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A Method to Categorize 2-Dimensional Patterns Using Statistics of Spatial Organization

机译:一种利用空间组织统计数据对二维模式进行分类的方法

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摘要

We developed a measurement framework of spatial organization to categorize 2-dimensional patterns from 2 multiscalar biological architectures. We propose that underlying shapes of biological entities can be approached using the statistical concept of degrees of freedom, defining it through expansion of area variability in a pattern. To help scope this suggestion, we developed a mathematical argument recognizing the deep foundations of area variability in a polygonal pattern (spatial heterogeneity). This measure uses a parameter called eutacticity. Our measuring platform of spatial heterogeneity can assign particular ranges of distribution of spatial areas for 2 biological architectures: ecological patterns of Namibia fairy circles and epithelial sheets. The spatial organizations of our 2 analyzed biological architectures are demarcated by being in a particular position among spatial order and disorder. We suggest that this theoretical platform can give us some insights about the nature of shapes in biological systems to understand organizational constraints.
机译:我们开发了一种空间组织的测量框架,可以对来自2个多标量生物学体系结构的二维模式进行分类。我们建议,可以使用自由度的统计概念来接近生物实体的基本形状,并通过扩展区域变异性来定义它。为了帮助解决这个建议,我们开发了一种数学论据,以识别多边形模式(空间异质性)中区域变化的深层基础。该度量使用一个称为eutacticity的参数。我们的空间异质性测量平台可以为2种生物建筑分配特定的空间区域分布范围:纳米比亚仙女圈和上皮片的生态模式。我们分析过的两种生物结构的空间组织是通过在空间顺序和混乱之间的特定位置来划分的。我们建议,该理论平台可以为我们提供一些有关生物系统形状性质的见解,以了解组织约束。

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