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

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

398
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
398
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.2K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.2K
Reducing Line Loss01:18

Reducing Line Loss

144
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
144
Structural Classification of Joints01:20

Structural Classification of Joints

3.2K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.2K
Functional Classification of Joints01:09

Functional Classification of Joints

3.8K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
3.8K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.3K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.3K

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

Integrated transcriptomic analysis reveals lymphatic <i>Icam1</i>-mediated immune dynamics after myocardial infarction.

Zoological research·2026
Same author

A mechanobiological hypothesis on bone cement-induced progression of bone metastases.

Frontiers in bioengineering and biotechnology·2026
Same author

A Dual-Focus Workflow for Simultaneously Engineering High Thermostability of Aldo-Keto Reductase for the Degradation of 3-Keto-Deoxynivalenol.

Journal of agricultural and food chemistry·2026
Same author

Structural basis of NMI-IFP35 domains and swapping phenomenon in IFP35-NID.

Journal of structural biology: X·2026
Same author

Integrated Single-Cell and Spatial Analysis Reveals a Metabolic-Immune Axis Driving Aortic Dissection.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Latent transition analysis of stigma and its association with treatment adherence in pulmonary tuberculosis patients during anti-tuberculosis therapy.

Frontiers in public health·2026

相关实验视频

Updated: Jun 7, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

643

通过基于加权距离罚款的拉索约束规范化的高斯图形模型来识别细胞类型.

Wei Zhang1, Yaxin Xu2, Xiaoying Zheng1

  • 1School of Mathematics and Physics, Wuhan Institute of Technology, Wuhan 430205, China.

Briefings in bioinformatics
|November 14, 2024
PubMed
概括

一个新的算法,WLGG,准确地从单细胞RNA测序 (scRNA-seq) 数据中识别细胞类型,而无需事先了解细胞数. 这种方法通过提高聚类准确性和下游分析可靠性来增强生物发现.

关键词:
细胞类型识别 细胞类型识别图形模型是一个图形模型.伪时间分析分析.在 scRNA-seq 数据中.按重量计的距离距离.

更多相关视频

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.2K
Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy
12:26

Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy

Published on: January 29, 2022

5.6K

相关实验视频

Last Updated: Jun 7, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

643
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.2K
Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy
12:26

Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy

Published on: January 29, 2022

5.6K

科学领域:

  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 单细胞RNA测序 (scRNA-seq) 对于理解细胞异质性至关重要.
  • 现有的细胞识别方法往往缺乏准确性或需要特定的设备.
  • 无监督的方法通常需要事先指定的细胞类型数量,从而限制了它们的使用.

研究的目的:

  • 从scRNA-seq数据开发一种新的,准确的和广泛适用的细胞类型识别算法.
  • 克服现有的无监督集群方法的局限性,特别是需要预先指定单元号的需求.
  • 为下游生物分析提供坚实的基础.

主要方法:

  • 提出了用于scRNA-seq数据分析的WLGG算法框架.
  • 结合加权距离罚款和高斯核来捕获非线性数据信息.
  • 在对线性数据特征的规范化高斯图形模型上应用了拉索约束.
  • 使用 Eigengap 策略进行自动细胞类型数量预测和光谱聚类进行标签分配.

主要成果:

  • 与14个测试数据集中的16种替代方法相比,WLGG表现出更高的集群精度.
  • 下游分析,包括标记基因识别和假名时间推断,证实了WLGG的可靠性.
  • 该算法为动态生物过程和监管机制提供了宝贵的见解.

结论:

  • WLGG算法在从scRNA-seq数据中准确和自动识别细胞类型方面取得了重大进展.
  • 它预测细胞数量和处理非线性数据的能力提高了它在生物研究中的适用性.
  • WLGG有助于更深入地了解细胞异质性和复杂的生物系统.