1
|
Martinez Q, Amson E, Ruf I, Smith TD, Pirot N, Broyon M, Lebrun R, Captier G, Gascó Martín C, Ferreira G, Fabre PH. Turbinal bones are still one of the last frontiers of the tetrapod skull: hypotheses, challenges and perspectives. Biol Rev Camb Philos Soc 2024. [PMID: 39092480 DOI: 10.1111/brv.13122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/03/2024] [Accepted: 07/10/2024] [Indexed: 08/04/2024]
Abstract
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.
Collapse
Affiliation(s)
- Quentin Martinez
- Institut des Sciences de l'Évolution (ISEM, UMR 5554 CNRS-IRD-UM), Université de Montpellier, Place E. Bataillon-CC 064 - 34095, Montpellier Cedex 5, France
- Staatliches Museum für Naturkunde Stuttgart, Stuttgart, DE-70191, Germany
| | - Eli Amson
- Staatliches Museum für Naturkunde Stuttgart, Stuttgart, DE-70191, Germany
| | - Irina Ruf
- Abteilung Messelforschung und Mammalogie, Senckenberg Forschungsinstitut und Naturmuseum Frankfurt, Frankfurt am Main, 60325, Germany
- Institut für Geowissenschaften, Goethe-Universität Frankfurt am Main, Frankfurt am Main, 60438, Germany
- Research Center of Paleontology and Stratigraphy, Jilin University, Changchun, 130026, China
| | - Timothy D Smith
- School of Physical Therapy, Slippery Rock University, Slippery Rock, PA, 16057, USA
| | - Nelly Pirot
- BioCampus Montpellier (BCM), Université de Montpellier, CNRS, INSERM, Montpellier, 34090, France
- Institut de Recherche en Cancérologie de Montpellier (IRCM), Université de Montpellier, Institut du Cancer de Montpellier (ICM), INSERM, Montpellier, 34298, France
| | - Morgane Broyon
- BioCampus Montpellier (BCM), Université de Montpellier, CNRS, INSERM, Montpellier, 34090, France
- Institut de Recherche en Cancérologie de Montpellier (IRCM), Université de Montpellier, Institut du Cancer de Montpellier (ICM), INSERM, Montpellier, 34298, France
| | - Renaud Lebrun
- Institut des Sciences de l'Évolution (ISEM, UMR 5554 CNRS-IRD-UM), Université de Montpellier, Place E. Bataillon-CC 064 - 34095, Montpellier Cedex 5, France
| | - Guillaume Captier
- Laboratoire d'anatomie, UFR médecine, Université Montpellier, Montpellier, 34060, France
- Département chirurgie pédiatrique, CHU Montpellier, université Montpellier, Montpellier, 34295, France
| | | | - Gabriel Ferreira
- Senckenberg Centre for Human Evolution and Palaeoenvironment at the Eberhard Karls University of Tübingen, Tübingen, 727074, Germany
- Department of Geosciences, Faculty of Sciences, Eberhard Karls University of Tübingen, Tübingen, 727074, Germany
| | - Pierre-Henri Fabre
- Institut des Sciences de l'Évolution (ISEM, UMR 5554 CNRS-IRD-UM), Université de Montpellier, Place E. Bataillon-CC 064 - 34095, Montpellier Cedex 5, France
- Mammal Section, Department of Life Sciences, The Natural History Museum, London, SW7 5DB, UK
- Institut Universitaire de France (IUF), Paris, 75231, France
- Division of Vertebrate Zoology (Mammalogy), American Museum of Natural History, Central Park West, 79th St, New York, NY, 10024-5192, USA
| |
Collapse
|
2
|
Röschmann LM, Harvey MS, Hou Y, Harms D, Kotthoff U, Hammel JU, Ren D, Loria SF. First fossil species of family Hyidae (Arachnida: Pseudoscorpiones) confirms 99 million years of ecological stasis in a Gondwanan lineage. PeerJ 2024; 12:e17515. [PMID: 38948233 PMCID: PMC11214426 DOI: 10.7717/peerj.17515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/13/2024] [Indexed: 07/02/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Liza M. Röschmann
- Department of Geology, Universität Hamburg, Hamburg, Germany
- Section Arachnology, Centre for Taxonomy and Morphology, Museum of Nature Hamburg—Zoology, Leibniz Institute for the Analysis of Biodiversity Change, Hamburg, Germany
| | - Mark S. Harvey
- Collections & Research, Western Australian Museum, Welshpool, Australia
| | - Yanmeng Hou
- College of Life Sciences, Capital Normal University, Beijing, China
| | - Danilo Harms
- Section Arachnology, Centre for Taxonomy and Morphology, Museum of Nature Hamburg—Zoology, Leibniz Institute for the Analysis of Biodiversity Change, Hamburg, Germany
| | - Ulrich Kotthoff
- Centre for Biomonitoring and Conservation Science, Museum of Nature Hamburg—Geology, Leibniz Institute for the Analysis of Biodiversity Change, Hamburg, Germany
| | - Jörg U. Hammel
- Institute of Materials Physics, Helmholtz-Zentrum Hereon, Geesthacht, Germany
| | - Dong Ren
- College of Life Sciences, Capital Normal University, Beijing, China
| | - Stephanie F. Loria
- Section Arachnology, Centre for Taxonomy and Morphology, Museum of Nature Hamburg—Zoology, Leibniz Institute for the Analysis of Biodiversity Change, Hamburg, Germany
| |
Collapse
|
3
|
Erozan A, Lösel PD, Heuveline V, Weinhardt V. Automated 3D cytoplasm segmentation in soft X-ray tomography. iScience 2024; 27:109856. [PMID: 38784019 PMCID: PMC11112332 DOI: 10.1016/j.isci.2024.109856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 03/22/2024] [Accepted: 04/27/2024] [Indexed: 05/25/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Ayse Erozan
- Centre for Organismal Studies, Heidelberg University, Heidelberg, Germany
- Engineering Mathematics and Computing Lab, Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany
- Data Mining and Uncertainty Quantification, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | - Philipp D. Lösel
- Engineering Mathematics and Computing Lab, Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany
- Data Mining and Uncertainty Quantification, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
- Department of Materials Physics Research School of Physics, The Australian National University, Acton ACT, Australia
| | - Vincent Heuveline
- Engineering Mathematics and Computing Lab, Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany
- Data Mining and Uncertainty Quantification, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | - Venera Weinhardt
- Centre for Organismal Studies, Heidelberg University, Heidelberg, Germany
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| |
Collapse
|
4
|
Mulqueeney JM, Searle-Barnes A, Brombacher A, Sweeney M, Goswami A, Ezard THG. How many specimens make a sufficient training set for automated three-dimensional feature extraction? ROYAL SOCIETY OPEN SCIENCE 2024; 11:rsos.240113. [PMID: 39100182 PMCID: PMC11296157 DOI: 10.1098/rsos.240113] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 08/06/2024]
Abstract
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.
Collapse
Affiliation(s)
- James M. Mulqueeney
- School of Ocean & Earth Science, National Oceanography Centre Southampton, University of Southampton Waterfront Campus, Southampton, UK
- Department of Life Sciences, Natural History Museum, London, UK
| | - Alex Searle-Barnes
- School of Ocean & Earth Science, National Oceanography Centre Southampton, University of Southampton Waterfront Campus, Southampton, UK
| | - Anieke Brombacher
- School of Ocean & Earth Science, National Oceanography Centre Southampton, University of Southampton Waterfront Campus, Southampton, UK
| | - Marisa Sweeney
- School of Ocean & Earth Science, National Oceanography Centre Southampton, University of Southampton Waterfront Campus, Southampton, UK
| | - Anjali Goswami
- Department of Life Sciences, Natural History Museum, London, UK
| | - Thomas H. G. Ezard
- School of Ocean & Earth Science, National Oceanography Centre Southampton, University of Southampton Waterfront Campus, Southampton, UK
| |
Collapse
|
5
|
Monchanin C, Drujont E, Le Roux G, Lösel PD, Barron AB, Devaud JM, Elger A, Lihoreau M. Environmental exposure to metallic pollution impairs honey bee brain development and cognition. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133218. [PMID: 38113738 DOI: 10.1016/j.jhazmat.2023.133218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 12/03/2023] [Accepted: 12/08/2023] [Indexed: 12/21/2023]
Abstract
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.
Collapse
Affiliation(s)
- Coline Monchanin
- CNRS, University Paul Sabatier, Toulouse III, France; Research Center on Animal Cognition (CRCA), Center for Integrative Biology (CBI), University Paul Sabatier, Toulouse III, France; Department of Biological Sciences, Macquarie University, NSW, Australia
| | - Erwann Drujont
- CNRS, University Paul Sabatier, Toulouse III, France; Research Center on Animal Cognition (CRCA), Center for Integrative Biology (CBI), University Paul Sabatier, Toulouse III, France
| | - Gaël Le Roux
- Laboratoire Ecologie Fonctionnelle et Environnement, Université de Toulouse, CNRS, INPT, UPS, Toulouse, France
| | - Philipp D Lösel
- Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Germany; Department of Materials Physics, Research School of Physics, The Australian National University, ACT, Australia
| | - Andrew B Barron
- Department of Biological Sciences, Macquarie University, NSW, Australia
| | - Jean-Marc Devaud
- CNRS, University Paul Sabatier, Toulouse III, France; Research Center on Animal Cognition (CRCA), Center for Integrative Biology (CBI), University Paul Sabatier, Toulouse III, France
| | - Arnaud Elger
- Laboratoire Ecologie Fonctionnelle et Environnement, Université de Toulouse, CNRS, INPT, UPS, Toulouse, France
| | - Mathieu Lihoreau
- CNRS, University Paul Sabatier, Toulouse III, France; Research Center on Animal Cognition (CRCA), Center for Integrative Biology (CBI), University Paul Sabatier, Toulouse III, France.
| |
Collapse
|
6
|
Fu SJ, Yang EC. Neuroplasticity in honey bee brains: An enhanced micro-computed tomography protocol for precise mushroom body volume measurement. J Neurosci Methods 2024; 403:110040. [PMID: 38135123 DOI: 10.1016/j.jneumeth.2023.110040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/31/2023] [Accepted: 12/17/2023] [Indexed: 12/24/2023]
Abstract
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.
Collapse
Affiliation(s)
- Shang-Jui Fu
- Department of Entomology, National Taiwan University, Taiwan
| | - En-Cheng Yang
- Department of Entomology, National Taiwan University, Taiwan.
| |
Collapse
|