7,532 research outputs found
Root system markup language: toward an unified root architecture description language
The number of image analysis tools supporting the extraction of architectural features of root systems has increased over the last years. These tools offer a handy set of complementary facilities, yet it is widely accepted that none of these software tool is able to extract in an efficient way growing array of static and dynamic features for different types of images and species.
We describe the Root System Markup Language (RSML) that has been designed to overcome two major challenges: (i) to enable portability of root architecture data between different software tools in an easy and interoperable manner allowing seamless collaborative work, and (ii) to provide a standard format upon which to base central repositories which will soon arise following the expanding worldwide root phenotyping effort.
RSML follows the XML standard to store 2D or 3D image metadata, plant and root properties and geometries, continuous functions along individual root paths and a suite of annotations at the image, plant or root scales, at one or several time points. Plant ontologies are used to describe botanical entities that are relevant at the scale of root system architecture. An xml-schema describes the features and constraints of RSML and open-source packages have been developed in several languages (R, Excel, Java, Python, C#) to enable researchers to integrate RSML files into popular research workflow
Topological traits of a cellular pattern versus growth rate anisotropy in radish roots
The topology of a cellular pattern, which means the spatial arrangement of cells, directly corresponds with cell packing, which is
crucial for tissue and organ functioning. The topological features of cells that are typically analyzed are the number of their neighbors
and the cell area. To date, the objects of most topological studies have been the growing cells of the surface tissues of plant and animal
organs. Some of these researches also provide verification of Lewis’s Law concerning the linear correlation between the number of
neighboring cells and the cell area. Our aim was to analyze the cellular topology and applicability of Lewis’s Lawto an anisotropically
growing plant organ. The object of our study was the root apex of radish. Based on the tensor description of plant organ growth, we
specified the level of anisotropy in specific zones (the root proper, the columella of the cap and the lateral parts of the cap) and in
specific types of both external (epidermis) and internal tissues (stele and ground tissue) of the apex. The strongest anisotropy occurred
in the root proper, while both zones of the cap showed an intermediate level of anisotropy of growth. Some differences in the topology
of the cellular pattern in the zones were also detected; in the root proper, six-sided cells predominated, while in the root cap columella
and in the lateral parts of the cap, most cells had five neighbors. The correlation coefficient rL between the number of neighboring
cells and the cell area was high in the apex as a whole as well as in all of the zones except the root proper and in all of the tissue types
except the ground tissue. In general, Lewis’s Law was fulfilled in the anisotropically growing radish root apex. However, the level of
the applicability (rL value) of Lewis’s Lawwas negatively correlated with the level of the anisotropy of growth, which may suggest
that in plant organs in the regions of anisotropic growth, the number of neighboring cells is less dependent on the cell size
Learning Generative Models of the Geometry and Topology of Tree-like 3D Objects
How can one analyze detailed 3D biological objects, such as neurons and
botanical trees, that exhibit complex geometrical and topological variation? In
this paper, we develop a novel mathematical framework for representing,
comparing, and computing geodesic deformations between the shapes of such
tree-like 3D objects. A hierarchical organization of subtrees characterizes
these objects -- each subtree has the main branch with some side branches
attached -- and one needs to match these structures across objects for
meaningful comparisons. We propose a novel representation that extends the
Square-Root Velocity Function (SRVF), initially developed for Euclidean curves,
to tree-shaped 3D objects. We then define a new metric that quantifies the
bending, stretching, and branch sliding needed to deform one tree-shaped object
into the other. Compared to the current metrics, such as the Quotient Euclidean
Distance (QED) and the Tree Edit Distance (TED), the proposed representation
and metric capture the full elasticity of the branches (i.e., bending and
stretching) as well as the topological variations (i.e., branch death/birth and
sliding). It completely avoids the shrinkage that results from the edge
collapse and node split operations of the QED and TED metrics. We demonstrate
the utility of this framework in comparing, matching, and computing geodesics
between biological objects such as neurons and botanical trees. The framework
is also applied to various shape analysis tasks: (i) symmetry analysis and
symmetrization of tree-shaped 3D objects, (ii) computing summary statistics
(means and modes of variations) of populations of tree-shaped 3D objects, (iii)
fitting parametric probability distributions to such populations, and (iv)
finally synthesizing novel tree-shaped 3D objects through random sampling from
estimated probability distributions.Comment: under revie
Characterisation of spatial network-like patterns from junctions' geometry
We propose a new method for quantitative characterization of spatial
network-like patterns with loops, such as surface fracture patterns, leaf vein
networks and patterns of urban streets. Such patterns are not well
characterized by purely topological estimators: also patterns that both look
different and result from different morphogenetic processes can have similar
topology. A local geometric cue -the angles formed by the different branches at
junctions- can complement topological information and allow to quantify the
large scale spatial coherence of the pattern. For patterns that grow over time,
such as fracture lines on the surface of ceramics, the rank assigned by our
method to each individual segment of the pattern approximates the order of
appearance of that segment. We apply the method to various network-like
patterns and we find a continuous but sharp dichotomy between two classes of
spatial networks: hierarchical and homogeneous. The first class results from a
sequential growth process and presents large scale organization, the latter
presents local, but not global organization.Comment: version 2, 14 page
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