a new adaptive wavelet threshold de-noising (aWTD) algorithm.
    • Implementation of the aWTD algorithm within the photoacoustic tomography framework.
    • Evaluation of SNR improvement, signal fidelity, imaging speed, and contrast enhancement.

    Main Results:

    • The aWTD algorithm significantly increases the SNR of PA signals.
    • PA image contrast is substantially improved.
    • The method maintains signal fidelity and imaging speed, unlike conventional averaging techniques.

    Conclusions:

    • The proposed aWTD algorithm effectively enhances PA signal SNR and image contrast.
    • This technique offers a pathway for developing real-time, low-cost PA tomography systems utilizing low-power lasers.
    • Adaptive wavelet threshold de-noising presents a promising solution for overcoming SNR limitations in PA imaging.