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相关概念视频

Mismatch Repair01:20

Mismatch Repair

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Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...
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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Since the discovery of the two BER pathways, there has been a debate about how a cell chooses one pathway over the other and the factors determining this selection. Numerous in vitro experiments have pointed out multiple determinants for the sub-pathway selection. These are:
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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. 
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自主监督的片段对齐与差距.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 数字图像处理 数字图像处理

    背景情况:

    • 传统的图像对齐方法需要重叠的区域或共同的边界.
    • 现实世界的场景,比如文物修复,往往呈现图像碎片有显著的差距和没有重叠.
    • 无监督调整这些碎片化数据是一个具有挑战性的,但至关重要的问题.

    研究的目的:

    • 开发一种无监督,自我监督的方法来学习图像片段的对齐,没有共同的边界或重叠区域.
    • 为应对对准碎片化考古文物,如壁画和马赛克的特定挑战.
    • 在没有外部指导的情况下,利用内部图像统计数据进行准确的对齐.

    主要方法:

    • 一种自我监督的方法,从现有的图像片段中生成"自我例子".
    • 使用对抗性神经网络,特别是空间变压器生成对抗性网络 (GAN).
    • 初始碎片的分片化,以暴露新的对齐关系和内部特征统计数据,将其输入GAN.

    主要成果:

    • 具备以自我监督的方式学习图像片段与空隙的对齐的能力.
    • 在合成数据集和大规模现实世界数据 (壁画和马赛克) 上成功应用.
    • 验证了内部图像统计数据足以准确对齐的假设.

    结论:

    • 拟议的自主监督方法有效地调整损坏的图像片段,而不需要重叠或共同的边界.
    • 该技术提供了一个可行的解决方案,用于无人监督的重建碎片化的文化遗产.
    • 内部图像统计为在自我监督的环境中学习空间转换提供了强有力的信号.