Specimen-based analysis of morphology and the environment in ecologically dominant grasses: the power of the herbarium

Herbaria contain a cumulative sample of the world's flora, assembled by thousands of people over centuries. To capitalize on this resource, we conducted a specimen-based analysis of a major clade in the grass tribe Andropogoneae, including the dominant species of the world's grasslands in the genera Andropogon , Schizachyrium , Hyparrhenia and several others. We imaged 186 of the 250 named species of the clade, georeferenced the specimens and extracted climatic variables for each. Using semi- and fully automated image analysis techniques, we extracted spikelet morphological characters and correlated these with environmental variables. We generated chloroplast genome sequences to correct for phylogenetic covariance and here present a new phylogeny for 81 of the species. We confirm and extend earlier studies to show that Andropogon and Schizachyrium are not monophyletic. In addition, we find all morphological and ecological characters are homoplasious but variable among clades. For example, sessile spikelet length is positively correlated with awn length when all accessions are considered, but when separated by clade, the relationship is positive for three sub-clades and negative for three others. Climate variables showed no correlation with morphological variation in the spikelet pair; only very weak effects of temperature and precipitation were detected on macrohair density. This article is part of the theme issue ‘Biological collections for understanding biodiversity in the Anthropocene'.

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Erratum to: Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies

GigaScience ◽

10.1093/gigascience/giy043 ◽

2018 ◽

Vol 7 (7) ◽

Author(s):

Jonathan A Atkinson ◽

Guillaume Lobet ◽

Manuel Noll ◽

Patrick E Meyer ◽

Marcus Griffiths ◽

...

Keyword(s):

Machine Learning ◽

Image Analysis ◽

Large Scale ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Automated Image Analysis ◽

Genetic Studies ◽

Analysis Techniques ◽

Image Analysis Techniques

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Comparing Automatic and Manual Image Processing in FLARE Assay Analysis for Colon Carcinogenesis

Statistical Applications in Genetics and Molecular Biology ◽

10.2202/1544-6115.1102 ◽

2005 ◽

Vol 4 (1) ◽

Cited By ~ 1

Author(s):

Malgorzata Leyk ◽

Danh V Nguyen ◽

Sanju N Attoor ◽

Edward R Dougherty ◽

Nancy D Turner ◽

...

Keyword(s):

Oxidative Damage ◽

Statistical Power ◽

Corn Oil ◽

Colon Carcinogenesis ◽

Automated Image Analysis ◽

Imaging Method ◽

Summary Statistics ◽

Analysis Techniques ◽

Oxidative Damage To Dna ◽

Image Analysis Techniques

Measurement of the amount of oxidative damage to DNA is one tool that can be used to estimate the beneficial effect of diet on the prevention of colon carcinogenesis. The FLARE assay is a modification of the single-cell gel electrophoresis (Comet) assay, and provides a measure of the 8OHdG adduct in the cells. In this paper, we present two innovations to the existing methods of analysis. The first one is related to the FLARE assay itself. We describe automated image analysis techniques that can be expected to measure oxidative damage faster, reproducibly, with less noise, and hence achieve greater statistical power. The proposed technique is compared to an existing technique, which was more manual and thus slower. The second innovation is our statistical analysis: we exploit the shape of FLARE intensity histograms, and show statistically significant diet effects in the duodenum. Previous analyses of this data concentrated on simple summary statistics, and found only marginally statistically significant diet effects. With the new imaging method and measure of oxidative damage, we show cells in the duodenum exposed to fish oil as having more oxidative damage than cells exposed to corn oil.

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Determining the sensitivity of grassland area burned to climate variation in Xilingol, China, with an autoregressive distributed lag approach

International Journal of Wildland Fire ◽

10.1071/wf18171 ◽

2019 ◽

Vol 28 (8) ◽

pp. 628 ◽

Cited By ~ 3

Author(s):

Ali Hassan Shabbir ◽

Jiquan Zhang ◽

Xingpeng Liu ◽

James A. Lutz ◽

Carlos Valencia ◽

...

Keyword(s):

Wind Speed ◽

Wildland Fire ◽

Strong Relationship ◽

Maximum Temperature ◽

Climate Variables ◽

Area Burned ◽

Distributed Lag ◽

Temperature And Precipitation ◽

Autoregressive Distributed Lag ◽

The Relationship

We examined the relationship between climate variables and grassland area burned in Xilingol, China, from 2001 to 2014 using an autoregressive distributed lag (ARDL) model, and describe the application of this econometric method to studies of climate influences on wildland fire. We show that there is a stationary linear combination of non-stationary climate time series (cointegration) that can be used to reliably estimate the influence of different climate signals on area burned. Our model shows a strong relationship between maximum temperature and grassland area burned. Mean monthly wind speed and monthly hours of sunlight were also strongly associated with area burned, whereas minimum temperature and precipitation were not. Some climate variables like wind speed had significant immediate effects on area burned, the strength of which varied over the 2001–14 observation period (in econometrics terms, a ‘short-run’ effect). The relationship between temperature and area burned exhibited a steady-state or ‘long-run’ relationship. We analysed three different periods (2001–05, 2006–10 and 2011–14) to illustrate how the effects of climate on area burned vary over time. These results should be helpful in estimating the potential impact of changing climate on the eastern Eurasian Steppe.

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Evaluation of the Use of Single- and Multi-Magnification Convolutional Neural Networks for the Determination and Quantitation of Lesions in Nonclinical Pathology Studies

Toxicologic Pathology ◽

10.1177/0192623320986423 ◽

2021 ◽

pp. 019262332098642

Author(s):

Jogile Kuklyte ◽

Jenny Fitzgerald ◽

Sophie Nelissen ◽

Haolin Wei ◽

Aoife Whelan ◽

...

Keyword(s):

Quantitative Data ◽

Digital Pathology ◽

Model Performance ◽

Lesion Detection ◽

Analysis Techniques ◽

Liver And Kidney ◽

Specific Tissue ◽

The Relationship ◽

Image Analysis Techniques ◽

Whole Slide Images

Digital pathology platforms with integrated artificial intelligence have the potential to increase the efficiency of the nonclinical pathologist’s workflow through screening and prioritizing slides with lesions and highlighting areas with specific lesions for review. Herein, we describe the comparison of various single- and multi-magnification convolutional neural network (CNN) architectures to accelerate the detection of lesions in tissues. Different models were evaluated for defining performance characteristics and efficiency in accurately identifying lesions in 5 key rat organs (liver, kidney, heart, lung, and brain). Cohorts for liver and kidney were collected from TG-GATEs open-source repository, and heart, lung, and brain from internally selected R&D studies. Annotations were performed, and models were trained on each of the available lesion classes in the available organs. Various class-consolidation approaches were evaluated from generalized lesion detection to individual lesion detections. The relationship between the amount of annotated lesions and the precision/accuracy of model performance is elucidated. The utility of multi-magnification CNN implementations in specific tissue subtypes is also demonstrated. The use of these CNN-based models offers users the ability to apply generalized lesion detection to whole-slide images, with the potential to generate novel quantitative data that would not be possible with conventional image analysis techniques.

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