Search research articles

相关概念视频

Synthetic Biology

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

Agentic AI for Structural Elucidation and Discovery of Drug Metabolites from Mass Spectrometry Data.

bioRxiv : the preprint server for biology·2026

Same author

Tyrosine-sulfated peptide-induced flavonol biosynthesis controls elongation and differentiation in Arabidopsis primary root.

The Plant cell·2026

Same author

Mapping the soil microbiome functions shaping wetland methane emissions.

mSystems·2026

Same author

Bioengineered algal lipids enriched in structured medium- and long-chain triacylglycerols, linoleate, and <i>sn</i>-2 palmitate for human milk fat substitutes.

bioRxiv : the preprint server for biology·2026

Same author

Metabolic blueprints of monocultures enable prediction and design of synthetic microbial consortia.

bioRxiv : the preprint server for biology·2026

Same author

Author Correction: The 'photosynthetic C1 pathway' links carbon assimilation and growth in California poplar.

Communications biology·2026

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

Search research articles

相关实验视频

Updated: Sep 10, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

使用VAE,GAN和扩散模型架构进行合成科学图像生成

Zineb Sordo¹, Eric Chagnon¹, Zixi Hu¹

¹Applied Math and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.

Journal of imaging

|August 27, 2025

概括

像GAN这样的生成人工智能模型擅长创建现实的科学图像, 但验证它们的准确性需要专家的意见. 需要进一步的研究来解决更广泛的科学应用的解释性和计算成本方面的挑战.

关键词:

产生敌对网络扩散情况生成性人工智能图像生成合成数据

更多相关视频

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

Published on: December 6, 2024

Generation of Shear Adhesion Map Using SynVivo Synthetic Microvascular Networks

Generation of Shear Adhesion Map Using SynVivo Synthetic Microvascular Networks

Published on: May 25, 2014

相关实验视频

Last Updated: Sep 10, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

Published on: December 6, 2024

Generation of Shear Adhesion Map Using SynVivo Synthetic Microvascular Networks

Generation of Shear Adhesion Map Using SynVivo Synthetic Microvascular Networks

Published on: May 25, 2014

科学领域:

科学成像
人工智能
数据综合

背景情况:

生成性人工智能 (genAI) 为合成复杂图像数据提供了强大的功能.
科学成像应用可以从新型图像生成技术中受益.

研究的目的:

对领先的生成AI架构进行比较分析,用于科学图像合成.
评估变量自编码器 (VAE),生成对抗网络 (GAN) 和扩散模型.

主要方法:

对特定领域的数据集进行评估 (微CT扫描,植物根).
整合定量指标 (SSIM,LPIPS,FID,CLIPScore) 和定性专家评估.
分析基础原则,建筑的进步和实际的权衡.

主要成果:

生成对抗网络 (GAN),特别是StyleGAN,表现出高度的感知质量和结构连贯性.
扩散模型显示高现实性和语义对齐,但在平衡视觉准确性和科学准确性方面面临挑战.
确定了标准定量指标在评估科学相关性的局限性,强调了对专家验证的需要.

结论:

生成型人工智能具有科学数据增强,模拟和假设生成的巨大潜力.
主要挑战包括模型可解释性,计算成本和强大的验证协议.
领域专家验证对于确保生成图像的科学相关性至关重要.