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The present dataset contains the 3D models analyzed in Berio, F., Bayle, Y., Baum, D., Goudemand, N., and Debiais-Thibaud, M. 2022. Hide and seek shark teeth in Random Forests: Machine learning applied to Scyliorhinus canicula. It contains the head surfaces of 56 North Atlantic and Mediterranean small-spotted catsharks Scyliorhinus canicula, from which tooth surfaces were further extracted to perform geometric morphometrics and machine learning.
3D shapes provide substantially more information than 2D images. However, the acquisition of 3D shapes is sometimes very difficult or even impossible in comparison with acquiring 2D images, making it necessary to derive the 3D shape from 2D images. Although this is, in general, a mathematically ill-posed problem, it might be solved by constraining the problem formulation using prior information. Here, we present a new approach based on Kendall’s shape space to reconstruct 3D shapes from single monocular 2D images. The work is motivated by an application to study the feeding behavior of the basking shark, an endangered species whose massive size and mobility render 3D shape data nearly impossible to obtain, hampering understanding of their feeding behaviors and ecology. 2D images of these animals in feeding position, however, are readily available. We compare our approach with state-of-the-art shape-based approaches both on human stick models and on shark head skeletons. Using a small set of training shapes, we show that the Kendall shape space approach is substantially more robust than previous methods and always results in plausible shapes. This is essential for the motivating application in which specimens are rare and therefore only few training shapes are available.
A Soft-Correspondence Approach to Shape-based Disease Grading with Graph Convolutional Networks
(2022)
Shape analysis provides principled means for understanding anatomical structures from medical images. The underlying notions of shape spaces, however, come with strict assumptions prohibiting the analysis of incomplete and/or topologically varying shapes. This work aims to alleviate these limitations by adapting the concept of soft correspondences. In particular, we present a graph-based learning approach for morphometric classification of disease states that is based on a generalized notion of shape correspondences in terms of functional maps. We demonstrate the performance of the derived classifier on the open-access ADNI database for differentiating normal controls and subjects with Alzheimer’s disease. Notably, our experiment shows that our approach can improve over state-of-the-art from geometric deep learning.
The analysis of brain networks is central to neurobiological research. In this context the following tasks often arise: (1) understand the cellular composition of a reconstructed neural tissue volume to determine the nodes of the brain network; (2) quantify connectivity features statistically; and (3) compare these to predictions of mathematical models. We present a framework for interactive, visually supported accomplishment of these tasks. Its central component, the stratification matrix viewer, allows users to visualize the distribution of cellular and/or connectional properties of neurons at different levels of aggregation. We demonstrate its use in four case studies analyzing neural network data from the rat barrel cortex and human temporal cortex.
Grasping, in both biological and engineered mechanisms, can be highly sensitive to the gripper and object morphology, as well as perception and motion planning. Here we circumvent the need for feedback or precise planning by using an array of fluidically-actuated slender hollow elastomeric filaments to actively entangle with objects that vary in geometric and topological complexity. The resulting stochastic interactions enable a unique soft and conformable grasping strategy across a range of target objects that vary in size, weight, and shape. We experimentally evaluate the grasping performance of our strategy, and use a computational framework for the collective mechanics of flexible filaments in contact with complex objects to explain our findings. Overall, our study highlights how active collective entanglement of a filament array via an uncontrolled, spatially distributed scheme provides new options for soft, adaptable grasping.
Our ability to grasp and understand complex phenomena is essentially based on recognizing structures and relating these to each other. For example, any meteorological description of a weather condition and explanation of its evolution recurs to meteorological structures, such as convection and circulation structures, cloud fields and rain fronts. All of these are spatiotemporal structures, defined by time-dependent patterns in the underlying fields. Typically, such a structure is defined by a verbal description that corresponds to the more or less uniform, often somewhat vague mental images of the experts.
However, a precise, formal definition of the structures or, more generally, concepts is often desirable, e.g., to enable automated data analysis or the development of phenomenological models. Here, we present a systematic approach and an interactive tool to obtain formal definitions of spatiotemporal structures. The tool enables experts to evaluate and compare different structure definitions on the basis of data sets with time-dependent fields that contain the respective structure. Since structure definitions are typically parameterized, an essential part is to identify parameter ranges that lead to desired structures in all time steps. In addition, it is important to allow a quantitative assessment of the resulting structures simultaneously. We demonstrate the use of the tool by applying it to two meteorological examples: finding structure definitions for vortex cores and center lines of temporarily evolving tropical cyclones.
Ideally, structure definitions should be objective and applicable to as many data sets as possible. However, finding such definitions, e.g., for the common atmospheric structures in meteorology, can only be a long-term goal. The proposed procedure, together with the presented tool, is just a first systematic approach aiming at facilitating this long and arduous way.
Shark populations that are distributed alongside a latitudinal gradient often display body size differences at sexual maturity and vicariance patterns related to their number of tooth files. Previous works have demonstrated that Scyliorhinus canicula exhibits distinct genetic structures, life history traits, and body size differences between populations inhabiting the North Atlantic Ocean and the Mediterranean Sea. In this work, we sample more than 3,000 S. canicula teeth from 56 specimens and provide and use a dataset containing their shape coordinates. We investigate tooth shape and form differences between a Mediterranean and an Atlantic S. canicula population using two approaches. Classification results show that the classical geometric morphometric framework is outperformed by an original Random Forests-based framework. Visually, both S. canicula populations share similar ontogenetic trends and timing of gynandric heterodonty emergence but the Atlantic population has bigger, blunter teeth, and less numerous accessory cusps than the Mediterranean population. According to the models, the populations are best differentiated based on their lateral tooth edges, which bear accessory cusps, and the tooth centroid sizes significantly improve classification performances. The differences observed are discussed in light of dietary and behavioural habits of the populations considered. The method proposed in this study could be further adapted to complement DNA analyses to identify shark species or populations based on tooth morphologies. This process would be of particular interest for fisheries management and identification of shark fossils.
The Hemiptera is the largest non-endopterygote insect order comprising approximately 98,000 recent species. All species of the suborders Cicadomorpha (leafhoppers, spittlebugs, treehoppers and cicadas) and Fulgoromorpha (planthoppers) feed by sucking sap from plant tissues and are thus often vectors for economically important phytopathogens. Except for the cicadas (Cicadomorpha: Cicadoidea: Cicadidae) which produce air-borne sounds, all species of the suborders Cicadomorpha and Fulgoromorpha communicate by vibrational (substrate-borne) signals. While the generation of these signals has been extensively investigated, the mechanisms of perception are poorly understood. This study provides a full description and 3D reconstruction of a large and complex array of six paired chordotonal organs in the first abdominal segments of the Rhododendron leafhopper Graphocephala fennahi (Cicadomorpha: Membracoidea: Cicadellidae). Further we were able to identify homologous organs in the closely related spittlebug Philaenus spumarius (Cicadomorpha: Cercopoidea: Aphrophoridae) and the planthopper Issus coleoptratus (Fulgoromorpha: Fulgoroidea: Issidae). The configuration is congruent with the abdominal chordotonal organs in cicadas, where one of them is an elaborate tympanal organ. This indicates that these organs, together with the tymbal organ constitute a synapomorphy of the Tymbalia (Hemiptera excl. Sternorrhyncha). Our results contribute to the understanding of the evolution from substrate-borne to airborne communication in insects.
Faithful chromosome segregation requires the assembly of a bipolar spindle, consisting of two antiparallel microtubule (MT) arrays having most of their minus ends focused at the spindle poles and their plus ends overlapping in the spindle midzone. Spindle assembly, chromosome alignment and segregation require highly dynamic MTs. The plus ends of MTs have been extensively investigated; instead, their minus end structure remains poorly characterized. Here, we used large-scale electron tomography to study the morphology of the MT minus ends in 3D-reconstructed metaphase spindles in HeLa cells. In contrast to the homogeneous open morphology of the MT plus ends at the kinetochores, we found that MT minus ends are heterogeneous showing either open or closed morphologies. Silencing the minus-end specific stabilizer, MCRS1 increased the proportion of open MT minus ends. Altogether, these data suggest a correlation between the morphology and the dynamic state of the MT ends. Taking this heterogeneity of the MT minus end morphologies into account, our work indicates an unsynchronized behavior of MTs at the spindle poles, thus laying the ground for further studies on the complexity of MT dynamics regulation.