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相关概念视频

Per-Unit Sequence Models01:26

Per-Unit Sequence Models

440
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
440
Self-Evaluation Maintenance Model01:29

Self-Evaluation Maintenance Model

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The Self-Evaluation Maintenance (SEM) model offers a psychological framework to understand how individuals’ self-esteem is influenced by the achievements of others, particularly those with whom they share close personal bonds. The SEM model operates when personal rather than social identity guides individuals. Central to this model is the notion that individuals have an inherent desire to preserve a favorable self-image, which is continuously shaped by interpersonal comparisons and...
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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Compartment Models: Single-Compartment Model01:14

Compartment Models: Single-Compartment Model

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The single-compartment model serves as a simplified representation of the human body. This model assumes that the body functions as a single, well-mixed open compartment. When a drug is administered intravenously, it enters the body and quickly distributes uniformly. The drug then undergoes biotransformation and elimination, ultimately leaving the body. The volume of this compartment is referred to as the apparent volume of distribution into which the drug can uniformly distribute. In this...
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Hybridoma Technology01:31

Hybridoma Technology

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Hybridoma technology is used for the large-scale production of monoclonal antibodies. Monoclonal antibodies bind to only a single antigenic determinant or epitope. Such antibodies are used in research, diagnostics, and disease therapy. The hybridoma technology established in 1975 by Georges Köhler and Cesar Milstein was awarded the Nobel Prize in Medicine in 1984 for revolutionizing research and therapy.
Hybridoma Selection
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相关实验视频

Updated: Jan 24, 2026

ATAC-Seq Optimization for Cancer Epigenetics Research
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ATAC-Seq Optimization for Cancer Epigenetics Research

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评估使用序列到功能建模的单细胞ATAC-seq图谱技术.

Hannah Dickmänken1,2,3, Marta Wojno4, Lukas Mahieu1,2,3,5

  • 1Laboratory of Computational Biology, VIB Center for AI & Computational Biology, Leuven, Belgium.

Nature communications
|January 22, 2026
PubMed
概括
此摘要是机器生成的。

这项研究对单细胞染色体可访问性 (scATAC-seq) 平台进行基准测试,用于训练深度学习模型以了解基因调节. 来自各种平台的数据的整合使得成本效益高的大型地图集可以用于监管建模.

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Nuclei Isolation from Fresh Frozen Brain Tumors for Single-Nucleus RNA-seq and ATAC-seq
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Nuclei Isolation from Fresh Frozen Brain Tumors for Single-Nucleus RNA-seq and ATAC-seq
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科学领域:

  • 基因组学就是基因组学.
  • 计算生物学 计算生物学
  • 分子生物学分子生物学

背景情况:

  • 了解 cis 调节逻辑对于细胞身份至关重要.
  • 单细胞染色体可访问性 (scATAC-seq) 图谱有助于训练顺序到功能 (S2F) 深度学习模型.
  • 对scATAC-seq培训数据集的最佳标准和S2F模型的平台适用性尚不清楚.

研究的目的:

  • 为S2F模型培训和转录因子 (TF) 足迹进行scATAC-seq平台的基准测试.
  • 评估细胞数和碎片计数对培训数据质量的影响.
  • 评估在不同数据源上训练的S2F模型的性能.

主要方法:

  • 介绍了HyDrop v2,一个改进的自定义滴滴scATAC-seq方法.
  • 对 scATAC-seq 平台进行S2F模型培训和TF足迹的比较.
  • 在定制和商业scATAC-seq数据上训练的S2F模型的比较分析.

主要成果:

  • 较低的碎片数量可以通过增加训练数据集中的细胞数来补偿.
  • 在定制或商业 scATAC-seq 数据上训练的 S2F 模型在增强器预测,序列解释性和 TF 足迹方面表现相似.
  • 来自不同scATAC-seq平台的数据集成有助于大规模,经济高效的地图集构建.

结论:

  • scATAC-seq平台的选择影响S2F模型培训和TF足迹能力.
  • 数据整合策略可以克服个别平台的局限性,以建立全面的监管地图.
  • 这项工作为构建有效的scATAC-seq数据集提供了指导方针,用于基于深度学习的监管建模.