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  • 1
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    Diatoms favor their younger daughters (2014)
    Association for the Sciences of Limnology and Oceanography
    Details
    Publication Date: 2022-05-26
    Description: Author Posting. © Association for the Sciences of Limnology and Oceanography, 2012. This article is posted here by permission of Association for the Sciences of Limnology and Oceanography for personal use, not for redistribution. The definitive version was published in Limnology and Oceanography 57 (2012): 1572-1578, doi:10.4319/lo.2012.57.5.1572.
    Description: We used a time-lapse imaging approach to examine cell division in the marine centric diatom Ditylum brightwellii and observed that daughter cells who inherited their parents' hypothecal frustule half were more likely to divide before their sisters. This is consistent with observations in Escherichia coli of a bias between sister cells, where faster growth in one sister is thought to arise from its inheriting parental material with less oxidative damage. We also observed that hypothecal sisters in D. brightwellii were more likely to inherit a greater proportion of their parents' cellular material, similar to what has been seen in E. coli. We found a statistically significant correlation between the amount of parental material inherited by a hypothecal daughter and its relative division rate, indicating that this extra material inherited by the hypothecal daughter plays a role in its more rapid division. Furthermore, the intercept in this regression was greater than zero, indicating that other factors, such as differences in the quality of inherited material, also play a role. This similarity between two taxonomically distant microbes suggests that favoritism toward one daughter might occur broadly among unicellular organisms that reproduce asexually by binary fission. Such a bias in cell division might be advantageous, given model predictions that show that favoring one daughter at the expense of the other can result in higher population growth rates, increasing the chance that a cell's genotype will survive compared to a model where the daughters divide at equal rates.
    Description: This research was funded in part by the Woods Hole Oceanographic Institution through an Ocean Life Institute Postdoctoral Scholarship to S.R.L. and by support to R.J.O. and H.M.S. from the Gordon and Betty Moore Foundation.
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
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    Machine learning techniques to characterize functional traits of plankton from image data (2022)
    Association for the Sciences of Limnology and Oceanography
    Details
    Publication Date: 2022-10-18
    Description: © The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Orenstein, E., Ayata, S., Maps, F., Becker, É., Benedetti, F., Biard, T., Garidel‐Thoron, T., Ellen, J., Ferrario, F., Giering, S., Guy‐Haim, T., Hoebeke, L., Iversen, M., Kiørboe, T., Lalonde, J., Lana, A., Laviale, M., Lombard, F., Lorimer, T., Martini, S., Meyer, A., Möller, K.O., Niehoff, B., Ohman, M.D., Pradalier, C., Romagnan, J.-B., Schröder, S.-M., Sonnet, V., Sosik, H.M., Stemmann, L.S., Stock, M., Terbiyik-Kurt, T., Valcárcel-Pérez, N., Vilgrain, L., Wacquet, G., Waite, A.M., & Irisson, J. Machine learning techniques to characterize functional traits of plankton from image data. Limnology and Oceanography, 67(8), (2022): 1647-1669, https://doi.org/10.1002/lno.12101.
    Description: Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.
    Description: SDA acknowledges funding from CNRS for her sabbatical in 2018–2020. Additional support was provided by the Institut des Sciences du Calcul et des Données (ISCD) of Sorbonne Université (SU) through the support of the sponsored junior team FORMAL (From ObseRving to Modeling oceAn Life), especially through the post-doctoral contract of EO. JOI acknowledges funding from the Belmont Forum, grant ANR-18-BELM-0003-01. French co-authors also wish to thank public taxpayers who fund their salaries. This work is a contribution to the scientific program of Québec Océan and the Takuvik Joint International Laboratory (UMI3376; CNRS - Université Laval). FM was supported by an NSERC Discovery Grant (RGPIN-2014-05433). MS is supported by the Research Foundation - Flanders (FWO17/PDO/067). FB received support from ETH Zürich. MDO is supported by the Gordon and Betty Moore Foundation and the U.S. National Science Foundation. ECB is supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) under the grant agreement no. 88882.438735/2019-01. TB is supported by the French National Research Agency (ANR-19-CE01-0006). NVP is supported by the Spanish State Research Agency, Ministry of Science and Innovation (PTA2016-12822-I). FL is supported by the Institut Universitaire de France (IUF). HMS was supported by the Simons Foundation (561126) and the U.S. National Science Foundation (CCF-1539256, OCE-1655686). Emily Peacock is gratefully acknowledged for expert annotation of IFCB images. LS was supported by the Chair VISION from CNRS/Sorbonne Université.
    Repository Name: Woods Hole Open Access Server
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  • 3
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    Distance maps to estimate cell volume from two-dimensional plankton images (2014)
    Association for the Sciences of Limnology and Oceanography
    Details
    Publication Date: 2022-05-26
    Description: Author Posting. © Association for the Sciences of Limnology and Oceanography, 2012. This article is posted here by permission of Association for the Sciences of Limnology and Oceanography for personal use, not for redistribution. The definitive version was published in Limnology and Oceanography: Methods 10 (2012): 278-288, doi:10.4319/lom.2012.10.278.
    Description: We describe and evaluate an algorithm that uses a distance map to automatically calculate the biovolume of a planktonic organism from its two-dimensional boundary. Compared with existing approaches, this algorithm dramatically increases the speed and accuracy of biomass estimates from plankton images, and is thus especially suited for use with automated cell imaging technologies that produce large quantities of data. The algorithm operates on a two-dimensional image processed to identify organism boundaries. First, the distance of each interior pixel to the nearest boundary is calculated; next these same distances are assumed to apply for projection in the third dimension; and finally the resulting volume is adjusted by a multiplicative factor assuming locally circular cross-sections in the third dimension. Other cross-sectional shape factors can be applied as needed. In this way, the simple, computationally efficient, volume calculation can be refined to include taxon-specific shape information if available. We show that compared to traditional manual microscopic analysis, the distance map algorithm is unbiased and accurate (mean difference = -0.25%, standard deviation = 17%) for a range of cell morphologies, including those with concave boundaries that deviate from simple geometric shapes and whose volumes are not well represented by a solid of revolution around a single axis. Automated calculation of cell volumes can now be implemented with a combination of this new distance map algorithm for complex shapes and the solid of revolution approach for simple shapes, with an automated decision criterion to choose the appropriate approach for each image.
    Description: This research was supported by grants (to HMS) from the Gordon and Betty Moore Foundation and NASA’s Ocean Biology and Biogeochemistry program, and a Woods Hole Oceanographic Institution Summer Student Fellow award (to EAM).
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Format: application/pdf
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