MicroCT 3D reconstruction of three described braconid species (Hymenoptera: Braconidae)

Saqalaksari, Mikaeel Pasandideh; Talebi, Ali Asghar; Kamp, Thomas van de

doi:10.52547/jibs.6.4.331

Cited by 7 publications

(2 citation statements)

References 26 publications

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“…The 3D models reconstructed from CT volumes were painted based on their surface reference color with Vertex-Paint in Blender 2.82 (Figure 2) (Pasandideh Saqalaksari and Talebi, 2018).…”

Section: Methodsmentioning

confidence: 99%

EntomonVR: a New Virtual Reality Game for Learning Insect Morphology

Saqalaksari¹,

Talebi²,

Kamp

et al. 2023

Preprint

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In recent years, the study of insect morphology has benefited greatly from the emergence of new digital imaging and analysis technologies such as X-ray micro-computed tomography (μ-CT), digital 3D reconstruction, and animation. Through interactive gaming and virtual reality, the external morphology of insects can be studied by a broad audience of both entomologists and non-specialists. EntomonVR is a serious game designed to investigate the external morphology of insects with adequate quality for the virtual reality platform. We discuss the advantages of virtual reality, introduce the EntomonVR new educational game, and conclude about future perspectives, validations, and cost-effectiveness. For game assessment, we have tested this game on 25 participants with an entomological background and improved the game based on their feedback. This study demonstrates the efficacy of virtual reality technology for an experimental learning environment in teaching the morphology of insects and the crucial needs for advancing an efficient and interactive educational program.

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“…The 3D models reconstructed from CT volumes were painted based on their surface reference color with Vertex-Paint in Blender 2.82 (Figure 2) (Pasandideh Saqalaksari and Talebi, 2018).…”

Section: Methodsmentioning

confidence: 99%

EntomonVR: a New Virtual Reality Game for Learning Insect Morphology

Saqalaksari¹,

Talebi²,

Kamp

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Recent improvements in resolution and acquisition times 8,9 have broadened its application to a wide range of animals, including non-model species of the smallest sizes, such as insects (e.g. ants 10,11 , wasps 12,13 , beetles 14,15 , bees 16,17 ). This facilitation of image acquisition has posed new demands for accelerated image analysis methods.…”

Section: Introductionmentioning

confidence: 99%

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

Lösel

Monchanin

Lebrun

et al. 2022

Preprint

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Analysing large numbers of brain samples is challenging but can reveal minor but statistically and biologically relevant variations, that provide important insights into animal behaviour, ecology and evolution. Here, we used micro-CT imaging and deep learning to perform automatic analyses of 187 bee brains (honey bees and bumblebees). This large-scale quantitative comparative analysis of 3D brain data revealed strong inter-individual variations in overall bee brain size that is consistent across colonies and species, suggesting selection processes supporting behavioural variability. In addition, analyses of bumblebee brains revealed a significant level of lateralization in the optic lobes, likely related to reported variations in visual learning. Our fast and accurate deep learning-based approach to process bee brain data is user friendly and holds considerable promises for conducting large-scale quantitative neuroanatomical comparisons across a wide range of species beyond insects. Ultimately, this will help address fundamental unsolved questions related to the evolution of animal brains and cognition.

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