A Local Iterative Approach for the Extraction of 2D Manifolds from Strongly Curved and Folded Thin-Layer Structures.

Ridge surfaces represent important features for the analysis of 3-dimensional (3D) datasets in diverse applications and are often derived from varying underlying data including flow fields, geological fault data, and point data, but they can also be present in the original scalar images acquired using a plethora of imaging techniques. Our work is motivated by the analysis of image data acquired using micro-computed tomography ([Formula: see text]) of ancient, rolled and folded thin-layer structures such as papyrus, parchment, and paper as well as silver and lead sheets. From these documents we know that they are 2-dimensional (2D) in nature. Hence, we are particularly interested in reconstructing 2D manifolds that approximate the document's structure. The image data from which we want to reconstruct the 2D manifolds are often very noisy and represent folded, densely-layered structures with many artifacts, such as ruptures or layer splitting and merging. Previous ridge-surface extraction methods fail to extract the desired 2D manifold for such challenging data. We have therefore developed a novel method to extract 2D manifolds. The proposed method uses a local fast marching scheme in combination with a separation of the region covered by fast marching into two sub-regions. The 2D manifold of interest is then extracted as the surface separating the two sub-regions. The local scheme can be applied for both automatic propagation as well as interactive analysis. We demonstrate the applicability and robustness of our method on both artificial data as well as real-world data including folded silver and papyrus sheets.

Serial-section Electron Tomography and Quantitative Analysis of Microtubule Organization in 3D-reconstructed Mitotic Spindles.

For the analysis of cellular architecture during mitosis, nanometer resolution is needed to visualize the organization of microtubules in spindles. Here, we present a detailed protocol that can be used to produce 3D reconstructions of whole mitotic spindles in cells grown in culture. For this, we attach mammalian cells enriched in mitotic stages to sapphire discs. Our protocol further involves cryo-immobilization by high-pressure freezing, freeze-substitution, and resin embedding. We then use fluorescence light microscopy to stage select mitotic cells in the resin-embedded samples. This is followed by large-scale electron tomography to reconstruct the selected and staged mitotic spindles in 3D. The generated and stitched electron tomograms are then used to semi-automatically segment the microtubules for subsequent quantitative analysis of spindle organization. Thus, by providing a detailed correlative light and electron microscopy (CLEM) approach, we give cell biologists a toolset to streamline the 3D visualization and analysis of spindle microtubules (http://kiewisz.shinyapps.io/asga). In addition, we refer to a recently launched platform that allows for an interactive display of the 3D-reconstructed mitotic spindles (https://cfci.shinyapps.io/ASGA_3DViewer/). Key features • High-throughput screening of mitotic cells by correlative light and electron microscopy (CLEM). • Serial-section electron tomography of selected cells. • Visualization of mitotic spindles in 3D and quantitative analysis of microtubule organization.

Dense reconstruction of elephant trunk musculature.

The elephant trunk operates as a muscular hydrostat1,2 and is actuated by the most complex musculature known in animals.3,4 Because the number of trunk muscles is unclear,5 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 muscles6 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);7,8,9 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 ventrally10 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.

Simulation-based inference for efficient identification of generative models in computational connectomics.

Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neuronal networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters, and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a fixed wiring rule to fit the empirical data, SBI considers many parametrizations of a rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rule parameters and relies on machine learning methods to estimate a probability distribution (the 'posterior distribution over parameters conditioned on the data') that characterizes all data-compatible parameters. We demonstrate how to apply SBI in computational connectomics by inferring the parameters of wiring rules in an in silico model of the rat barrel cortex, given in vivo connectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data.

Evolutionary traces of miniaturization in a giant-Comparative anatomy of brain and brain nerves in Bathochordaeus stygius (Tunicata, Appendicularia).

Appendicularia comprises 70 marine, invertebrate, chordate species. Appendicularians play important ecological and evolutionary roles, yet their morphological disparity remains understudied. Most appendicularians are small, develop rapidly, and with a stereotyped cell lineage, leading to the hypothesis that Appendicularia derived progenetically from an ascidian-like ancestor. Here, we describe the detailed anatomy of the central nervous system of Bathochordaeus stygius, a giant appendicularian from the mesopelagic. We show that the brain consists of a forebrain with on average smaller and more uniform cells and a hindbrain, in which cell shapes and sizes vary to a greater extent. Cell count for the brain was 102. We demonstrate the presence of three paired brain nerves. Brain nerve 1 traces into the epidermis of the upper lip region and consists of several fibers with some supportive bulb cells in its course. Brain nerve 2 innervates oral sensory organs and brain nerve 3 innervates the ciliary ring of the gill slits and lateral epidermis. Brain nerve 3 is asymmetric, with the right nerve consisting of two neurites originating posterior to the left one that contains three neurites. Similarities and differences to the anatomy of the brain of the model species Oikopleura dioica are discussed. We interpret the small number of cells in the brain of B. stygius as an evolutionary trace of miniaturization and conclude that giant appendicularians evolved from a small, progenetic ancestor that secondarily increased in size within Appendicularia.

A Visual Interface for Exploring Hypotheses about Neural Circuits.

One of the fundamental problems in neurobiological research is to understand how neural circuits generate behaviors in response to sensory stimuli. Elucidating such neural circuits requires anatomical and functional information about the neurons that are active during the processing of the sensory information and generation of the respective response, as well as an identification of the connections between these neurons. With modern imaging techniques, both morphological properties of individual neurons as well as functional information related to sensory processing, information integration and behavior can be obtained. Given the resulting information, neurobiologists are faced with the task of identifying the anatomical structures down to individual neurons that are linked to the studied behavior and the processing of the respective sensory stimuli. Here, we present a novel interactive tool that assists neurobiologists in the aforementioned task by allowing them to extract hypothetical neural circuits constrained by anatomical and functional data. Our approach is based on two types of structural data: brain regions that are anatomically or functionally defined, and morphologies of individual neurons. Both types of structural data are interlinked and augmented with additional information. The presented tool allows the expert user to identify neurons using Boolean queries. The interactive formulation of these queries is supported by linked views, using, among other things, two novel 2D abstractions of neural circuits. The approach was validated in two case studies investigating the neural basis of vision-based behavioral responses in zebrafish larvae. Despite this particular application, we believe that the presented tool will be of general interest for exploring hypotheses about neural circuits in other species, genera and taxa.

High-throughput segmentation, data visualization, and analysis of sea star skeletal networks.

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, 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.

MCRS1 modulates the heterogeneity of microtubule minus-end morphologies in mitotic spindles.

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 but their minus-end structure remains poorly characterized. Here, we used large-scale electron tomography to study the morphology of the MT minus ends in three dimensionally 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 groundwork for further studies on the complexity of MT dynamics regulation.

Active entanglement enables stochastic, topological grasping.

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 options for soft, adaptable grasping.

Volumetric macromolecule identification in cryo-electron tomograms using capsule networks.

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 macromolecule 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 [Formula: see text]score mostly above 60% and 70%, respectively, on the test data. In both network architectures, we observed degradation of at least 40% in the [Formula: see text]-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 [Formula: see text]-score of 91% on the test data in contrast to 64% of the 3D-UNet. The better [Formula: see text]-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 [Formula: see text]-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 macromolecules 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.

Three-dimensional structure of kinetochore-fibers in human mitotic spindles.

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.

Hide and seek shark teeth in Random Forests: machine learning applied to Scyliorhinus canicula populations.

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 populations differ between the northeastern Atlantic Ocean and the Mediterranean Sea based on biological features and genetic analysis. In this study, we sample more than 3,000 teeth from 56 S. canicula specimens caught incidentally off Roscoff and Banyuls-sur-Mer. We investigate population differences based on tooth shape and form by 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.

Ontogeny of a tessellated surface: Carapace growth of the longhorn cowfish Lactoria cornuta.

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 composed 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.

Large abdominal mechanoreceptive sense organs in small plant-dwelling insects.

The Hemiptera, with approximately 98 000 species, is one of the largest insect orders. Most species feed by sucking sap from plant tissues and are thus often vectors for economically important phytopathogens. Well known within this group are the large cicadas (Cicadomorpha: Cicadoidea: Cicadidae) because they produce extremely loud airborne sounds. Less well known are their mostly tiny relatives, the leafhoppers, spittlebugs, treehoppers and planthoppers that communicate by silent vibrational signals. While the generation of these signals has been extensively investigated, the mechanisms of their perception are poorly understood. This study provides a complete description and three-dimensional reconstruction of a large and complex array of mechanoreceptors in the first abdominal segments of the Rhododendron leafhopper Graphocephala fennahi (Cicadomorpha: Membracoidea: Cicadellidae). Further, we identify homologous organs in the spittlebug Philaenus spumarius (Cicadomorpha: Cercopoidea: Aphrophoridae) and the planthopper Issus coleoptratus (Fulgoromorpha: Fulgoroidea: Issidae). Such large abdominal sensory arrays have not been found in any other insect orders studied so far. This indicates that these sense organs, together with the signal-producing 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.

Semi-automatic stitching of filamentous structures in image stacks from serial-section electron tomography.

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 labour. 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 visualisation of the full image stack is hampered by the size of the data (up to 100 GB). Here, we present a specialised, 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 visualisation and interaction tools for each processing step to guarantee real-time response, and an optimised 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. LAY DESCRIPTION: Electron tomography of biological samples is used for a three-dimensional (3D) reconstruction of filamentous structures, such as microtubules (MTs) in mitotic and meiotic spindles. Large-scale electron tomography can be applied to increase the reconstructed volume for the visualisation of full spindles. For this, each spindle is cut into a series of semi-thick physical sections, from 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. Previously, we presented fully automatic approaches for this 3D reconstruction pipeline. However, large volumes often suffer from imperfections (ie physical distortions) caused by the image acquisition process, making it difficult to apply fully automatic approaches for matching and stitching of numerous tomograms. Therefore, we developed an interactive, semi-automatic solution that considers all requirements for large-scale stitching of microtubules in image stacks of consecutive sections. We achieved this by combining automatic methods, visual validation and interactive error correction, thus allowing the user to continuously improve the result by interactively correcting landmarks or matches of filaments. We present large-scale reconstructions of spindles in which the automatic workflow failed and where different steps of manual corrections were needed. Our approach is also applicable to other biological samples showing 3D distributions of MTs in a number of different cellular contexts.

Automatic detection of prosodic boundaries in spontaneous speech.

Automatic speech recognition (ASR) and natural language processing (NLP) are expected to benefit from an effective, simple, and reliable method to automatically parse conversational speech. The ability to parse conversational speech depends crucially on the ability to identify boundaries between prosodic phrases. This is done naturally by the human ear, yet has proved surprisingly difficult to achieve reliably and simply in an automatic manner. Efforts to date have focused on detecting phrase boundaries using a variety of linguistic and acoustic cues. We propose a method which does not require model training and utilizes two prosodic cues that are based on ASR output. Boundaries are identified using discontinuities in speech rate (pre-boundary lengthening and phrase-initial acceleration) and silent pauses. The resulting phrases preserve syntactic validity, exhibit pitch reset, and compare well with manual tagging of prosodic boundaries. Collectively, our findings support the notion of prosodic phrases that represent coherent patterns across textual and acoustic parameters.

Scattering and phase-contrast X-ray methods reveal damage to glass fibers in endodontic posts following dental bur trimming.

OBJECTIVES: There is concern that the integrity of fiberglass dental posts may be affected by chairside trimming during treatment. We hypothesize that hard X-ray methods of phase contrast-enhanced micro-CT (PCE-CT) and synchrotron based X-ray refraction (SXRR) can reliably identify and help characterize the extent of damage. METHODS: Fiberglass posts were imaged both as manufactured and following trimming with a diamond bur. Each of the posts was imaged by SXRR and by PCE-CT. Datasets from PCE-CT were used to visualize and quantify 2D and 3D morphological characteristics of intact and of damage-affected regions caused by trimming. RESULTS: The SXRR images revealed fiber inhomogeneities from manufacturing with a significant increase in internal surfaces in sample regions corresponding to damage from trimming. PCE-CT volumes unveiled the micromorphology of single fibers in the posts and some damage in the trimmed area (e.g. fractures, splinters and cracks). Area, perimeter, circularity, roundness, volume and thickness of the glass fibers in the trimmed area were statistically different from the control (p < 0.01). SIGNIFICANCE: The integrity of single fibers in the post is critical for bending resistance and for long-term adhesion to the cement in the root canals. Damage to the fibers causes substantial structural weakening across the post diameter. Glass fragments produced due to contact with the dental bur may separate from the post and may significantly reduce bond capacity. The above mentioned synchrotron-based imaging techniques can further facilitate assessment of the structural integrity and the appearance of defects in posts (e.g. after mechanical load).

Image analysis pipeline for segmentation of a biological porosity network, the lacuno-canalicular system in stingray tesserae.

A prerequisite for many analysis tasks in modern comparative biology is the segmentation of 3-dimensional (3D) images of the specimens being investigated (e.g. from microCT data). Depending on the specific imaging technique that was used to acquire the images and on the image resolution, different segmentation tools are required. While some standard tools exist that can often be applied for specific subtasks, building whole processing pipelines solely from standard tools is often difficult. Some tasks may even necessitate the implementation of manual interaction tools to achieve a quality that is sufficient for subsequent analysis. In this work, we present a pipeline of segmentation tools that can be used for the semiautomatic segmentation and quantitative analysis of voids in tissue (i.e. internal structural porosity). We use this pipeline to analyze lacuno-canalicular networks in stingray tesserae from 3D images acquired with synchrotron microCT.•The first step of this pipeline, the segmentation of the tesserae, was performed using standard marker-based watershed segmentation.•The efficient processing of the next two steps, that is, the segmentation of all lacunae spaces belonging to a specific tessera and the separation of these spaces into individual lacunae required recently developed, novel tools.•For error correction, we developed an interactive method that allowed us to quickly split lacunae that were accidentally merged, and to merge lacunae that were wrongly split.•Finally, the tesserae and their corresponding lacunae were subdivided into structural wedges (i.e. specific anatomical regions) using a semi-manual approach. With this processing pipeline, analysis of a variety of interconnected structural networks (e.g. vascular or lacuno-canalicular networks) can be achieved in a comparatively high-throughput fashion. In our study system, we were able to efficiently segment more than 12,000 lacunae in high-resolution scans of nine tesserae, providing a robust data set for statistical analysis.

Exposure to Odors Increases Pain Threshold in Chronic Low Back Pain Patients.

OBJECTIVES: Structured exposure to odors is an acknowledged therapy in patients with smell loss but has also been shown to be effective in depression. The latter might rely on connections between olfactory and emotional structures, suggesting possible effects of a similar approach in pain patients. Based on neuroanatomy, there are several interfaces between the "pain matrix" and olfactory system, such as the limbic system, hypothalamus, and mediodorsal thalamus. We aimed to investigate whether structured exposure to odors may impact perceived pain in patients with chronic low back pain. DESIGN: Randomized controlled parallel-group design. Subjects were tested on two occasions, at baseline and after four weeks. SETTING: Ambulatory. SUBJECTS: Forty-two patients with chronic low back pain. METHODS: For all patients, olfactory function (using the "Sniffin'Sticks" test kit), detection, and pain thresholds for cutaneous electrical stimuli (applied to the forearm) were tested at baseline and after four weeks. Twenty-eight patients exposed themselves to four odors (rose, vanilla, chocolate, peach) every two hours over a period of four weeks (training group). Control patients (N = 14) underwent no such "olfactory training" (nontraining group). RESULTS: Pain thresholds were significantly increased in patients who performed olfactory training compared with patients who did not train with odors. Detection thresholds and olfactory function remained unchanged. CONCLUSIONS: The present results indicate that regular exposure to odors increases pain thresholds in patients with chronic back pain and could be useful for general pain control in these patients. Furthermore, olfactory training in chronic pain patients might help to reduce chronification of pain by desensitization.

4D imaging of lithium-batteries using correlative neutron and X-ray tomography with a virtual unrolling technique.

The temporally and spatially resolved tracking of lithium intercalation and electrode degradation processes are crucial for detecting and understanding performance losses during the operation of lithium-batteries. Here, high-throughput X-ray computed tomography has enabled the identification of mechanical degradation processes in a commercial Li/MnO2 primary battery and the indirect tracking of lithium diffusion; furthermore, complementary neutron computed tomography has identified the direct lithium diffusion process and the electrode wetting by the electrolyte. Virtual electrode unrolling techniques provide a deeper view inside the electrode layers and are used to detect minor fluctuations which are difficult to observe using conventional three dimensional rendering tools. Moreover, the 'unrolling' provides a platform for correlating multi-modal image data which is expected to find wider application in battery science and engineering to study diverse effects e.g. electrode degradation or lithium diffusion blocking during battery cycling.