🍩 Database of Original & Non-Theoretical Uses of Topology

(found 4 matches in 0.00152s)
  1. Diverse 3D Cellular Patterns Underlie the Development of Cardamine Hirsuta and Arabidopsis Thaliana Ovules (2023)

    Tejasvinee Atul Mody, Alexander Rolle, Nico Stucki, Fabian Roll, Ulrich Bauer, Kay Schneitz
    Abstract A fundamental question in biology is how organ morphogenesis comes about. The ovules of Arabidopsis thaliana have been established as a successful model to study numerous aspects of tissue morphogenesis; however, little is known regarding the relative contributions and dynamics of differential tissue and cellular growth and architecture in establishing ovule morphogenesis in different species. To address this issue, we generated a 3D digital atlas of Cardamine hirsuta ovule development with full cellular resolution. We combined quantitative comparative morphometrics and topological analysis to explore similarities and differences in the 3D cellular architectures underlying ovule development of the two species. We discovered that they show diversity in the way the three radial cell layers of the primordium contribute to its growth, in the formation of a new cell layer in the inner integument and, in certain cases, in the topological properties of the 3D cell architectures of homologous tissues despite their similar shape. Our work demonstrates the power of comparative 3D cellular morphometry and the importance of internal tissues and their cellular architecture in organ morphogenesis. Summary Statement Quantitative morphometric comparison of 3D digital ovules at full cellular resolution reveals diversity in internal 3D cellular architectures between similarly shaped ovules of Cardamine hirsuta and Arabidopsis thaliana.
  2. Topology Identifies Emerging Adaptive Mutations in SARS-CoV-2 (2021)

    Michael Bleher, Lukas Hahn, Juan Angel Patino-Galindo, Mathieu Carriere, Ulrich Bauer, Raul Rabadan, Andreas Ott
    Abstract The COVID-19 pandemic has lead to a worldwide effort to characterize its evolution through the mapping of mutations in the genome of the coronavirus SARS-CoV-2. Ideally, one would like to quickly identify new mutations that could confer adaptive advantages (e.g. higher infectivity or immune evasion) by leveraging the large number of genomes. One way of identifying adaptive mutations is by looking at convergent mutations, mutations in the same genomic position that occur independently. However, the large number of currently available genomes precludes the efficient use of phylogeny-based techniques. Here, we establish a fast and scalable Topological Data Analysis approach for the early warning and surveillance of emerging adaptive mutations based on persistent homology. It identifies convergent events merely by their topological footprint and thus overcomes limitations of current phylogenetic inference techniques. This allows for an unbiased and rapid analysis of large viral datasets. We introduce a new topological measure for convergent evolution and apply it to the GISAID dataset as of February 2021, comprising 303,651 high-quality SARS-CoV-2 isolates collected since the beginning of the pandemic. We find that topologically salient mutations on the receptor-binding domain appear in several variants of concern and are linked with an increase in infectivity and immune escape, and for many adaptive mutations the topological signal precedes an increase in prevalence. We show that our method effectively identifies emerging adaptive mutations at an early stage. By localizing topological signals in the dataset, we extract geo-temporal information about the early occurrence of emerging adaptive mutations. The identification of these mutations can help to develop an alert system to monitor mutations of concern and guide experimentalists to focus the study of specific circulating variants.
  3. A Stable Multi-Scale Kernel for Topological Machine Learning (2015)

    Jan Reininghaus, Stefan Huber, Ulrich Bauer, Roland Kwitt
    Abstract Topological data analysis offers a rich source of valuable information to study vision problems. Yet, so far we lack a theoretically sound connection to popular kernel-based learning techniques, such as kernel SVMs or kernel PCA. In this work, we establish such a connection by designing a multi-scale kernel for persistence diagrams, a stable summary representation of topological features in data. We show that this kernel is positive definite and prove its stability with respect to the 1-Wasserstein distance. Experiments on two benchmark datasets for 3D shape classification/retrieval and texture recognition show considerable performance gains of the proposed method compared to an alternative approach that is based on the recently introduced persistence landscapes.
  4. Statistical Topological Data Analysis - A Kernel Perspective (2015)

    Roland Kwitt, Stefan Huber, Marc Niethammer, Weili Lin, Ulrich Bauer
    Abstract We consider the problem of statistical computations with persistence diagrams, a summary representation of topological features in data. These diagrams encode persistent homology, a widely used invariant in topological data analysis. While several avenues towards a statistical treatment of the diagrams have been explored recently, we follow an alternative route that is motivated by the success of methods based on the embedding of probability measures into reproducing kernel Hilbert spaces. In fact, a positive definite kernel on persistence diagrams has recently been proposed, connecting persistent homology to popular kernel-based learning techniques such as support vector machines. However, important properties of that kernel enabling a principled use in the context of probability measure embeddings remain to be explored. Our contribution is to close this gap by proving universality of a variant of the original kernel, and to demonstrate its effective use in two-sample hypothesis testing on synthetic as well as real-world data.