@misc{TitschackBaum2014, author = {Titschack, J{\"u}rgen and Baum, Daniel}, title = {Advanced computed tomography analyses of cold-water coral mound cores: new insights into mound formation processes}, journal = {Poster, 19th International Sedimentological Congress, Geneva, Switzerland, 2014, August 18 - 22}, year = {2014}, language = {en} } @misc{TitschackBaum2015, author = {Titschack, J{\"u}rgen and Baum, Daniel}, title = {Ambient occlusion - a powerful algorithm to segment skeletal intrapores and gastral cavities in dendrophyllid cold-water corals}, journal = {Poster, 31st IAS Meeting of Sedimentology, 2015, June 22-25, Krak{\´o}w, Poland}, year = {2015}, language = {en} } @misc{KnoetelSeidelWeaveretal.2015, author = {Kn{\"o}tel, David and Seidel, Ronald and Weaver, James C. and Baum, Daniel and Dean, Mason N.}, title = {Segmentation of the Tessellated Mineralized Endoskeleton of Sharks and Rays}, journal = {Poster, Tomography for Scientific Advancement symposium (ToScA), Manchester, UK, September 3 - 4, 2015}, year = {2015}, abstract = {The cartilaginous endoskeletons of sharks and rays are covered by tiles of mineralized cartilage called tesserae that enclose areas of unmineralized cartilage. These tesselated layers are vital to the growth as well as the material properties of the skeleton, providing both flexibility and strength. An understanding of the principles behind the tiling of the mineralized layer requires a quantitative analysis of shark and ray skeletal tessellation. However, since a single skeletal element comprises several thousand tesserae, manual segmentation is infeasible. We developed an automated segmentation pipeline that, working from micro-CT data, allows quantification of all tesserae in a skeletal element in less than an hour. Our segmentation algorithm relies on aspects we have learned of general tesseral morphology. In micro-CT scans, tesserae usually appear as round or star-shaped plate-like tiles, wider than deep and connected by mineralized intertesseral joints. Based on these observations, we exploit the distance map of the mineralized layer to separate individual tiles using a hierarchical watershed algorithm. Utilizing a two-dimensional distance map that measures the distance in the plane of the mineralized layer only greatly improves the segmentation. We developed post-processing techniques to quickly correct segmentation errors in regions where tesseral shape differs from the assumed shape. Evaluation of our results is done qualitatively by visual comparison with raw datasets, and quantitatively by comparison to manual segmentations. Furthermore, we generate two-dimensional abstractions of the tiling network based on the neighborhood, allowing representation of complex, biological forms as simpler geometries. We apply our newly developed techniques to the analysis of the left and right hyomandibulae of four ages of stingray enabling the first quantitative analyses of the tesseral tiling structure, while clarifying how these patterns develop across ontogeny.}, language = {en} } @misc{KnoetelSeidelHosnyetal.2016, author = {Kn{\"o}tel, David and Seidel, Ronald and Hosny, Ahmed and Zaslansky, Paul and Weaver, James C. and Baum, Daniel and Dean, Mason N.}, title = {Understanding the Tiling Rules of the Tessellated Mineralized Endoskeleton of Sharks and Rays}, journal = {Poster, Euro Bio-inspired Materials 2016, Potsdam, Germany, February 22 - 25, 2016}, year = {2016}, abstract = {The endoskeletons of sharks and rays are composed of an unmineralized cartilaginous core, covered in an outer layer of mineralized tiles called tesserae. The tessellated layer is vital to the growth as well as the material properties of the skeletal element, providing both flexibility and strength. However, characterizing the relationship between tesseral size and shape, and skeletal growth and mechanics is challenging because tesserae are small (a few hundred micrometers wide), anchored to the surrounding tissue in complex three-dimensional ways, and occur in huge numbers. Using a custom-made semi-automatic segmentation algorithm, we present the first quantitative and three-dimensional description of tesserae in micro-CT scans of whole skeletal elements. Our segmentation algorithm relies on aspects we have learned of general tesseral morphology. We exploit the distance map of the mineralized layer to separate individual tiles using a hierarchical watershed algorithm. Additionally, we have developed post-processing techniques to quickly correct segmentation errors. Our data reveals that the tessellation is not regular, with tesserae showing a great range of shapes, sizes and number of neighbors. This is partly region-dependent: for example, thick, columnar tesserae are arranged in series along convex edges with small radius of curvature (RoC), whereas more brick-or disc-shaped tesserae are found in planar areas. We apply our newly developed techniques on the left and right hyomandibula (skeletal elements supporting the jaws) from four different ages of a stingray species, to clarify how tiling patterns develop across ontogeny and differ within and between individuals. We evaluate the functional consequences of tesseral morphologies using finite element analysis and 3d-printing, for a better understanding of shark skeletal mechanics, but also to extract fundamental engineering design principles of tiling arrangements on load-bearing three-dimensional objects.}, language = {en} } @misc{SeidelKnoetelBaumetal.2014, author = {Seidel, Ronald and Kn{\"o}tel, David and Baum, Daniel and Weaver, James C. and Dean, Mason N.}, title = {Material and structural characterization of mineralized elasmobranch cartilage - lessons in repeated tiling patterns in mechanically loaded 3D objects}, journal = {Poster, Tomography for Scientific Advancement symposium (ToScA), London, UK, September 1 - 3, 2014}, year = {2014}, abstract = {Biological tissues achieve a wide range of properties and function, however with limited components. The organization of these constituent parts is a decisive factor in the impressive properties of biological materials, with tissues often exhibiting complex arrangements of hard and soft materials. The "tessellated" cartilage of the endoskeleton of sharks and rays, for example, is a natural composite of mineralized polygonal tiles (tesserae), collagen fiber bundles, and unmineralized cartilage, resulting in a material that is both flexible and strong, with optimal stiffness. The properties of the materials and the tiling geometry are vital to the growth and mechanics of the system, but had not been investigated due to the technical challenges involved. We use high-resolution materials characterization techniques (qBEI, µCT) to show that tesserae exhibit great variability in mineral density, supporting theories of accretive growth mechanisms. We present a developmental series of tesserae and outline the development of unique structural features that appear to function in load bearing and energy dissipation, with some structural features far exceeding cortical bone's mineral content and tissue stiffness. To examine interactions among tesserae, we developed an advanced tiling-recognition-algorithm to semi-automatically detect and isolate individual tiles in microCT scans of tesseral mats. The method allows quantification of shape variation across a wide area, allowing localization of regions of high/low reinforcement or flexibility in the skeleton. The combination of our material characterization and visualization techniques allows the first quantitative 3d description of anatomy and material properties of tesserae and the organization of tesseral networks in elasmobranch mineralized cartilage, providing insight into form-function relationships of the repeating tiled pattern. We aim to combine detailed knowledge of intra-tesseral morphology and mineralization to model the relationships of tesseral shapes and skeletal surface curvature, to understand fundamental tiling laws important for complex, mechanically loaded 3d objects.}, language = {en} } @inproceedings{PaetschBaumProhaskaetal.2015, author = {Paetsch, Olaf and Baum, Daniel and Prohaska, Steffen and Ehrig, Karsten and Meinel, Dietmar and Ebell, Gino}, title = {3D Corrosion Detection in Time-dependent CT Images of Concrete}, booktitle = {DIR-2015 Proceedings}, year = {2015}, abstract = {In civil engineering, the corrosion of steel reinforcements in structural elements of concrete bares a risk of stability-reduction, mainly caused by the exposure to chlorides. 3D computed tomography (CT) reveals the inner structure of concrete and allows one to investigate the corrosion with non-destructive testing methods. To carry out such investigations, specimens with a large artificial crack and an embedded steel rebar have been manufactured. 3D CT images of those specimens were acquired in the original state. Subsequently three cycles of electrochemical pre-damaging together with CT imaging were applied. These time series have been evaluated by means of image processing algorithms to segment and quantify the corrosion products. Visualization of the results supports the understanding of how corrosion propagates into cracks and pores. Furthermore, pitting of structural elements can be seen without dismantling. In this work, several image processing and visualization techniques are presented that have turned out to be particularly effective for the visualization and segmentation of corrosion products. Their combination to a workflow for corrosion analysis is the main contribution of this work.}, language = {en} } @misc{KnoetelSeidelProhaskaetal.2017, author = {Kn{\"o}tel, David and Seidel, Ronald and Prohaska, Steffen and Dean, Mason N. and Baum, Daniel}, title = {Automated Segmentation of Complex Patterns in Biological Tissues: Lessons from Stingray Tessellated Cartilage}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-65785}, year = {2017}, abstract = {Introduction - Many biological structures show recurring tiling patterns on one structural level or the other. Current image acquisition techniques are able to resolve those tiling patterns to allow quantitative analyses. The resulting image data, however, may contain an enormous number of elements. This renders manual image analysis infeasible, in particular when statistical analysis is to be conducted, requiring a larger number of image data to be analyzed. As a consequence, the analysis process needs to be automated to a large degree. In this paper, we describe a multi-step image segmentation pipeline for the automated segmentation of the calcified cartilage into individual tesserae from computed tomography images of skeletal elements of stingrays. Methods - Besides applying state-of-the-art algorithms like anisotropic diffusion smoothing, local thresholding for foreground segmentation, distance map calculation, and hierarchical watershed, we exploit a graph-based representation for fast correction of the segmentation. In addition, we propose a new distance map that is computed only in the plane that locally best approximates the calcified cartilage. This distance map drastically improves the separation of individual tesserae. We apply our segmentation pipeline to hyomandibulae from three individuals of the round stingray (Urobatis halleri), varying both in age and size. Results - Each of the hyomandibula datasets contains approximately 3000 tesserae. To evaluate the quality of the automated segmentation, four expert users manually generated ground truth segmentations of small parts of one hyomandibula. These ground truth segmentations allowed us to compare the segmentation quality w.r.t. individual tesserae. Additionally, to investigate the segmentation quality of whole skeletal elements, landmarks were manually placed on all tesserae and their positions were then compared to the segmented tesserae. With the proposed segmentation pipeline, we sped up the processing of a single skeletal element from days or weeks to a few hours.}, language = {en} } @article{KnoetelSeidelProhaskaetal.2017, author = {Kn{\"o}tel, David and Seidel, Ronald and Prohaska, Steffen and Dean, Mason N. and Baum, Daniel}, title = {Automated Segmentation of Complex Patterns in Biological Tissues: Lessons from Stingray Tessellated Cartilage}, journal = {PLOS ONE}, doi = {10.1371/journal.pone.0188018}, year = {2017}, abstract = {Introduction - Many biological structures show recurring tiling patterns on one structural level or the other. Current image acquisition techniques are able to resolve those tiling patterns to allow quantitative analyses. The resulting image data, however, may contain an enormous number of elements. This renders manual image analysis infeasible, in particular when statistical analysis is to be conducted, requiring a larger number of image data to be analyzed. As a consequence, the analysis process needs to be automated to a large degree. In this paper, we describe a multi-step image segmentation pipeline for the automated segmentation of the calcified cartilage into individual tesserae from computed tomography images of skeletal elements of stingrays. Methods - Besides applying state-of-the-art algorithms like anisotropic diffusion smoothing, local thresholding for foreground segmentation, distance map calculation, and hierarchical watershed, we exploit a graph-based representation for fast correction of the segmentation. In addition, we propose a new distance map that is computed only in the plane that locally best approximates the calcified cartilage. This distance map drastically improves the separation of individual tesserae. We apply our segmentation pipeline to hyomandibulae from three individuals of the round stingray (Urobatis halleri), varying both in age and size. Results - Each of the hyomandibula datasets contains approximately 3000 tesserae. To evaluate the quality of the automated segmentation, four expert users manually generated ground truth segmentations of small parts of one hyomandibula. These ground truth segmentations allowed us to compare the segmentation quality w.r.t. individual tesserae. Additionally, to investigate the segmentation quality of whole skeletal elements, landmarks were manually placed on all tesserae and their positions were then compared to the segmented tesserae. With the proposed segmentation pipeline, we sped up the processing of a single skeletal element from days or weeks to a few hours.}, language = {en} } @misc{KramerNoackBaumetal.2017, author = {Kramer, Tobias and Noack, Matthias and Baum, Daniel and Hege, Hans-Christian and Heller, Eric J.}, title = {Dust and gas emission from cometary nuclei: the case of comet 67P/Churyumov-Gerasimenko}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-66338}, year = {2017}, abstract = {Comets display with decreasing solar distance an increased emission of gas and dust particles, leading to the formation of the coma and tail. Spacecraft missions provide insight in the temporal and spatial variations of the dust and gas sources located on the cometary nucleus. For the case of comet 67P/Churyumov-Gerasimenko (67P/C-G), the long-term obser- vations from the Rosetta mission point to a homogeneous dust emission across the entire illuminated surface. Despite the homogeneous initial dis- tribution, a collimation in jet-like structures becomes visible. We propose that this observation is linked directly to the complex shape of the nucleus and projects concave topographical features into the dust coma. To test this hypothesis, we put forward a gas-dust description of 67P/C-G, where gravitational and gas forces are accurately determined from the surface mesh and the rotation of the nucleus is fully incorporated. The emerging jet-like structures persist for a wide range of gas-dust interactions and show a dust velocity dependent bending.}, language = {en} } @phdthesis{Baum2007, author = {Baum, Daniel}, title = {A Point-Based Algorithm for Multiple 3D Surface Alignment of Drug-Sized Molecules}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:188-fudissthesis000000002759-2}, school = {Freie Universit{\"a}t Berlin}, year = {2007}, abstract = {One crucial step in virtual drug design is the identification of new lead structures with respect to a pharmacological target molecule. The search for new lead structures is often done with the help of a pharmacophore, which carries the essential structural as well as physico-chemical properties that a molecule needs to have in order to bind to the target molecule. In the absence of the target molecule, such a pharmacophore can be established by comparison of a set of active compounds. In order to identify their common features,a multiple alignment of all or most of the active compounds is necessary. Moreover, since the "outer shape" of the molecules plays a major role in the interaction between drug and target, an alignment algorithm aiming at the identification of common binding properties needs to consider the molecule's "outer shape", which can be approximated by the solvent excluded surface. In this thesis, we present a new approach to molecular surface alignment based on a discrete representation of shape as well as physico-chemical properties by points distributed on the solvent excluded surface. We propose a new method to distribute points regularly on a surface w.r.t. a smoothly varying point density given on that surface. Since the point distribution algorithm is not restricted to molecular surfaces, it might also be of interest for other applications. For the computation of pairwise surface alignments, we extend an existing point matching scheme to surface points, and we develop an efficient data structure speeding up the computation by a factor of three. Moreover, we present an approach to compute multiple alignments from pairwise alignments, which is able to handle a large number of surface points. All algorithms are evaluated on two sets of molecules: eight thermolysin inhibitors and seven HIV-1 protease inhibitors. Finally, we compare the results obtained from surface alignment with the results obtained by applying an atom alignment approach.}, language = {en} } @article{ToulkeridouGutierrezBaumetal.2021, author = {Toulkeridou, Evropi and Gutierrez, Carlos Enrique and Baum, Daniel and Doya, Kenji and Economo, Evan P.}, title = {Automated segmentation of insect anatomy from micro-CT images using deep learning}, journal = {bioRxiv}, doi = {10.1101/2021.05.29.446283}, year = {2021}, language = {en} } @article{LindowBruenigDercksenetal.2021, author = {Lindow, Norbert and Br{\"u}nig, Florian and Dercksen, Vincent J. and Fabig, Gunar and Kiewisz, Robert and Redemann, Stefanie and M{\"u}ller-Reichert, Thomas and Prohaska, Steffen and Baum, Daniel}, title = {Semi-automatic stitching of filamentous structures in image stacks from serial-section electron tomography}, volume = {284}, journal = {Journal of Microscopy}, number = {1}, doi = {10.1111/jmi.13039}, pages = {25 -- 44}, year = {2021}, abstract = {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.}, language = {en} } @article{ToulkeridouGutierrezBaumetal.2023, author = {Toulkeridou, Evropi and Gutierrez, Carlos Enrique and Baum, Daniel and Doya, Kenji and Economo, Evan P.}, title = {Automated segmentation of insect anatomy from micro-CT images using deep learning}, volume = {3}, journal = {Natural Sciences}, number = {4}, doi = {10.1002/ntls.20230010}, year = {2023}, abstract = {Three-dimensional (3D) imaging, such as micro-computed tomography (micro-CT), is increasingly being used by organismal biologists for precise and comprehensive anatomical characterization. However, the segmentation of anatomical structures remains a bottleneck in research, often requiring tedious manual work. Here, we propose a pipeline for the fully-automated segmentation of anatomical structures in micro-CT images utilizing state-of-the-art deep learning methods, selecting the ant brain as a test case. We implemented the U-Net architecture for 2D image segmentation for our convolutional neural network (CNN), combined with pixel-island detection. For training and validation of the network, we assembled a dataset of semi-manually segmented brain images of 76 ant species. The trained network predicted the brain area in ant images fast and accurately; its performance tested on validation sets showed good agreement between the prediction and the target, scoring 80\% Intersection over Union (IoU) and 90\% Dice Coefficient (F1) accuracy. While manual segmentation usually takes many hours for each brain, the trained network takes only a few minutes. Furthermore, our network is generalizable for segmenting the whole neural system in full-body scans, and works in tests on distantly related and morphologically divergent insects (e.g., fruit flies). The latter suggests that methods like the one presented here generally apply across diverse taxa. Our method makes the construction of segmented maps and the morphological quantification of different species more efficient and scalable to large datasets, a step toward a big data approach to organismal anatomy.}, language = {en} } @inproceedings{HarthBastTroidletal.2023, author = {Harth, Philipp and Bast, Arco and Troidl, Jakob and Meulemeester, Bjorge and Pfister, Hanspeter and Beyer, Johanna and Oberlaender, Marcel and Hege, Hans-Christian and Baum, Daniel}, title = {Rapid Prototyping for Coordinated Views of Multi-scale Spatial and Abstract Data: A Grammar-based Approach}, booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM)}, doi = {10.2312/vcbm.20231218}, year = {2023}, abstract = {Visualization grammars are gaining popularity as they allow visualization specialists and experienced users to quickly create static and interactive views. Existing grammars, however, mostly focus on abstract views, ignoring three-dimensional (3D) views, which are very important in fields such as natural sciences. We propose a generalized interaction grammar for the problem of coordinating heterogeneous view types, such as standard charts (e.g., based on Vega-Lite) and 3D anatomical views. An important aspect of our web-based framework is that user interactions with data items at various levels of detail can be systematically integrated and used to control the overall layout of the application workspace. With the help of a concise JSON-based specification of the intended workflow, we can handle complex interactive visual analysis scenarios. This enables rapid prototyping and iterative refinement of the visual analysis tool in collaboration with domain experts. We illustrate the usefulness of our framework in two real-world case studies from the field of neuroscience. Since the logic of the presented grammar-based approach for handling interactions between heterogeneous web-based views is free of any application specifics, it can also serve as a template for applications beyond biological research.}, language = {en} } @article{LongrenEigenShubitidzeetal.2023, author = {Longren, Luke L. and Eigen, Lennart and Shubitidze, Ani and Lieschnegg, Oliver and Baum, Daniel and Nyakatura, John A. and Hildebrandt, Thomas and Brecht, Michael}, title = {Dense Reconstruction of Elephant Trunk Musculature}, volume = {33}, journal = {Current Biology}, doi = {10.1016/j.cub.2023.09.007}, pages = {1 -- 8}, year = {2023}, abstract = {The elephant trunk operates as a muscular hydrostat and is actuated by the most complex musculature known in animals. Because the number of trunk muscles is unclear, we performed dense reconstructions of trunk muscle fascicles, elementary muscle units, from microCT scans of an Asian baby elephant trunk. Muscle architecture changes markedly across the trunk. Trunk tip and finger consist of about 8,000 extraordinarily filigree fascicles. The dexterous finger consists exclusively of microscopic radial fascicles pointing to a role of muscle miniaturization in elephant dexterity. Radial fascicles also predominate (at 82\% volume) the remainder of the trunk tip and we wonder if radial muscle fascicles are of particular significance for fine motor control of the dexterous trunk tip. By volume, trunk-shaft muscles comprise one-third of the numerous, small radial muscle fascicles, two-thirds of the three subtypes of large longitudinal fascicles (dorsal longitudinals, ventral outer obliques, and ventral inner obliques), and a small fraction of transversal fascicles. Shaft musculature is laterally, but not radially, symmetric. A predominance of dorsal over ventral radial muscles and of ventral over dorsal longitudinal muscles may result in a larger ability of the shaft to extend dorsally than ventrally and to bend inward rather than outward. There are around 90,000 trunk muscle fascicles. While primate hand control is based on fine control of contraction by the convergence of many motor neurons on a small set of relatively large muscles, evolution of elephant grasping has led to thousands of microscopic fascicles, which probably outnumber facial motor neurons.}, language = {en} } @article{KiewiszBaumMuellerReichertetal.2023, author = {Kiewisz, Robert and Baum, Daniel and M{\"u}ller-Reichert, Thomas and Fabig, Gunar}, title = {Serial-section electron tomography and quantitative analysis of the microtubule organization in 3D-reconstructed mitotic spindles}, volume = {13}, journal = {Bio-protocol}, number = {20}, doi = {10.21769/BioProtoc.4849}, year = {2023}, language = {en} } @article{SchmittTitschackBaum2024, author = {Schmitt, Kira and Titschack, J{\"u}rgen and Baum, Daniel}, title = {CoDA: Interactive Segmentation and Morphological Analysis of Dendroid Structures Exemplified on Stony Cold-Water Corals}, arxiv = {http://arxiv.org/abs/2406.18236}, year = {2024}, abstract = {Dendroid stony corals build highly complex colonies that develop from a single coral polyp sitting in a cup-like skeleton, called corallite, by asexual reproduction, resulting in a tree-like branching pattern of its skeleton. Despite their beauty and ecological importance as reef builders in tropical shallow-water reefs as well as in cold-water coral mounds in the deep ocean, systematic studies investigating the ontogenetic morphological development of such coral colonies are largely missing. One reason for this is the sheer number of corallites - up to several thousands in a single coral colony. Another limiting factor, especially for the analysis of dendroid cold-water corals, is the existence of many secondary joints in the ideally tree-like structure that make a reconstruction of the skeleton tree extremely tedious. Herein, we present CoDA, the Coral Dendroid structure Analyzer, a visual analytics suite that allows for the first time to investigate the ontogenetic morphological development of complex dendroid coral colonies, exemplified on three important framework-forming dendroid cold-water corals: Lophelia pertusa (Linnaeus, 1758), Madrepora oculata (Linnaeus, 1758), and Goniocorella dumosa (Alcock, 1902). Input to CoDA is an initial instance segmentation of the coral polyp cavities (calices), from which it estimates the skeleton tree of the colony and extracts classical morphological measurements and advanced shape features of the individual corallites. CoDA also works as a proofreading and error correction tool by helping to identify wrong parts in the skeleton tree and providing tools to quickly correct these errors. The final skeleton tree enables the derivation of additional information about the calices/corallite instances that otherwise could not be obtained, including their ontogenetic generation and branching patterns - the basis of a fully quantitative statistical analysis of the coral colony morphology. Part of CoDA is CoDA.Graph, a feature-rich link-and-brush user interface for visualizing the extracted features and 2D graph layouts of the skeleton tree, enabling the real-time exploration of complex coral colonies and their building blocks, the individual corallites and branches. In the future, we expect CoDA to greatly facilitate the analysis of large stony corals of different species and morphotypes, as well as other dendroid structures, enabling new insights into the influence of genetic and environmental factors on their ontogenetic morphological development.}, language = {en} } @article{VohraHerreraTavhelidseSucketal.2024, author = {Vohra, Sumit Kumar and Herrera, Kristian and Tavhelidse-Suck, Tinatini and Knoblich, Simon and Seleit, Ali and Boulanger-Weill, Jonathan and Chambule, Sydney and Aspiras, Ariel and Santoriello, Cristina and Randlett, Owen and Wittbrodt, Joachim and Aulehla, Alexander and Lichtman, Jeff W. and Fishman, Mark and Hege, Hans-Christian and Baum, Daniel and Engert, Florian and Isoe, Yasuko}, title = {Multi-species community platform for comparative neuroscience in teleost fish}, journal = {bioRxiv}, doi = {10.1101/2024.02.14.580400}, year = {2024}, abstract = {Studying neural mechanisms in complementary model organisms from different ecological niches in the same animal class can leverage the comparative brain analysis at the cellular level. To advance such a direction, we developed a unified brain atlas platform and specialized tools that allowed us to quantitatively compare neural structures in two teleost larvae, medaka (Oryzias latipes) and zebrafish (Danio rerio). Leveraging this quantitative approach we found that most brain regions are similar but some subpopulations are unique in each species. Specifically, we confirmed the existence of a clear dorsal pallial region in the telencephalon in medaka lacking in zebrafish. Further, our approach allows for extraction of differentially expressed genes in both species, and for quantitative comparison of neural activity at cellular resolution. The web-based and interactive nature of this atlas platform will facilitate the teleost community's research and its easy extensibility will encourage contributions to its continuous expansion.}, language = {en} } @misc{HajarolasvadiBaum2024, author = {Hajarolasvadi, Noushin and Baum, Daniel}, title = {Data for Training the DeepOrientation Model: Simulated cryo-ET tomogram patches}, doi = {10.12752/9686}, year = {2024}, abstract = {A major restriction to applying deep learning methods in cryo-electron tomography is the lack of annotated data. Many large learning-based models cannot be applied to these images due to the lack of adequate experimental ground truth. One appealing alternative solution to the time-consuming and expensive experimental data acquisition and annotation is the generation of simulated cryo-ET images. In this context, we exploit a public cryo-ET simulator called PolNet to generate three datasets of two macromolecular structures, namely the ribosomal complex 4v4r and Thermoplasma acidophilum 20S proteasome, 3j9i. We select these two specific particles to test whether our models work for macromolecular structures with and without rotational symmetry. The three datasets contain 50, 150, and 450 tomograms with a voxel size of 10 ̊A, respectively. Here, we publish patches of size 40 × 40 × 40 extracted from the medium-sized dataset with 26,703 samples of 4v4r and 40,671 samples of 3j9i. The original tomograms from which the samples were extracted are of size 500 × 500 × 250. Finally, it should be noted that the currently published test dataset is employed for reporting the results of our paper titled "DeepOrientation: Deep Orientation Estimation of Macromolecules in Cryo-electron tomography" paper.}, language = {en} } @article{LuetzkendorfMatkovicRachidLiuetal.2025, author = {L{\"u}tzkendorf, Janine and Matkovic-Rachid, Tanja and Liu, Sunbin and G{\"o}tz, Torsten and Gao, Lili and Turrel, Oriane and Maglione, Marta and Grieger, Melanie and Putignano, Sabrina and Ramesh, Niraja and Ghelani, Tina and Neumann, Alexander and Gimber, Niclas and Schmoranzer, Jan and Stawrakakis, Anastasia and Brence, Blaž and Baum, Daniel and Ludwig, Kai and Heine, Martin and Mielke, Thorsten and Liu, Fan and Walter, Alexander and Wahl, Markus and Sigrist, Stephan}, title = {Blobby is a synaptic active zone assembly protein required for memory in Drosophila}, volume = {16}, journal = {Nature Communications}, doi = {10.1038/s41467-024-55382-9}, year = {2025}, language = {en} } @article{YangKnoetelCiecierskaHolmesetal.2024, author = {Yang, Binru and Kn{\"o}tel, David and Ciecierska-Holmes, Jana and W{\"o}lfer, Jan and Chaumel, J{\´u}lia and Zaslansky, Paul and Baum, Daniel and Fratzl, Peter and Dean, Mason N.}, title = {Growth of a tessellation: geometric rules for the development of stingray skeletal patterns}, volume = {11}, journal = {Advanced Science}, number = {48}, doi = {10.1002/advs.202407641}, year = {2024}, language = {en} }