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

Entropy within the Cell01:22

Entropy within the Cell

8.6K
A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
8.6K
The Entropy as a State Function01:14

The Entropy as a State Function

134
Consider an arbitrary process that moves between two specific states (A and B) in a cyclic manner. This process is reversible and broken down into smaller parts that each follow a Carnot cycle. A Carnot cycle has two isothermal (constant temperature) processes. During these processes, the ratio of the amount of heat transferred to their respective temperature remains constant. The other two processes in the Carnot cycle are also reversible but adiabatic, which means they occur without any heat...
134
State Space Representation01:27

State Space Representation

785
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
785

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

Frailty phenotype transitions and functional improvements during a supervised exercise trial in older people with HIV: results from the HEALTH Trial.

Age and ageing·2026
Same author

An Exploration of Heart Rate Response and Blood Tetrahydrocannabinol (THC) Levels to Commercially Available Cannabis Edibles by Dose.

Research square·2026
Same author

Making highways and workplaces safer: An interpretable machine learning approach to predicting recent cannabis use and impairment.

Journal of safety research·2026
Same author

Advancing translational science through biostatistics, epidemiology, and research design consultations: A multi-perspective evaluation of the Georgia CTSA BERD program.

Journal of clinical and translational science·2026
Same author

Glycemic response trajectories on metformin monotherapy in real-world diabetes care.

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

Sphingosine kinase-2 inhibition promotes immunogenic differentiation of myeloid-derived suppressor cells through an Acetyl-CoA carboxylase-phosphatidylcholine axis.

Nature communications·2026

相关实验视频

Updated: May 4, 2026

An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images
10:00

An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images

Published on: August 31, 2012

14.6K

FunSpace:一种功能和空间分析方法,用于使用度测量的细胞成像数据.

Thao Vu1, Souvik Seal1, Tusharkanti Ghosh1

  • 1Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America.

PLoS computational biology
|September 27, 2023
PubMed
概括

这项研究引入了一种新的方法来分析瘤微环境 (TME) 中的空间异质性. 该方法揭示了TME细胞组成和相互作用对患者生存结果的重大影响.

更多相关视频

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
09:56

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

6.6K
Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells
10:21

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

Published on: September 16, 2020

6.2K

相关实验视频

Last Updated: May 4, 2026

An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images
10:00

An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images

Published on: August 31, 2012

14.6K
Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
09:56

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

6.6K
Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells
10:21

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

Published on: September 16, 2020

6.2K

科学领域:

  • 计算生物学 计算生物学
  • 癌症研究 癌症研究
  • 空间统计的空间统计.

背景情况:

  • 瘤微环境 (TME) 的空间异质性对瘤进展至关重要.
  • 现有的指标无法完全捕捉同时发生的细胞多样性和空间配置.
  • 需要一个全面的方法来量化复杂的TME空间模式.

研究的目的:

  • 开发一种用于量化TME空间异质性的新方法.
  • 评估TME空间异质性对患者生存结果的影响.
  • 整合细胞多样性和空间配置,以改善预后模型.

主要方法:

  • 在多个距离范围内利用空间度.
  • 应用功能主要组件分析 (FPCA) 来得出预测得分.
  • 采用Cox回归模型生存结果,对混因素进行调整.

主要成果:

  • 确定了TME空间异质性对非小细胞肺癌整体存活率的显著影响 (p=0.027).
  • 瘤与免疫细胞相互作用对三阴性乳腺癌的生存有显著影响 (p=0.046).
  • 模拟研究证实了占空间异质性的高预测能力.

结论:

  • 拟议的方法有效量化了TME空间异质性.
  • 细胞组成和相互作用的空间模式是显著的预后因素.
  • 这种方法为了解瘤生物学和改善癌症患者的治疗结果提供了一个强大的工具.