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For mating, leafhoppers (Cicadellidae) use substrate-borne vibrational signals to communicate. We provide the first complete description of the abdominal chordotonal organs that enable the perception of these signals. This supplementary data provides the aligned stack of 450 semithin serial sections of the first and second abdominal segment of an adult male Rhododendron leafhopper (Graphocephala fennahi). Further, this supplementary data comprises the segmentation files of five chordotonal organs, the exoskeleton, the segmental nerves and the spiracles of the first and the second abdominal segment. Due to time limitations, the structures of only one half of the body were segmented. The specimen was caught by hand net in September 2018 in Berlin-Tiergarten, Germany. Samples were embedded in Araldite® 502 resin and cut transversally in 1 μm thick sections using a Leica ultramicrotome and a DIATOME Histo Jumbo 6.0 mm diamond knife. Sections were placed on microscopic slides and stained with methylene blue/azur II. The images were taken by means of a 3DHISTECH PANNORAMIC SCAN II slide scanner in the Institute of Pathology Charité in Berlin-Mitte, Germany. Images with a voxel size of 0.273809 μm x 0.273809 μm x 1 μm where obtained. The images were converted from MRXS-files to TIFF-files with the 3DHistech software Slide Converter 2.3. Using Photoshop, the images were cropped to the same canvas size and artefacts were removed. All further steps, such as alignment and segmentation, were done with the software Amira. In order to facilitate the further processing of the dataset, the voxels where resampled to a size of 0.547619 μm x 0.547619 μm x 1 μm.
This article revisits a complexly folded silver scroll excavated in Jerash, Jordan in 2014 that was digitally examined in 2015. In this article we apply, examine and discuss a new virtual unfolding technique that results in a clearer image of the scroll’s 17 lines of writing. We also compare it to the earlier unfolding and discuss progress in general analytical tools. We publish the original and the new images as well as the unfolded volume data open access in order to make these available to researchers interested in optimising unfolding processes of various complexly folded materials.
We present a software-assisted workflow for the alignment and matching of filamentous structures across a three-dimensional (3D) stack of serial images. This is achieved by combining automatic methods, visual validation, and interactive correction. After the computation of an initial automatic matching, the user can continuously improve the result by interactively correcting landmarks or matches of filaments. Supported by a visual quality assessment of regions that have been already inspected, this allows a trade-off between quality and manual labor. The software tool was developed in an interdisciplinary collaboration between computer scientists and cell biologists to investigate cell division by quantitative 3D analysis of microtubules (MTs) in both mitotic and meiotic spindles. For this, each spindle is cut into a series of semi-thick physical sections, of which electron tomograms are acquired. The serial tomograms are then stitched and non-rigidly aligned to allow tracing and connecting of MTs across tomogram boundaries. In practice, automatic stitching alone provides only an incomplete solution, because large physical distortions and a low signal-to-noise ratio often cause experimental difficulties. To derive 3D models of spindles despite dealing with imperfect data related to sample preparation and subsequent data collection, semi-automatic validation and correction is required to remove stitching mistakes. However, due to the large number of MTs in spindles (up to 30k) and their resulting dense spatial arrangement, a naive inspection of each MT is too time-consuming. Furthermore, an interactive visualization of the full image stack is hampered by the size of the data (up to 100 GB). Here, we present a specialized, interactive, semi-automatic solution that considers all requirements for large-scale stitching of filamentous structures in serial-section image stacks. To the best of our knowledge, it is the only currently available tool which is able to process data of the type and size presented here. The key to our solution is a careful design of the visualization and interaction tools for each processing step to guarantee real-time response, and an optimized workflow that efficiently guides the user through datasets. The final solution presented here is the result of an iterative process with tight feedback loops between the involved computer scientists and cell biologists.
Thin, curved structures occur in many volumetric datasets. Their analysis using classical volume rendering is difficult because parts of such structures can bend away or hide behind occluding elements. This problem cannot be fully compensated by effective navigation alone, because structure-adapted navigation in the volume is cumbersome and only parts of the structure are visible in each view.
We solve this problem by rendering a spatially transformed view into the volume so that an unobscured visualization of the entire curved structure is obtained. As a result, simple and intuitive navigation becomes possible. The domain of the spatial transform is defined by a triangle mesh that is topologically equivalent to an open disc and that approximates the structure of interest. The rendering is based on ray-casting in which the rays traverse the original curved sub-volume. In order to carve out volumes of varying thickness, the lengths of the rays as well as the position of the mesh vertices can be easily modified in a view-controlled manner by interactive painting. We describe a prototypical implementation and demonstrate the interactive visual inspection of complex structures from digital humanities, biology, medicine, and materials science. Displaying the structure as a whole enables simple inspection of interesting substructures in their original spatial context.
Overall, we show that transformed views utilizing ray-casting-based volume rendering supported by guiding surface meshes and supplemented by local, interactive modifications of ray lengths and vertex positions, represent a simple but versatile approach to effectively visualize thin, curved structures in volumetric data.
Biological armors derive their mechanical integrity in part from their geometric architectures, often involving tessellations: individual structural elements tiled together to form surface shells. The carapace of boxfish, for example, is comprised of mineralized polygonal plates, called scutes, arranged in a complex geometric pattern and nearly completely encasing the body. In contrast to artificial armors, the boxfish exoskeleton grows with the fish; the relationship between the tessellation and the gross structure of the armor is therefore critical to sustained protection throughout growth. To clarify whether or how the boxfish tessellation is maintained or altered with age, we quantify architectural aspects of the tessellated carapace of the longhorn cowfish Lactoria cornuta through ontogeny (across nearly an order of magnitude in standard length) and in a high-throughput fashion, using high-resolution microCT data and segmentation algorithms to characterize the hundreds of scutes that cover each individual. We show that carapace growth is canalized with little variability across individuals: rather than continually adding scutes to enlarge the carapace surface, the number of scutes is surprisingly constant, with scutes increasing in volume, thickness, and especially width with age. As cowfish and their scutes grow, scutes become comparatively thinner, with the scutes at the edges (weak points in a boxy architecture) being some of the thickest and most reinforced in younger animals and thinning most slowly across ontogeny. In contrast, smaller scutes with more variable curvature were found in the limited areas of more complex topology (e.g. around fin insertions, mouth, and anus). Measurements of Gaussian and mean curvature illustrate that cowfish are essentially tessellated boxes throughout life: predominantly zero curvature surfaces comprised of mostly flat scutes, and with scutes with sharp bends used sparingly to form box edges. Since growth of a curved, tiled surface with a fixed number of tiles would require tile restructuring to accommodate the surface’s changing radius of curvature, our results therefore illustrate a previously unappreciated advantage of the odd boxfish morphology: by having predominantly flat surfaces, it is the box-like body form that in fact permits a relatively straightforward growth system of this tessellated architecture (i.e. where material is added to scute edges). Our characterization of the ontogeny and maintenance of the carapace tessellation provides insights into the potentially conflicting mechanical, geometric and developmental constraints of this species, but also perspectives into natural strategies for constructing mutable tiled architectures.
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.
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.
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.
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.
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.
During cell division, kinetochore microtubules (KMTs) provide a physical linkage between the chromosomes and the rest of the spindle. KMTs in mammalian cells are organized into bundles, so-called kinetochore-fibers (k-fibers), but the ultrastructure of these fibers is currently not well characterized. Here we show by large-scale electron tomography that each k-fiber in HeLa cells in metaphase is composed of approximately nine KMTs, only half of which reach the spindle pole. Our comprehensive reconstructions allowed us to analyze the three-dimensional (3D) morphology of k-fibers and their surrounding MTs in detail. We found that k-fibers exhibit remarkable variation in circumference and KMT density along their length, with the pole-proximal side showing a broadening. Extending our structural analysis then to other MTs in the spindle, we further observed that the association of KMTs with non-KMTs predominantly occurs in the spindle pole regions. Our 3D reconstructions have implications for KMT growth and k-fiber self-organization models as covered in a parallel publication applying complementary live-cell imaging in combination with biophysical modeling (Conway et al., 2022). Finally, we also introduce a new visualization tool allowing an interactive display of our 3D spindle data that will serve as a resource for further structural studies on mitosis in human cells.
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.
Background: Despite recent advances in cellular cryo-electron tomography (CET), developing automated tools for macromolecule identification in submolecular resolution remains challenging due to the lack of annotated data and high structural complexities. To date, the extent of the deep learning methods constructed for this problem is limited to conventional Convolutional Neural Networks (CNNs). Identifying macromolecules of different types and sizes is a tedious and time-consuming task. In this paper, we employ a capsule-based architecture to automate the task of macro- molecule identification, that we refer to as 3D-UCaps. In particular, the architecture is composed of three components: feature extractor, capsule encoder, and CNN decoder. The feature extractor converts voxel intensities of input sub-tomograms to activities of local features. The encoder is a 3D Capsule Network (CapsNet) that takes local features to generate a low-dimensional representation of the input. Then, a 3D CNN decoder reconstructs the sub-tomograms from the given representation by upsampling.
Results: We performed binary and multi-class localization and identification tasks on synthetic and experimental data. We observed that the 3D-UNet and the 3D-UCaps had an F1−score mostly above 60% and 70%, respectively, on the test data. In both network architectures, we observed degradation of at least 40% in the F1-score when identifying very small particles (PDB entry 3GL1) compared to a large particle (PDB entry 4D8Q). In the multi-class identification task of experimental data, 3D-UCaps had an F1-score of 91% on the test data in contrast to 64% of the 3D-UNet. The better F1-score of 3D-UCaps compared to 3D-UNet is obtained by a higher precision score. We speculate this to be due to the capsule network employed in the encoder. To study the effect of the CapsNet-based encoder architecture further, we performed an ablation study and perceived that the F1-score is boosted as network depth is increased which
is in contrast to the previously reported results for the 3D-UNet. To present a reproducible work, source code, trained models, data as well as visualization results are made publicly available.
Conclusion: Quantitative and qualitative results show that 3D-UCaps successfully perform various downstream tasks including identification and localization of macro- molecules and can at least compete with CNN architectures for this task. Given that the capsule layers extract both the existence probability and the orientation of the molecules, this architecture has the potential to lead to representations of the data that are better interpretable than those of 3D-UNet.
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.
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.
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.
Polyp-Cavity Segmentation of Cold-Water Corals guided by Ambient Occlusion and Ambient Curvature
(2022)
The segmentation of cavities in three-dimensional images of arbitrary objects is a difficult problem since the cavities are usually connected to the outside of the object without any difference in image intensity. Hence, the information whether a voxel belongs to a cavity or the outside needs to be derived from the ambient space. If a voxel is enclosed by object material, it is very likely that this voxel belongs to a cavity. However, there are dense structures where a voxel might still belong to the outside even though it is surrounded to a large degree by the object. This is, for example, the case for coral colonies. Therefore, additional information needs to be considered to distinguish between those cases. In this paper, we introduce the notion of ambient curvature, present an efficient way to compute it, and use it to segment coral polyp cavities by integrating it into the ambient occlusion framework. Moreover, we combine the ambient curvature with other ambient information in a Gaussian mixture model, trained from a few user scribbles, resulting in a significantly improved cavity segmentation. We showcase the superiority of our approach using four coral colonies of very different morphological types. While in this paper we restrict ourselves to coral data, we believe that the concept of ambient curvature is also useful for other data. Furthermore, our approach is not restricted to curvature but can be easily extended to exploit any properties given on an object's surface, thereby adjusting it to specific applications.
The remarkably complex skeletal systems of the sea stars (Echinodermata, Asteroidea), consisting of hundreds to thousands of individual elements (ossicles), have intrigued investigators for more than 150 years. While the general features and structural diversity of isolated asteroid ossicles have been well documented in the literature, the task of mapping the spatial organization of these constituent skeletal elements in a whole-animal context represents an incredibly laborious process, and as such, has remained largely unexplored.
To address this unmet need, particularly in the context of understanding structure-function relationships in these complex skeletal systems, we present an integrated approach that combines micro-computed tomography, semi-automated ossicle segmentation, data visualization tools, and the production of additively manufactured tangible models to reveal biologically relevant structural data that can be rapidly analyzed in an intuitive manner.
In the present study, we demonstrate this high-throughput workflow by segmenting and analyzing entire skeletal systems of the giant knobby star, Pisaster giganteus, at four different stages of growth. The in-depth analysis, presented herein, provides a fundamental understanding of the three-dimensional skeletal architecture of the sea star body wall, the process of skeletal maturation during growth, and the relationship between skeletal organization and morphological characteristics of individual ossicles.
The widespread implementation of this approach for investigating other species, subspecies, and growth series has the potential to fundamentally improve our understanding of asteroid skeletal architecture and biodiversity in relation to mobility, feeding habits, and environmental specialization in this fascinating group of echinoderms.
Data are often subject to some degree of uncertainty, whether aleatory or epistemic. This applies both to experimental data acquired with sensors as well as to simulation data. Displaying these data and their uncertainty faithfully is crucial for gaining knowledge. Specifically, the effective communication of the uncertainty can influence the interpretation of the data and the user’s trust in the visualization. However, uncertainty-aware visualization has gotten little attention in molecular visualization. When using the established molecular representations, the physicochemical attributes of the molecular data usually already occupy the common visual channels like shape, size, and color. Consequently, to encode uncertainty information, we need to open up another channel by using feature lines. Even though various line variables have been proposed for uncertainty visualizations, they have so far been primarily used for two-dimensional data and there has been little perceptual evaluation. Thus, we conducted two perceptual studies to determine the suitability of the line variables blur, dashing, grayscale, sketchiness, and width for distinguishing several values in molecular visualizations. While our work was motivated by uncertainty visualization, our techniques and study results also apply to other types of scalar data.