出租车在哪里?在三维共聚焦图像中包含凹肌动蛋白束(cab)的特定区域的验证。

Q2 Medicine
Doyoung Park
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引用次数: 0

摘要

cab(凹形肌动蛋白束)以不同于传统的纵向f -肌动蛋白束的方式横向朝向支架。cab存在于特定区域,而不存在于随机区域。从生物学上讲,cab被发展成将细胞牢牢地附着在纤维上,因此cab在细胞附近被发现。基于这些知识,我们仔细检查了3D共聚焦显微图像,其中包含纤维支架,肌动蛋白和细胞。然后,我们假设含有高纤维紧密度、高肌动蛋白紧密度和高细胞密度的区域会有很多cab。在这项研究中,我们想要证明这个假设。我们首先结合两点相关函数来定义紧度的度量。然后,我们用贝叶斯定理来证明上述假设。作为假设,我们的结果验证了cab存在于光纤网络的高密实度,肌动蛋白分布的高密实度和高密度细胞的区域。因此,我们得出结论,cab的发展是为了将细胞牢固地附着在纤维支架上。这一发现可能会在未来的研究中得到进一步的数学验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Where Do CABs Exist? Verification of a specific region containing concave Actin Bundles (CABs) in a 3-Dimensional confocal image.

CABs (Concave Actin Bundles) are oriented against the scaffold transversally in a manner different from traditional longitudinal F-actin bundles. CABs are present in a specific area, and do not exist in random areas. Biologically, CABs are developed to attach cells to fibers firmly so that CABs are found near cells. Based on this knowledge, we closely examined 3D confocal microcopy images containing fiber scaffolds, actin, and cells. Then, we assumed that the areas containing high values of compactness of fiber, compactness of actin, and density of cells would have many numbers of CABs.In this research, we wanted to prove this assumption. We first incorporated a two-point correlation function to define a measure of compactness. Then, we used the Bayes' theorem to prove the above assumption. As the assumption, our results verified that CABs exist in an area of high compactness of a fiber network, high compactness of actin distribution, and high density of cells. Thus, we concluded that CABs are developed to attach cells to a fibrillar scaffold firmly. This finding may be further verified mathematically in future studies.

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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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