Biology subjects

Barclay, R. S.

Publications and source records attributed to Barclay, R. S..

3 recordsLinked to original sources

bioRxiv2026

Solving Paleobotany's 200-year problem with fluorescence and artificial intelligence

Paleobotany has a 200-year problem: leaf fossils are often too poorly preserved to make proper taxonomic identification. Though they are the most abundant, and well-studied macroscopic fossil plant organ, many fossil leaves-- including specimens held in museum and university collections-- appear to lack the detailed features needed to take crucial next steps, like placement into phylogenetic context, generation of ecological interpretations, or reconstruction of paleoclimate. However, we have discovered that under UV and laser light [~]50% of leaves across a diverse survey of fossil deposits fluoresce in a way that reveals spectacularly detailed leaf venation and gross morphology that is invisible under white light. These 'Fossil Ciphers' bring new life into each step of the paleobotanical method, unlocking previously unattainable taxonomic classifications, quantitative morphometrics, and promising new avenues for the incorporation of AI into fossil leaf analyses. With the aid of fluorescence techniques, we can help solve paleobotany's 200-year problem.

paleontology↗

bioRxiv2023

CuticleTrace: A toolkit for capturing cell outlines of leaf cuticle with implications for paleoecology and paleoclimatology

PremiseLeaf epidermal cell morphology is closely tied to plants evolutionary histories and growth environments, and is therefore of interest to many plant biologists. However, cell measurement can be time-consuming and restrictive with current methods. CuticleTrace is a suite of FIJI and R-based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities. Methods and ResultsWe evaluated CuticleTrace-generated measurements against those from alternate automated methods and expert and undergraduate hand-tracings across a taxonomically diverse 50-image dataset of variable image qualities. We observed [~]93% statistical agreement between CuticleTrace and expert hand-traced measurements, outperforming alternate methods. ConclusionsCuticleTrace is broadly applicable, modular, and customizable, and integrates data visualization and cell shape measurement with image segmentation, lowering the barrier to high-throughput studies of epidermal morphology by vastly decreasing the labor investment required to generate high-quality cell shape datasets.

paleontology↗

bioRxiv2018

StomataCounter: a deep learning method applied to automatic stomatal identification and counting

O_LIStomata fulfill an important physiological role and are often phenotyped by researchers in many fields. Currently, no fully automated method exists to perform this task. Researchers typically rely on manual counts of stomata, which is an error-prone method and difficult to reproduce.\nC_LIO_LIWe introduce StomataCounter, an automated stomata counting system using a deep convolutional neural network to identify pores in a variety of different microscopic images. We used a human-in-the-loop approach to train and refine a neural network on a large variety of microscopic images, which helps us achieve robust detection among a number of datasets.\nC_LIO_LIOur network achieves 98.1% identification accuracy on Ginkgo SEM micrographs, and 94.2% transfer accuracy when tested on untrained species.\nC_LIO_LITo facilitate adoption of the method, we make a web tool available under http://www.stomata.science/\nC_LI

plant biology↗