Natural variability in bee brain size and symmetry revealed by micro-CT imaging and deep learning

Turbinal bones are still one of the last frontiers of the tetrapod skull: hypotheses, challenges and perspectives

Turbinals are bony or cartilaginous structures that are present in the nasal cavity of most tetrapods. They are involved in key functions such as olfaction, heat, and moisture conservation, as well as protection of the respiratory tract. Despite recent studies that challenged long-standing hypotheses about their physiological and genomic correlation, turbinals remain largely unexplored, particularly for non-mammalian species. Herein, we review and synthesise the current knowledge of turbinals using an integrative approach that includes comparative anatomy, physiology, histology and genomics. In addition, we provide synonyms and correspondences of tetrapod turbinals from about 80 publications. This work represents a first step towards drawing hypotheses of homology for the whole clade, and provides a strong basis to develop new research avenues.

First fossil species of family Hyidae (Arachnida: Pseudoscorpiones) confirms 99 million years of ecological stasis in a Gondwanan lineage

Burmese amber preserves a diverse assemblage of Cretaceous arachnids, and among pseudoscorpions (Arachnida: Pseudoscorpiones), ten species in five families have already been named. Here, we describe a new fossil species from Burmese amber in the pseudoscorpion family Hyidae, providing detailed measurements, photographs and 3D-models from synchrotron scanning. Based on morphology, the new fossil, Hya fynni sp. nov. is placed in the genus Hya, and is nearly identical to extant species in the genus, except for the position of trichobothrium est on the pedipalpal chela, thereby indicating extreme morphological stasis in this invertebrate lineage over the last 99 million years. Hya fynni represents the first described fossil species in Hyidae, and the third described Burmese fossil in the superfamily Neobisioidea. It also joins the garypinid, Amblyolpium burmiticum, in representing the oldest fossil records for extant pseudoscorpion genera. Considering proposed divergence dates, the newly described fossil species bolsters a Gondwanan origin for Hyidae, and provides evidence for the "Late Jurassic Rifting" hypothesis for the Burma Terrane, in which this landmass rifted from Gondwana in the Late Jurassic and collided with Eurasia by the Cretaceous/Eocene. Like Hya species today, H. fynni likely inhabited humicolous microhabitats in tropical forests on the Burma Terrane, supporting ecological niche stasis for this family since the Mesozoic.

Automated 3D cytoplasm segmentation in soft X-ray tomography

Cells' structure is key to understanding cellular function, diagnostics, and therapy development. Soft X-ray tomography (SXT) is a unique tool to image cellular structure without fixation or labeling at high spatial resolution and throughput. Fast acquisition times increase demand for accelerated image analysis, like segmentation. Currently, segmenting cellular structures is done manually and is a major bottleneck in the SXT data analysis. This paper introduces ACSeg, an automated 3D cytoplasm segmentation model. ACSeg is generated using semi-automated labels and 3D U-Net and is trained on 43 SXT tomograms of immune T cells, rapidly converging to high-accuracy segmentation, therefore reducing time and labor. Furthermore, adding only 6 SXT tomograms of other cell types diversifies the model, showing potential for optimal experimental design. ACSeg successfully segmented unseen tomograms and is published on Biomedisa, enabling high-throughput analysis of cell volume and structure of cytoplasm in diverse cell types.

How many specimens make a sufficient training set for automated three-dimensional feature extraction?

Deep learning has emerged as a robust tool for automating feature extraction from three-dimensional images, offering an efficient alternative to labour-intensive and potentially biased manual image segmentation methods. However, there has been limited exploration into the optimal training set sizes, including assessing whether artficial expansion by data augmentation can achieve consistent results in less time and how consistent these benefits are across different types of traits. In this study, we manually segmented 50 planktonic foraminifera specimens from the genus Menardella to determine the minimum number of training images required to produce accurate volumetric and shape data from internal and external structures. The results reveal unsurprisingly that deep learning models improve with a larger number of training images with eight specimens being required to achieve 95% accuracy. Furthermore, data augmentation can enhance network accuracy by up to 8.0%. Notably, predicting both volumetric and shape measurements for the internal structure poses a greater challenge compared with the external structure, owing to low contrast differences between different materials and increased geometric complexity. These results provide novel insight into optimal training set sizes for precise image segmentation of diverse traits and highlight the potential of data augmentation for enhancing multivariate feature extraction from three-dimensional images.

Environmental exposure to metallic pollution impairs honey bee brain development and cognition

Laboratory studies show detrimental effects of metallic pollutants on invertebrate behaviour and cognition, even at low levels. Here we report a field study on Western honey bees exposed to metal and metalloid pollution through dusts, food and water at a historic mining site. We analysed more than 1000 bees from five apiaries along a gradient of contamination within 11 km of a former gold mine in Southern France. Bees collected close to the mine exhibited olfactory learning performances lower by 36% and heads smaller by 4%. Three-dimensional scans of bee brains showed that the olfactory centres of insects sampled close to the mine were also 4% smaller, indicating neurodevelopmental issues. Our study raises serious concerns about the health of honey bee populations in areas polluted with potentially harmful elements, particularly with arsenic, and illustrates how standard cognitive tests can be used for risk assessment.

Neuroplasticity in honey bee brains: An enhanced micro-computed tomography protocol for precise mushroom body volume measurement

BACKGROUND

In insect brains, mushroom bodies are associated with memory and learning behavior. It has been demonstrated that the volume of the mushroom bodies in the brain of a worker honey bee changes during the adult stage. Changes in mushroom body volume imply high neuroplasticity in the brains and may be related to the age polyethism of honey bees. A suitable volume measurement method is needed to understand the correlation between behavioral changes and mushroom body volume changes in honey bees.

NEW METHOD

We developed a new protocol for insect micro-computed tomography by modifying a previously reported method. Permount™ mounting medium was used as the embedding medium for micro-computed tomography scanning.

RESULTS

This protocol can generate images with high contrast inside the brain and reduce the marked shape changes during specimen processing. From the resulting high-contrast images, we used freeware to generate a three-dimensional model and calculate the volumes of the mushroom bodies in honey bees. The measured volumes of the mushroom bodies were larger than the values reported in most previous studies. There was no significant difference between the left and right mushroom body volumes, but the volumes of honey bee mushroom bodies significantly increased with age.

COMPARISON WITH EXISTING METHODS

Previous protocols for micro-computed tomography using dried samples would cause brain shrinkage; protocols using ethanol-preserved or resin-embedded samples generated images with lower contrast.

CONCLUSIONS

The embedding protocol for micro-computed tomography is suitable for calculating volume of the mushroom bodies in honey bee brains.