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相关概念视频

Structural Classification of Joints01:20

Structural Classification of Joints

3.3K
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...
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Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

120
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
120
Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

52
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
52
Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

47
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
47
Reducing Line Loss01:18

Reducing Line Loss

150
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...
150
Functional Classification of Joints01:09

Functional Classification of Joints

4.0K
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...
4.0K

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相关实验视频

Updated: Jun 25, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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通过使用坐标网络进行无监督学习来完成缺失的形.

Dave Van Veen1, Jesús G Galaz-Montoya2, Liyue Shen3

  • 1Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.

International journal of molecular sciences
|May 25, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了一种无监督的深度学习方法,用于冷电子断层扫描 (cryo-ET) 重建. 这种方法减少了文物,加快了处理速度,不需要预训练,改善了结构生物学中的3D成像.

关键词:
人工智能的人工智能是人工智能.协调网络 协调网络低温电子显微镜 (cryoEM) 的使用低温电子断层扫描 (cryoET) 是一种冷电子断层扫描.机器学习是机器学习.缺少的形形.重建的重建的重建.模拟模拟是指一个模拟模拟.没有监督的学习学习.

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科学领域:

  • 结构生物学是结构生物学.
  • 生物物理学的生物物理.
  • 计算机成像成像技术

背景情况:

  • 低温电子断层扫描 (cryo-ET) 为生物样本提供纳米级的3D成像.
  • 缺失的形文物,由不完整的角度数据引起,降低了重建质量.
  • 监督深度学习方法 (CNNs) 减少了工件,但需要广泛的,代表性的预训练数据,冒着不准确的风险.

研究的目的:

  • 引入和评估用于冷ET重建的无监督学习方法.
  • 克服监督方法的局限性,特别是预培训要求和数据稀缺问题.
  • 通过减轻缺失的形工件来提高3D重建质量.

主要方法:

  • 开发了一种使用坐标网络 (CNs) 的概念验证无监督学习方法.
  • 优化的网络权重直接与输入预测相对应,消除了预训练的需要.
  • 通过使用in silico数据与基于voxel的图像质量指标和定向Fourier Shell相关性 (FSC) 指标来评估性能.

主要成果:

  • 与监督方法相比,无监督的CN方法减少了3-20倍的重建运行时间.
  • 证明了改进的形状完成和显著减少缺失的形文物.
  • 在真实空间和通过新的定向FSC指标实现了更好的图像质量指标.

结论:

  • 使用CN的无监督学习为冷ET重建的监督方法提供了可行的替代方案,特别是当培训数据有限时.
  • 这种方法加速了重建过程,并增强了3D结构细节.
  • 该研究提供了对监督和无监督深度学习策略的见解,以推进冷ET数据处理.