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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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相关实验视频

Updated: Mar 6, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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基于集群的渐进对齐与模糊逻辑 (CPA-FL)

Behzad Hajieghrari1

  • 1Department of Agricultural Biotechnology, College of Agriculture, Jahrom University, Jahrom, Iran.

Biochemistry and biophysics reports
|March 5, 2026
PubMed
概括

基于集群的渐进对齐与模糊逻辑 (CPA-FL) 改进了对大型,多样化的数据集的多次序对齐 (MSA). 通过控制集群颗粒度,CPA-FL提供了强大的,准确的对齐,优于其他领先的MSA工具.

科学领域:

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

背景情况:

  • 多重序列对齐 (MSA) 对于理解蛋白质结构,功能和演变至关重要.
  • 调整大型,多样化的序列集是计算密集且容易出现错误的.
  • 现有的MSA方法在可扩展性和稳定性方面扎.

研究的目的:

  • 评估CPA-FL (基于集群的渐进调整与模糊逻辑) 的性能,这是一个新的MSA框架.
  • 与现有的对齐工具相比,评估CPA-FL的稳定性和准确性.
  • 调查聚类策略对MSA质量的影响.

主要方法:

  • 对CPA-FL与Omega,MUSCLE,Kalign,MAFFT和T-Coffee进行了比较.
  • 使用大型蛋白质家族 (HEN1,HST) 和精心策划的BALiBASE 3.0数据集.
  • 在逐步调整框架内采用基于图形的集群和模糊的会员改进.

主要成果:

  • CPA-FL配置实现了竞争性或优异的性能,特别是在保护区.
  • 适度集群与渐进型HMM合,最大限度地提高了对准准确度和进化信号的保存.
  • 基于维特比的合并产生了紧的对齐,而渐进的合并提高了局部准确性.
关键词:
模糊的C-Mean聚类.多个序列对齐的调整.配置文件HMM的合并情况渐进的合并 渐进的合并基于维特尔比的配置HMM合并并.

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Last Updated: Mar 6, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

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结论:

  • CPA-FL为大规模的MSA提供了一个可扩展和生物学上有意义的框架.
  • 该方法提供了对集群细分度的明确控制,减轻了传统渐进对齐中的问题.
  • CPA-FL表现出更好的稳定性和准确性,特别是在具有挑战性的数据集中.