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

Machines: Problem Solving I01:22

Machines: Problem Solving I

841
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
841
Machines: Problem Solving II01:30

Machines: Problem Solving II

791
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
791
Survival Tree01:19

Survival Tree

499
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
499

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

Order-disorder transition in incompressible polar active fluids with an easy axis.

Physical review. E·2026
Same author

Only the ambidextrous can flock: Two-dimensional chiral Malthusian flocks, time cholesterics, and the Kardar-Parisi-Zhang equation.

Physical review. E·2026
Same author

Stochastic Pairwise Forces Enhance Tracer Diffusion in Nonmotile Active Matter.

Physical review letters·2026
Same author

Optimizing scheduling in dual-pulse nucleoside labeling experiments for cell-cycle analysis.

Biophysical journal·2026
Same author

Nested Stochastic Resetting: Nonequilibrium Steady States and Exact Correlations.

Physical review letters·2025
Same author

Boosting macroscopic diffusion with local resetting.

Physical review. E·2025
Same journal

Cortical Brain Entropy Architecture Reveals Multidimensional Structure of Schizophrenia.

Biophysical reports·2026
Same journal

The oligomeric state of chitooligosaccharide deacetylase from the marine bacterium Vibrio campbellii.

Biophysical reports·2026
Same journal

Quantifying Species-Specific Binding Affinities of Transthyretin Aggregation Inhibitors.

Biophysical reports·2026
Same journal

Drosophila jump muscle myofibrils: A new tool for investigating activation and relaxation.

Biophysical reports·2026
Same journal

Optical tweezers combined with FRET tension sensor reveal force-dependent vinculin dynamics.

Biophysical reports·2026
Same journal

Role of E. coli acid resistance systems in proton motive force formation during fermentation.

Biophysical reports·2026
查看所有相关文章

相关实验视频

Updated: May 2, 2026

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

14.4K

机器学习在交汇组织中的拓缺陷.

Andrew Killeen1, Thibault Bertrand2, Chiu Fan Lee1

  • 1Department of Bioengineering, Imperial College London, South Kensington Campus, London, United Kingdom.

Biophysical reports
|February 5, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的卷积神经网络,用于检测和分类生物系统中的活体阴性缺陷. 机器学习模型准确地识别了细胞层中的缺陷,改善了数据解释和降低成本.

更多相关视频

Analysis of Congenital Heart Defects in Mouse Embryos Using Qualitative and Quantitative Histological Methods
08:28

Analysis of Congenital Heart Defects in Mouse Embryos Using Qualitative and Quantitative Histological Methods

Published on: March 10, 2020

6.8K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.2K

相关实验视频

Last Updated: May 2, 2026

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

14.4K
Analysis of Congenital Heart Defects in Mouse Embryos Using Qualitative and Quantitative Histological Methods
08:28

Analysis of Congenital Heart Defects in Mouse Embryos Using Qualitative and Quantitative Histological Methods

Published on: March 10, 2020

6.8K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.2K

科学领域:

  • 物理和生物学 物理和生物学
  • 生物系统表征中的新兴范式

背景情况:

  • 活体遗传学对于理解生物系统至关重要.
  • 活体体质中的缺陷在生物过程中起着关键作用.
  • 现有的缺陷检测方法不适合非杆状细胞,如上皮层.

研究的目的:

  • 开发一个卷积神经网络 (CNN),用于检测和分类交汇细胞层中的脑性缺陷.
  • 创建一种适用于细胞层实验图像的方法,特别是那些具有非杆状细胞的细胞.

主要方法:

  • 卷积神经网络 (CNN) 的开发.
  • 在细胞层的实验图像上训练CNN.
  • 在使用非棒形细胞的实验数据上证明了缺陷检测.

主要成果:

  • 在CNN成功地检测和分类在交汇的细胞层内性缺陷.
  • 开发的方法适用于非杆状细胞.
  • 机器学习模型的性能优于当前的缺陷检测技术.

结论:

  • 新的CNN方法显著提高了对阴性缺陷的实验数据解释的准确性.
  • 这种方法减少了准确捕获缺陷属性的数据.
  • 这一发现推动了对生物系统中阴性缺陷的研究,为成本和精度带来了好处.