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相关概念视频

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Peptide Bonds

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A peptide bond covalently attaches amino acids through a dehydration reaction. One amino acid's carboxyl group and another amino acid's amino group combine, releasing a water molecule. The resulting bond is the peptide bond. The products that such linkages form are peptides. As more amino acids join this growing chain, the resulting chain is a polypeptide. Each polypeptide has a free amino group at one end. This end has the N-terminal, or the amino-terminal, and the other end has a free...
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In 1928, bacteriologist Frederick Griffith worked on a vaccine for pneumonia, which is caused by Streptococcus pneumoniae bacteria. Griffith studied two pneumonia strains in mice: one pathogenic and one non-pathogenic. Only the pathogenic strain killed host mice.
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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
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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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相关实验视频

Updated: Feb 13, 2026

Deep Neural Networks for Image-Based Dietary Assessment
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深层囊神经网络用于识别使用序列到图像转换的基于本地嵌入特征的抗癌.

Shahid Akbar1,2, Ali Raza3,4,5, Matee Ullah6,5

  • 1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan, China.

BMC biology
|February 12, 2026
PubMed
概括

一个新的模型,pACP-CapsNet,准确地识别了97.0%的抗癌 (ACP). 这种计算工具为癌症药物开发提供了一个有希望的,低副作用的替代方案.

关键词:
抗癌是一种抗癌.囊神经网络是一个神经网络.药物发现 药物发现的转化过程中的转化.预测 预测 预测治疗性类的治疗性类.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 药物发现 药物发现

背景情况:

  • 癌症仍然是一个重大的全球健康挑战.
  • 传统的癌症治疗由于高成本和副作用而面临限制.
  • 抗癌 (ACP) 为癌症治疗提供了一个有希望的替代方案.

研究的目的:

  • 开发一个有效的计算模型,以准确识别ACP.
  • 利用深度学习来增强对抗癌序列的预测.
  • 为了解决现有非洲和非洲国家识别方法的局限性.

主要方法:

  • 用SMR和RECM将输入序列转换为图像.
  • 特征提取涉及HOG,DWT和CLBP转换,创建混合特征空间.
  • 混合青跳跃算法 (SFLA) 用于特征选择.
  • 囊神经网络 (CapsNet) 用于分类.

主要成果:

  • 在培训数据上,pACP-CapsNet模型实现了97.0%的准确性和0.98 AUC.
  • 该模型在ACP240和ACP740测试组上表现出优于现有方法的性能.
  • 集成功能和SFLA选择的功能改善了预测率.

结论:

  • 该pACP-CapsNet模型显示了高效率和稳定性,用于ACP的识别.
  • 这种工具在学术研究和癌症药物设计中具有潜在的应用.
  • 该模型有助于药物诊断和开发新型癌症疗法.