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相关概念视频

Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

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In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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相关实验视频

Updated: Sep 13, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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DNFE:用于在生物过程中检测临界点的定向网络流.

Xueqing Peng1,2, Rui Qiao1,2, Peiluan Li1,2

  • 1School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, China.

PLoS computational biology
|July 29, 2025
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概括

我们开发了一种新的定向网络流 (DNFE) 方法,以使用omics数据识别生物系统中的关键转折点. 这种强大的方法有效地检测动态网络生物标志物和调节基因关系,即使是在大规模的,杂的数据集中.

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科学领域:

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

背景情况:

  • 动态生物过程表现出关键的临界点,标志着状态过渡.
  • 使用非定向网络的传统方法在高维,小样本的奥米克数据,特别是单细胞数据方面遇到了困难.
  • 识别临界点及其驱动网络对于预测和减轻生物转变至关重要.

研究的目的:

  • 开发一种可靠的方法,从omics数据中识别转折点和动态网络生物标志物.
  • 解决传统网络分析方法在高维生物数据集中的局限性.
  • 探索基因调节关系,并确定生物转变中的关键驱动因素.

主要方法:

  • 开发了定向网络流 (DNFE) 方法,将omics数据转化为定向网络.
  • 将DNFE应用于各种数据集,包括单细胞RNA测序 (scRNA-seq),批量瘤和血液数据.
  • 通过对各种噪声水平和大型基因调控网络的数值模拟来验证该方法.

主要成果:

  • 在多个现实数据集中,DNFE有效地识别了关键状态及其动态网络生物标志物.
  • 该方法证明了对噪声的稳定性,并且在临界点检测方面表现优于现有的方法.
  • DNFE成功地预测了活跃的转录因子,并确定了以前被忽视的"黑暗基因".

结论:

  • 该DNFE方法提供了一个强大的和有效的框架,用于分析动态生物过程使用OMIC数据.
  • DNFE增强了关键状态,调控网络和关键基因的识别,推进了系统生物学研究.
  • 这种方法适用于单细胞和批量omics数据,在生物发现中具有广泛的实用性.