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相关概念视频

Classifying Matter by Composition03:35

Classifying Matter by Composition

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Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
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Overview
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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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Classifying Matter by State02:49

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Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
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The relative difference in electrical charge, or voltage, between the inside and the outside of a cell membrane, is called the membrane potential. It is generated by differences in permeability of the membrane to various ions and the concentrations of these ions across the membrane.
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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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PIPEMAT-RS: Development and Validation of a Standardized MATLAB Pipeline for Resting-State EEG Preprocessing
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利用预训练的视觉变压器来使用原始休息状态EEG对酒精使用障碍进行分类.

A Bingly, C D Richard, B Porjesz

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    概括
    此摘要是机器生成的。

    深度学习模型显示了使用电脑电图 (EEG) 数据诊断酒精使用障碍 (AUD) 的潜力. 虽然准确性不高,但这种方法为开发用于AUD的新神经生理诊断工具提供了基础.

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    科学领域:

    • 神经科学是一个神经科学.
    • 人工智能的人工智能
    • 精神病学是一个精神病学.

    背景情况:

    • 酒精使用障碍 (AUD) 是一种广泛的神经精神疾病,影响着数百万人,但缺乏客观的诊断生物标志物.
    • 目前对AUD的诊断方法有限,这凸显了对新型神经生理学工具的需求.

    研究的目的:

    • 研究深度学习的有效性,特别是EEGViT模型,在使用原始静止电脑图 (EEG) 数据对AUD患者进行分类.
    • 探索基于变压器的精神病分类模型的潜力,并开发基于EEG的诊断工具.

    主要方法:

    • 利用来自"酒遗传学合作研究" (COGA) 的大量数据集,包括来自2,710名参与者的5,402个EEG记录.
    • 应用了人口统计匹配和低样本,以管理混因素和阶级不平衡,保持原始的EEG特征.
    • 采用了混合深度学习架构EEGViT,用于端到端对AUD,CUD和OUD进行分类,并根据性别和年龄分层分析.

    主要成果:

    • AUD深度学习模型实现了大约56%的整体分类准确度,性别之间存在差异 (54%的男性,58%的女性).
    • 对于大麻使用障碍 (CUD) 和阿片类药物使用障碍 (OUD) 的模型显示了更高的准确性,约为63%.
    • 时间分析显示,在以后的EEG记录间隔中,模型性能有所改善,这表明了动态神经模式.

    结论:

    • 基于变压器的深度学习模型显示,使用原始EEG数据对AUD进行分类是有前途的,尽管目前的准确性不高.
    • 这些发现为开发针对AUD和其他物质使用障碍的客观,基于EEG的诊断工具提供了一个基本步骤.
    • 需要进一步的研究和模型改进,以提高精神病诊断的准确性和临床实用性.