P.A. Sims, D.G. Mann and L.K. Medlin. 2006. Evolution of the diatoms: insights from fossil, biological and molecular data.Phycologia 45: 361–402. DOI: 10.2216/05-22.1Molecular sequence analyses hav...

/pdf/evolution-of-the-diatoms-insights-from-fossil-biological-and-1yu5ukpsz5.pdf

Evolution of the diatoms: insights from fossil, biological and molecular data

High-resolution photomicrographs of phytoplankton cells and chains can now be acquired with imaging-in-flow systems at rates that make manual identification impractical for many applications. To address the challenge for automated taxonomic identification of images generated by our custom-built submersible Imaging FlowCytobot, we developed an approach that relies on extraction of image features, which are then presented to a machine learning algorithm for classification. Our approach uses a combination of image feature types including size, shape, symmetry, and texture characteristics, plus orientation invariant moments, diffraction pattern sampling, and co-occurrence matrix statistics. Some of these features required preprocessing with image analysis techniques including edge detection after phase congruency calculations, morphological operations, boundary representation and simplification, and rotation. For the machine learning strategy, we developed an approach that combines a feature selection algorithm and use of a support vector machine specified with a rigorous parameter selection and training approach. After training, a 22-category classifier provides 88% overall accuracy for an independent test set, with individual category accuracies ranging from 68% to 99%. We demonstrate application of this classifier to a nearly uninterrupted 2-month time series of images acquired in Woods Hole Harbor, including use of statistical error correction to derive quantitative concentration estimates, which are shown to be unbiased with respect to manual estimates for random subsamples. Our approach, which provides taxonomically resolved estimates of phytoplankton abundance with fine temporal resolution (hours for many species), permits access to scales of variability from tidal to seasonal and longer.

https://aslopubs.onlinelibrary.wiley.com/doi/pdf/10.4319/lom.2007.5.204

Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry

Key-words: Bacillariophyta, biomonitoring, check-list, metric, river Diatoms are a very diverse group of microalgae that encompass about 100000 taxa. This makes an excellent bioindicator especially of rivers. The majority of biotic indices based on diatoms are constructed on species pollution-sensitivities. Nevertheless, recent studies showed the interest to use complementary metrics for biomonitoring: life-forms, cell-sizes and ecological guilds. Unfortunately, taxa memberships to such metrics are scattered in numerous floras and papers. Therefore, studies using such metrics often referred to grey literature or home-made checklists. The objective of this paper is to make available to the scientific community an important check-list of taxa frequently observed in European rivers with their memberships to these metrics. After defining these metrics, 1115 taxa were codified using Biofresh and Omnidia codes in an Excel file. Metrics memberships were then explored in a taxonomical framework. It clearly appears that metrics memberships are determined by taxonomy. Lifeforms and cell sizes taxa memberships and their study in the framework of evolutionary history of diatoms is a field that is worth studying for a better understanding of diatoms ecology. Such inputs should be beneficial for the future improvement of diatom-based monitoring tools.

/pdf/life-forms-cell-sizes-and-ecological-guilds-of-diatoms-in-54sjtbcjjh.pdf

Life-forms, cell-sizes and ecological guilds of diatoms in European rivers

An Assessment of Potential Diatom “Barcode” Genes (cox1, rbcL, 18S and ITS rDNA) and their Effectiveness in Determining Relationships in Sellaphora (Bacillariophyta)

http://www.vliz.be/imisdocs/publications/133824.pdf

Global diversity and biogeography of Skeletonema species (bacillariophyta).

Morphometric shape analysis and ultrastructural data are provided for six genodemes of the Sellaphora pupula species complex that have been studied during the last 20 years from Blackford Pond, Edi

/pdf/the-sellaphora-pupula-species-complex-bacillariophyceae-299nb9xlmp.pdf

The Sellaphora pupula species complex (Bacillariophyceae): morphometric analysis, ultrastructure and mating data provide evidence for five new species

A range image segmentation contest was organized in conjunction with ICPR'2000. The goal is to continue the effort of experimentally evaluating range image segmentation algorithms initiated by Hoover et al. (1996) and Powell et al. (1998). This paper summarizes the results of the contest.

Some further results of experimental comparison of range image segmentation algorithms

Identification by curvature of convex and concave segments

The algorithm developed uses an octree pyramid in which noise is reduced at the expense of the spatial resolution. At a certain level an unsupervised clustering without spatial connectivity constraints is applied. After the classification, isolated voxels and insignificant regions are removed by assigning them to their neighbours. The spatial resolution is then increased by the downprojection of the regions, level by level. At each level the uncertainty of the boundary voxels is minimised by a dynamic selection and classification of these, using an adaptive 3D filtering. The algorithm is tested using different data sets, including NMR data.

Fast segmentation of 3D data using an octree

We describe a new contour feature set. A contour is segmented into convex, concave and straight segments, after which length and curvature features are computed. A symmetry analysis allows the detection of the number of elementary segments. Results on two contour test sets were obtained: Using only four features, a simple nearest-mean classifier yielded a perfect identification (ID) rate of 100% on a small set consisting of shapes with minute differences, which are difficult to identify even for human experts. Using 10 features, it yielded 83.5% on a large set with very diverse shapes.

Diatom recognition by convex and concave contour curvature

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