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相关概念视频

Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
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基于知识的在线多模式自动化表型化系统.

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    此摘要是机器生成的。

    新的KOMAP系统通过使用在线功能搜索引擎来简化电子健康记录 (EHR) 的表型,以创建准确的多式联络算法. 这种方法增强了大规模的表型和多中心协作,而不需要人类标记的数据.

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    科学领域:

    • 生物医学信息学 生物医学信息学
    • 临床数据科学 临床数据科学
    • 医疗保健中的人工智能

    背景情况:

    • 电子健康记录 (EHR) 系统提供了大量的临床数据,但由于记录不准确和特征相关性,在表型化方面面临挑战.
    • 目前基于EHR的表型化通常需要人类标记的培训套件,这限制了可扩展性和效率.

    研究的目的:

    • 引入知识驱动的在线多式联通自动化表型化 (KOMAP) 系统.
    • 通过简要信息和自动化特征选择,从EHR数据中实现高效准确的表型化.

    主要方法:

    • 开发了整合在线叙事和编码特征搜索引擎 (ONCE) 的KOMAP系统.
    • 利用来自EHR,在线文章和大型语言模型的综合知识来生成功能.
    • 训练有素的多式模式表型算法使用总结数据,绕过患者级数据和黄金标准标签的需求.

    主要成果:

    • 由ONCE选择的特征与最先进的AI模型 (GPT4,ChatGPT) 具有很高的一致性.
    • KOMAP生成了高效的表型算法,具有强大的性能,在四个医疗保健中心进行了验证.
    • 该系统成功地减少了特征集大小,同时保持了对大规模表型形成的高度相关性.

    结论:

    • 通过克服传统基于EHR的方法的局限性,KOMAP为大规模的多中心表型化提供了显著的进步.
    • KOMAP的完全在线性质促进了协作,并减少了对手动数据标签和患者级数据访问的依赖.