Nutrigonometry III: curvature, area and differences between performance landscapes.
Nutrigonometry III: curvature, area and differences between performance landscapes.
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DOI：
10.1098/rsos.221326
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发表时间：
2022-11
影响因子：
3.5
通讯作者：
Smoczyk, Knut
中科院分区：
文献类型：
--
作者：
Morimoto, Juliano;Conceicao, Pedro;Smoczyk, Knut
营养是表达生命史和生命树的适应性所必需的基本因素之一。近几十年来，几何框架（GF）已成为一个强大的框架，以获得生物学的见解，通过构建多维性能景观。然而，到目前为止，这些多维景观的许多属性仍然无法访问，由于我们缺乏数学和统计框架的GF分析。这限制了我们理解、描述和估计可能包含来自GF多维性能景观的有用生物信息的参数的能力。在这里，我们提出了一个新的模型来研究GF多维景观的曲率，通过计算被称为高斯和平均曲率的微分几何参数。我们还估计了多维性能景观的表面积作为衡量景观偏离平坦的一种方式。我们将模型应用于该领域的地标数据集，在那里我们还验证了曲率计算所需的假设。特别是，我们证明了线性模型的性能与GF数据中使用的其他模型一样好，使景观可以用二次多项式近似。然后，我们引入了Hausdorff距离作为度量来比较多维景观的相似性。
Nutrition is one of the underlying factors necessary for the expression of life-histories and fitness across the tree of life. In recent decades, the geometric framework (GF) has become a powerful framework to obtain biological insights through the construction of multidimensional performance landscapes. However, to date, many properties of these multidimensional landscapes have remained inaccessible due to our lack of mathematical and statistical frameworks for GF analysis. This has limited our ability to understand, describe and estimate parameters which may contain useful biological information from GF multidimensional performance landscapes. Here, we propose a new model to investigate the curvature of GF multidimensional landscapes by calculating the parameters from differential geometry known as Gaussian and mean curvatures. We also estimate the surface area of multidimensional performance landscapes as a way to measure landscape deviations from flat. We applied the models to a landmark dataset in the field, where we also validate the assumptions required for the calculations of curvature. In particular, we showed that linear models perform as well as other models used in GF data, enabling landscapes to be approximated by quadratic polynomials. We then introduced the Hausdorff distance as a metric to compare the similarity of multidimensional landscapes.
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DOI：
10.1093/gerona/glac172
发表时间：
2023-03-01
影响因子：
5.1
作者：
Senior, Alistair M.
通讯作者：
Senior, Alistair M.
影响因子：
5.2
作者：
Ng, Soon Hwee;Simpson, Stephen J.;Simmons, Leigh W.
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Simmons, Leigh W.
DOI：
10.1073/pnas.40.7.588
发表时间：
1954-01-01
影响因子：
11.1
作者：
DEBREU, G
通讯作者：
DEBREU, G
影响因子：
3
作者：
Morimoto, Juliano;Lihoreau, Mathieu
通讯作者：
Lihoreau, Mathieu
影响因子：
5.2
作者：
Pascacio-Villafan, Carlos;Righini, Nicoletta;Aluja, Martin
通讯作者：
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