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相关概念视频

Environmental Applications of Microorganisms01:30

Environmental Applications of Microorganisms

29
Microorganisms play a pivotal role in maintaining ecosystem balance by recycling essential elements such as carbon, nitrogen, and phosphorus, as well as supporting processes like bioremediation, wastewater treatment, and biofuel production.Microbes in Elemental CyclesIn the carbon cycle, microorganisms decompose organic matter, releasing carbon dioxide via aerobic respiration. This carbon dioxide is subsequently used by photosynthetic organisms to synthesize organic compounds, closing the...
29

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相关实验视频

Updated: Jul 13, 2025

Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans
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在生态毒理学中用于机器学习的基准数据集.

Christoph Schür1, Lilian Gasser2, Fernando Perez-Cruz2,3

  • 1Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland. christoph.schuer@eawag.ch.

Scientific data
|October 18, 2023
PubMed
概括

这项研究介绍了ADORE,这是一个使用机器学习预测水生有毒性的综合数据集. 它旨在标准化生态毒理学研究,并鼓励新的预测模型.

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A Toxicological and Ecotoxicological Assay Based on Mussel (Mytilus galloprovincialis) Hemocytes Motility
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Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
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Last Updated: Jul 13, 2025

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Published on: April 28, 2023

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A Toxicological and Ecotoxicological Assay Based on Mussel (Mytilus galloprovincialis) Hemocytes Motility
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科学领域:

  • 生态毒理学 生态毒理学
  • 环境科学 环境科学
  • 计算生物学 计算生物学

背景情况:

  • 机器学习 (ML) 对预测生态毒理学结果具有前景,但由于数据策划挑战,其利用率不足.
  • 缺乏标准化的数据集和方法阻碍了跨研究模型性能比较.
  • 经常需要在ML和生态毒理学方面的专业知识,从而造成进入障碍.

研究的目的:

  • 为急性水生毒性研究提供一个广泛,详细描述的数据集 (ADORE).
  • 促进生态毒理学的标准化模型开发和绩效评估.
  • 挑战研究人员开发新的ML模型来预测生态毒性.

主要方法:

  • 策划了关于急性水生有毒性的广泛数据集,包括实验数据.
  • 综合的遗传学,物种特异性,化学和分子数据.
  • 开发了标准化的数据集和训练测试分割,用于可重现的研究.

主要成果:

  • 阿多尔数据集包括对鱼类,甲类和藻类的急性水生毒性数据.
  • 包括详细的物种和化学特性,以及分子表示.
  • 为定义的研究挑战提供特定的数据子集和分割.

结论:

  • 阿多尔解决了在生态毒理学ML研究中需要标准化,高质量的数据的需求.
  • 方便可重复的模型开发和基准测试.
  • 促进预测化学品环境风险的进步.