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Figure 13. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013

Figure 13. - Entrance of cave Miljacka II, type locality of Eupolybothrus cavernicolus Komerički & Stoev sp. n.

Figure 18a. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013

Figure 18a. - Prefemur of male leg 15. From Stoev et al. (2010). Figure 18a. Eupolybothrus caesar Figure 18b. Eupolybothrus spiniger Eupolybothrus caesar

Figure 19. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013

Figure 19. - Delineation of Eupolybothrus species – Neighbor joining tree K2P distances. Visualised are the clusters obtained from the reversed Statistical Parsimony (SP) method and the Automatic Barcoding Gap Discovery (ABGD) procedure. Bootstrap support for the identified lineages are given above. The intraspecific genetic variability is given for each cluster. Source data is available in Suppl. material 1.

Figure 20b. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013

Figure 20b. - Gene annotation. Original data available from GigaScience GigaDB (Stoev et al. 2013). Figure 20a. E-value, identity and species distribution statistics of the sequences that can find homologs on Nr database Figure 20b. COG functional classification of the transcripts Figure 20c. GO categories of the transcripts COG functional classification of the transcripts

Figure 17b. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013

Figure 17b. - Prefemur of male leg 15. From Stoev et al. (2010). Figure 17a. Eupolybothrus tabularum Figure 17b. Eupolybothrus excellens Eupolybothrus excellens

FIGURE 5 U in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 5 U-Net implementation. The architecture of the used convolutional neural network (CNN) is an implementation of U-Net. It consists of two parts: two 3×3 convolutions followed by 2×2 max pooling and two 3×3 convolutions followed by 2×2 upconvolutions. Dropout was added to avoid overfitting. As a final step a 1×1 convolution is applied, resulting in an output map with two classes.

FIGURE 6 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 6 Network performance evaluation. High true positive rate (TPR) and low false positive rate (FPR) values for training (blue) and testing data (red) indicate the network's high generalizability.

FIGURE 10 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 10 Application of pipeline for other insect species. The brain textures of various insect species can be very similar to those of ants, facilitating the prediction by the network even without pretraining on specific insect brain scans. (a) Raw image of wasp head (original 1000 × 1000 px) and (b) its prediction without postprocessing (original 520 × 520 px), indicating satisfactory identification of the borders of the brain area. (c) 2D image of praying mantis head (520 × 520 px) and (d) the prediction of its brain area without postprocessing. Even though the network overpredicts some small pixel islands, it excludes from its prediction areas of the muscles, fibers, and cuticle.

FIGURE 1 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 1 Segmentation pipeline overview. (a) Specimens are placed in iodine for staining for 2 weeks and then placed in small vials containing 99% ethanol to prevent them from moving during scanning. (b) The computed tomography (CT) scanner acquires successive X-ray images of the stepwise rotating specimen, and, using a user-defined reference image, automatically reconstructs them to produce orthogonal cross-section stacks that are used for the volume reconstruction of the specimen. (c) Volume rendering for future morphological studies is performed using Amira software. (d) Semiautomated segmentation of the brain volume of each scan (in orange) using the watershed method in Amira. (e) Schematic representation of the U-Net architecture used as the core of the pipeline for the development of a fully automated brain segmentation method. (f) The acquired brain images are used for training after preprocessing augmentation and manual creation of masks. (g) The network's prediction (in yellow) is postprocessed for smoothing out overpredicted areas (in red).

FIGURE 2 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 2 Exemplar images of full-body scans from different ant species. Three-dimensional (3D) reconstructed microcomputed tomography (micro-CT) image of (a) Acromyrmex versicolor and (b) Atta texana worker specimens, using volume rendering in Amira. (c) 2D micro-CT full body image of the Atta texana specimen (original 1000 × 1000 px). The brain area is the area with the most uniform pixel density within the whole body in its stained state, which makes it easy to recognize in most high-quality scans.

FIGURE 3 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 3 Example of semiautomated brain image segmentation. The brain area (in orange) of an Atta texana ant specimen was segmented using the watershed method in Amira; the 1000 × 1000 × 1000 px 3D image was manually postprocessed by smoothing and cropping oversegmented areas.

FIGURE 9 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 9 Prediction of ganglia in the thorax. As the tissue texture in the image is similar to that of the brain, the network accurately predicts other areas of nervous tissue in the organism. The pixel island detection step isolates the brain, but without this step neural tissue can be isolated.

FIGURE 8 3D in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 8 3D volume of ant brain reconstructed from 2D images (original 520 × 520 px) predicted by the algorithm. 3D reconstructed brain prediction of an Atta texana worker.

FIGURE 7 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 7 Pipeline performance demonstrated both for validation (top row) and testing (bottom row) sets. (a, d) Raw images of head of Acromyrmex versicolor and Carebara atoma ant specimens, cropped along the x-y axes. The manually segmented brain areas are indicated in blue. (b, e) Network predictions before postprocessing (in yellow). Areas in yellow dotted circles are pixel islands not connected to the brain area that were overpredicted. (c, f) Predictions after postprocessing (in red). The borders of the predicted areas show good agreement with the manual segmentation in both sets. Note that in overlapping manually and automatically segmented areas in b, c, e, and f, colors appear green or purple.

opencc-by-4.0Nov 2023View details →

Micro-CT image of cell-populated collagen scaffold in the aqueous environment (contrasted with PTA)

A dataset of the collagen scaffold populated with the 3T3 cells scanned in the aqueous environment using Bruker Skyscan 1276 machine (Bruker, Belgium). Scaffold productionThe processes of obtaining and working with collagen scaffolds were conducted in an isolated environment under sterile conditions. The collagen sponge matrix was manufactured at the Center for Collagen Innovation within the Institute of Regenerative Medicine at Sechenov University and provided to us for experimental purposes. In order to obtain the collagen, the authors utilized animal-derived materials sourced from the tendons of large horned cattle. To do this, the tendons were cleaned of excess tissues, cut into pieces with a thickness of 0.5-1 cm, and sequentially treated for 12 hours in a 0.5 M NaCl solution. Subsequently, the mass was homogenized in a 0.83 M acetic acid solution. The resulting suspension was hydrolyzed with 0.24% pepsin for 2 days, after which 1 M NaOH was added to adjust the pH to 7.5, halting the hydrolysis process. The suspension was precipitated with a 12% NaCl solution, the resulting precipitate was redissolved in 0.02 M acetic acid, and then dialyzed. To obtain collagen porous matrices (sponges), the obtained solution was neutralized using 0.1 M NaOH until a pH of 7-7.5 was reached, and the resulting suspension was lyophilized at -40°C for 2 days.Subsequently, the collagen matrix was cut into cubes with sides measuring 0.5 cm. These cubes were placed in 15 ml test tubes filled with 70% ethyl alcohol for sterilization. The test tubes were then placed on a shaker and left in the refrigerator at +4°C for 24 hours. Afterward, the collagen matrices were removed from the alcohol and rinsed five times with 0.9% NaCl.Following the alcohol rinse to confirm the absence of toxicity, an elution test, adapted following the ISO 10993 protocol, was conducted. To obtain collagen cube extracts, they were incubated in a cell culture medium at a volume of 1 ml per sample for 24 hours at 37°C. The 3T3 cell culture was passaged, with 5000 cells seeded in each well of a 96-well plate. After 24 hours, the cells were treated with extract at a volume of 200 µl per well and left in the incubator at 37°C for 24 hours. The following day, extracts were collected, and AlamarBlue reagent (Invitrogen, Waltham, MA, USA) was added according to the manufacturer's instructions to assess the metabolic activity of the cells. Serial dilutions of sodium dodecyl sulfate (SDS) were used as the positive control. Fluorescence intensity was measured using a Victor Nivo spectrofluorimeter (PerkinElmer, Waltham, Massachusetts, USA) at an excitation wavelength of 530 nm and an emission wavelength of 590 nm.Cell seedingAfter confirming the absence of cytotoxic effects, collagen sponges were seeded with the NIH 3T3 cell line at a density of 50,000 cells per sample (cubes of collagen sponge measuring 0.5 cm per side). Staining techniqueFixed specimens in 10% formalin with PBS were washed after 24 hours with distilled water and after that placed in 3% phosphotungstic acid dissolved in distilled water for 24 hours and kept on the rotary shaker at room temperature. After staining, samples were washed and stored in distilled water at 5 °C. Image acquisition and reconstructionA plastic tube filled with distilled water containing the contrasted sample was placed on the sample holder in a SkyScan 1276 micro-CT (Bruker, Kontich, Belgium) and were scanned at 3 μm voxel resolution with 70 kV voltage and 200 uA source power and an aluminum filter with 1 mm of thickness. The rotation was set to 360° around the vertical axis of the sample, with two middle frames for each 0.2° angle step.After scanning, the data were reconstructed using Bruker's NRecon software. During reconstruction, the ring artifact reduction value was set to 20% and the beam hardening correction value to 30%. After that, samples were exported as a series of 16-bit TIFF images which could be opened in the specialized software.

dryad40/100

Micro-CT data from the skeleton of the sea urchin Cidaris rugosa at four different resolutions

The sponge-like biomineralised calcite materials found in echinoderm skeletons are of interest in terms of both structure formation and biological function. Despite their crystalline atomic structure, they exhibit curved interfaces that have been related to known triply-periodic minimal surfaces. Here, we investigate the endoskeleton of the sea urchin Cidaris rugosa that has long been known to form a microstructure related to the Primitive surface. Using X-ray tomography, we find that the endoskeleton is organised as a composite material consisting of domains of bicontinuous microstructures with different structural properties. We describe, for the first time, the co-occurrence of ordered Primitive and Diamond structures and of a disordered structure within a single skeletal plate. We show that these structures can be distinguished by structural properties including solid volume fraction, trabeculae width, and to a lesser extent, interface area and mean curvature. In doing so, we present a robust method that extracts interface areas and curvature integrals from voxelized datasets using the Steiner polynomial for parallel body volumes. We discuss these very large scale bicontinuous structures in the context of their function, formation, and evolution.

opencc-zeroJan 2024View details →

Fig. 19 in 3D X-ray microscopy (Micro-CT) and SEM reveal Zospeum troglobalcanicum Absolon, 1916 and allied species from the Western Balkans (Ellobioidea: Carychiidae)

Fig. 19. Zospeum simplex Inäbnit, Jochum & Neubert, 2021, (SMF 349425) Scanning Electron Microscopy images. A. Protoconch and upper teleoconch showing microstructure of superficial pitting. B. Close up view of pitting microstructure. C. Last whorl with axial ribbing extending beyond peristome lip. D. Close up view of second whorl showing rows of interrupted dashes of radial pitting. E. Growth lines and radial banding on teleoconch. F. Close up view of growth lines and shell microstructure.

Fig. 22 in 3D X-ray microscopy (Micro-CT) and SEM reveal Zospeum troglobalcanicum Absolon, 1916 and allied species from the Western Balkans (Ellobioidea: Carychiidae)

Fig. 22. Sites of localities and collections. A. Site of type locality of Zospeum njunjicae Jochum, Schilthuizen & Ruthensteiner sp. nov., Golubova pećina, Gornja Seoca, Montenegro. Credit: I. Njunjić. B. Collection site within St John's cave, type locality of Z. kolbae Jochum, Inäbnit, Kneubühler & Ruthensteiner sp. nov. and Z. njegusiense Jochum & Ruthensteiner sp. nov., Njeguši, Montenegro (42.4307° N, 18.8115° E) with speleologist, P. Kunisch. Credit: Péter Lenkei. C. Entrance to Golubova pećina (42.2093° N, 19.1306° E), Gornja Seoca, Montenegro. Credit: I. Njunjić.

Fig. 16 in 3D X-ray microscopy (Micro-CT) and SEM reveal Zospeum troglobalcanicum Absolon, 1916 and allied species from the Western Balkans (Ellobioidea: Carychiidae)

Fig. 16. Light microscopic images of full-bodied Zospeum Bourguignat, 1856 from Njeguši, St John's cave. A–B. Zospeum kolbae Jochum, Inäbnit, Kneubühler & Ruthensteiner sp. nov. (NMBE 571122– 571123), individuals assessed by DNA sequencing with sigmoid intestine showing through shells (shells were destroyed post imaging for tissue extraction). A. Holotype (NMBE 571122), shell of aliquot, aperture, and aperture facing left view. B. Paratype (NMBE 571123), shell of aliquot, aperture, and aperture facing left view. C. Undescribed Zospeum sp. 1 (NMBE 577052) showing all perspectives. D–E. Undescribed Zospeum sp. 1 (NMBE 577053/2) showing all perspectives.

Fig. 13 in 3D X-ray microscopy (Micro-CT) and SEM reveal Zospeum troglobalcanicum Absolon, 1916 and allied species from the Western Balkans (Ellobioidea: Carychiidae)

Fig. 13. Light microscopic images of Zospeum constrictum Jochum & Ruthensteiner sp. nov. (NHMW Mol.Coll.Edlauer 16.693) and collection labels.A. Holotype, aperture, and dorsal views. B–D. Paratypes, aperture and dorsal views.