Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
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 JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

12.8K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
12.8K
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.6K
2.6K
Leaky Scanning02:28

Leaky Scanning

5.1K
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
5.1K
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

7.0K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
7.0K

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

Predicting the Thermodynamic Limits of Metal-Organic Framework Metastability.

Journal of the American Chemical Society·2026
Same author

Ion correlations explain kinetic selectivity in diffusion-limited solid-state synthesis reactions.

Nature materials·2026
Same author

Identification of Solid-Electrolyte Interphase Species by Joint Characterization of Li-Ion Battery Chemistry by Mass Spectrometry and Electrochemical Reaction Networks.

Journal of the American Chemical Society·2026
Same author

Blood-based RNA-Seq of 5412 individuals with rare disease identifies new candidate diagnoses in the National Genomic Research Library.

medRxiv : the preprint server for health sciences·2026
Same author

Generative Models for Crystalline Materials.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Li<sup>+</sup>/H<sup>+</sup> Exchange in Solid-State Oxide Li-Ion Conductors.

ACS energy letters·2026
Same journal

The BRCA1-A complex restricts replication fork reversal-dependent DNA repair in ATM deficient cells.

Nature communications·2026
Same journal

Signaling downstream of tumor-stroma interaction regulates mucinous colorectal adenocarcinoma apicobasal polarity.

Nature communications·2026
Same journal

Click-polymerized polyenamine membranes for efficient lithium extraction.

Nature communications·2026
Same journal

Joint trajectories of brain atrophy, white matter hyperintensities and cognition quantify brain maintenance.

Nature communications·2026
Same journal

Proton shuttling at electrochemical interfaces under alkaline hydrogen evolution.

Nature communications·2026
Same journal

metilene<sup>3</sup>: identifying DMRs across multiple conditions with auto-classification.

Nature communications·2026
查看所有相关文章

相关实验视频

Updated: Jul 3, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

558

用大型语言模型从科学文本中提取结构化的信息.

John Dagdelen1,2, Alexander Dunn1,2, Sanghoon Lee1,2

  • 1Lawrence Berkeley National Laboratory, Berkeley, CA, USA.

Nature communications
|February 15, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了一种简单的机器学习方法,使用大型语言模型从文本中提取结构化的科学知识. 这种方法有效地为材料化学研究创建了大型数据库.

更多相关视频

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.6K
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

15.9K

相关实验视频

Last Updated: Jul 3, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

558
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.6K
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

15.9K

科学领域:

  • 材料化学 材料化学
  • 计算科学 计算科学
  • 自然语言处理自然语言处理.

背景情况:

  • 从科学文献中提取结构化知识是机器学习的一个重大挑战.
  • 现有的方法可能缺乏灵活性来处理不同类型的科学数据.

研究的目的:

  • 为共同命名实体识别和关系提取提供一种简单,易于使用的方法.
  • 为了证明微调预训练的大型语言模型 (LLM) 的有效性,用于科学知识提取.
  • 从科学研究论文创建大型,结构化的数据库.

主要方法:

  • 微调预训练的LLM (GPT-3,Llama-2) 用于命名实体识别和关系提取.
  • 将该方法应用于三种材料化学任务:剂-宿主链接,金属有机框架目录以及成分/相位/形态/应用提取.
  • 处理文本从单个句子到整个段落.

主要成果:

  • 成功提取复杂科学知识的结构化记录.
  • 在输出格式中表现出灵活性,包括简单的英语句子和JSON对象.
  • 展示了创建大型,专门的科学知识数据库的潜力.

结论:

  • 拟议的方法为自动化科学知识提取提供了一个高度灵活和可访问的途径.
  • 微调的LLMs是从非结构化的科学文本构建结构化数据库的可行策略.
  • 这种方法可以显著加快科学数据的研究和开发的策划.