scholarly journals Colormesh: A novel method for quantifying variation in complex color patterns

2020 ◽  
Author(s):  
Jennifer J. Valvo ◽  
F. Helen Rodd ◽  
David Houle ◽  
J. David Aponte ◽  
Mitchel J. Daniel ◽  
...  
Keyword(s):  
Spatial Information ◽  
Color Pattern ◽  
Color Variation ◽  
Sampling Location ◽  
Continuous Measure ◽  
Color Patterns ◽  
Color Categories ◽  
Wide Range ◽  
Novel Method ◽  
Pattern Quantification

AbstractColor variation is one of the most obvious examples of variation in nature. Objective quantification and interpretation of variation in color and complex patterns is challenging. Assessment of variation in color patterns is limited by the reduction of color into categorical measures and lack of spatial information. We present Colormesh as a novel method for analyzing complex color patterns that offers unique capabilities. Compared to other methods, Colormesh maintains the continuous measure of color at individual sampling points throughout the pattern. This is particularly useful for analyses of variation in color patterns, whether interest is in specific locations or the pattern as a whole. In our approach, the use of Delaunay triangulation to determine sampling location eliminates the need for color patterns to have clearly defined pattern elements, and users are not required to identify discrete color categories. This method is complementary to several other methods available for color pattern quantification, and can be usefully deployed to address a wide range of questions about color pattern variation.

Measuring Perceptual Distance of Organismal Color Pattern using the Features of Deep Neural Networks

2019 ◽  
Author(s):  
Drew C. Wham ◽  
Briana Ezray ◽  
Heather M. Hines
Keyword(s):  
Deep Neural Networks ◽  
Color Pattern ◽  
Color Contrast ◽  
Biological Processes ◽  
Color Patterns ◽  
Biologically Relevant ◽  
Perceptual Distance ◽  
Data Formats ◽  
Wide Range ◽  
Study Designs

ABSTRACTA wide range of research relies upon the accurate and repeatable measurement of the degree to which organisms resemble one another. Here, we present an unsupervised workflow for analyzing the relationships between organismal color patterns. This workflow utilizes several recent advancements in deep learning based computer vision techniques to calculate perceptual distance. We validate this approach using previously published datasets surrounding diverse applications of color pattern analysis including mimicry, population differentiation, heritability, and development. We demonstrate that our approach is able to reproduce the biologically relevant color pattern relationships originally reported in these studies. Importantly, these results are achieved without any task-specific training. In many cases, we were able to reproduce findings directly from original photographs or plates with minimum standardization, avoiding the need for intermediate representations such as a cartoonized images or trait matrices. We then present two artificial datasets designed to highlight how this approach handles aspects of color patterns, such as changes in pattern location and the perception of color contrast. These results suggest that this approach will generalize well to support the study of a wide range of biological processes in a diverse set of taxa while also accommodating a variety of data formats, preprocessing techniques, and study designs.

scholarly journals Patternize: An R Package For Quantifying Color Pattern Variation

2017 ◽  
Author(s):  
Steven M. Van Belleghem ◽  
Riccardo Papa ◽  
Humberto Ortiz-Zuazaga ◽  
Frederik Hendrickx ◽  
Chris Jiggins ◽  
...  
Keyword(s):  
Association Studies ◽  
Genetic Association Studies ◽  
Image Data ◽  
Principal Component ◽  
R Package ◽  
Color Pattern ◽  
Color Patterns ◽  
Wide Range ◽  
Potential Applications ◽  
Population Comparisons

The use of image data to quantify, study and compare variation in the colors and patterns of organisms requires the alignment of images to establish homology, followed by color-based segmentation of images. Here we describe an R package for image alignment and segmentation that has applications to quantify color patterns in a wide range of organisms. patternize is an R package that quantifies variation in color patterns obtained from image data. patternize first defines homology between pattern positions across specimens either through manually placed homologous landmarks or automated image registration. Pattern identification is performed by categorizing the distribution of colors using an RGB threshold, k-means clustering or watershed transformation. We demonstrate that patternize can be used for quantification of the color patterns in a variety of organisms by analyzing image data for butterflies, guppies, spiders and salamanders. Image data can be compared between sets of specimens, visualized as heatmaps and analyzed using principal component analysis (PCA). patternize has potential applications for fine scale quantification of color pattern phenotypes in population comparisons, genetic association studies and investigating the basis of color pattern variation across a wide range of organisms.

scholarly journals Dog color patterns explained by modular promoters of ancient canid origin

2020 ◽  
Author(s):  
Danika L. Bannasch ◽  
Christopher B. Kaelin ◽  
Anna Letko ◽  
Robert Loechel ◽  
Petra Hug ◽  
...  
Keyword(s):  
Color Pattern ◽  
Color Variation ◽  
Hair Cycle ◽  
Color Patterns ◽  
Temporal And Spatial Distribution ◽  
Multiple Alleles ◽  
Haplotype Combination ◽  
Regulatory Modules ◽  
Regulatory Module ◽  
Temporal And Spatial

Distinctive color patterns in dogs are an integral component of canine diversity. Color pattern differences are thought to have arisen from mutation and artificial selection during and after domestication from wolves 1,2 but important gaps remain in understanding how these patterns evolved and are genetically controlled 3,4. In other mammals, variation at the ASIP gene controls both the temporal and spatial distribution of yellow and black pigments 3,5-7. Here we identify independent regulatory modules for ventral and hair cycle ASIP expression, and we characterize their action and evolutionary origin. Structural variants define multiple alleles for each regulatory module and are combined in different ways to explain five distinctive dog color patterns. Phylogenetic analysis reveals that the haplotype combination for one of these patterns is shared with arctic white wolves and that its hair cycle-specific module likely originated from an extinct canid that diverged from grey wolves more than 2 million years before present. Natural selection for a lighter coat during the Pleistocene provided the genetic framework for widespread color variation in dogs and wolves.

scholarly journals Evolutionary Dynamics of Structural Variation at a Key Locus for Color Pattern Diversification in Cichlid Fishes

2019 ◽  
Vol 11 (12) ◽  
pp. 3452-3465 ◽  
Author(s):  
Claudius F Kratochwil ◽  
Yipeng Liang ◽  
Sabine Urban ◽  
Julián Torres-Dowdall ◽  
Axel Meyer
Keyword(s):  
Evolutionary Dynamics ◽  
Lake Victoria ◽  
Functional Divergence ◽  
Color Pattern ◽  
Color Variation ◽  
Comparative Genomic ◽  
Color Patterns ◽  
East African ◽  
Cichlid Fishes ◽  
Adaptive Radiations

Abstract Color patterns in African cichlid fishes vary spectacularly. Although phylogenetic analysis showed already 30 years ago that many color patterns evolved repeatedly in these adaptive radiations, only recently have we begun to understand the genomic basis of color variation. Horizontal stripe patterns evolved and were lost several times independently across the adaptive radiations of Lake Victoria, Malawi, and Tanganyika and regulatory evolution of agouti-related peptide 2 (agrp2/asip2b) has been linked to this phenotypically labile trait. Here, we asked whether the agrp2 locus exhibits particular characteristics that facilitate divergence in color patterns. Based on comparative genomic analyses, we discovered several recent duplications, insertions, and deletions. Interestingly, one of these events resulted in a tandem duplication of the last exon of agrp2. The duplication likely precedes the East African radiations that started 8–12 Ma, is not fixed within any of the radiations, and is found to vary even within some species. Moreover, we also observed variation in copy number (two to five copies) and secondary loss of the duplication, illustrating a surprising dynamic at this locus that possibly promoted functional divergence of agrp2. Our work suggests that such instances of exon duplications are a neglected mechanism potentially involved in the repeated evolution and diversification that deserves more attention.

scholarly journals Batch-Mask: An automated Mask R-CNN workflow to isolate non-standard biological specimens for color pattern analysis

2021 ◽  
Author(s):  
John David Curlis ◽  
Timothy J Renney ◽  
Alison R Davis Rabosky ◽  
Talia Y Moore
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Visual Analysis ◽  
Source Region ◽  
Color Pattern ◽  
Color Variation ◽  
Data Sets ◽  
Color Patterns ◽  
Color Analysis ◽  
Data Set

Efficient comparisons of biological color patterns are critical for understanding the mechanisms by which organisms evolve in ecosystems, including sexual selection, predator-prey interactions, and thermoregulation. However, elongate or spiral-shaped organisms do not conform to the standard orientation and photographic techniques required for automated analysis. Currently, large-scale color analysis of elongate animals requires time-consuming manual landmarking, which reduces their representation in coloration research despite their ecological importance. We present Batch-Mask: an automated and customizable workflow to facilitate the analysis of large photographic data sets of non-standard biological subjects. First, we present a user guide to run an open-source region-based convolutional neural network with fine-tuned weights for identifying and isolating a biological subject from a background (masking). Then, we demonstrate how to combine masking with existing manual visual analysis tools into a single streamlined, automated workflow for comparing color patterns across images. Batch-Mask was 60x faster than manual landmarking, produced masks that correctly identified 96% of all snake pixels, and produced pattern energy results that were not significantly different from the manually landmarked data set. The fine-tuned weights for the masking neural network, user guide, and automated workflow substantially decrease the amount of time and attention required to quantitatively analyze non-standard biological subjects. By using these tools, biologists will be able to compare color, pattern, and shape differences in large data sets that include significant morphological variation in elongate body forms. This advance will be especially valuable for comparative analyses of natural history collections, and through automation can greatly expand the scale of space, time, or taxonomic breadth across which color variation can be quantitatively examined.

Functional significance of preserved color patterns of mollusks from the Gosport Sand (Eocene) of Alabama

1988 ◽  
Vol 62 (01) ◽  
pp. 83-87 ◽  
Author(s):  
Patricia H. Kelley ◽  
Charles T. Swann
Keyword(s):  
Functional Morphology ◽  
Functional Significance ◽  
Intraspecific Variability ◽  
Color Pattern ◽  
Color Patterns ◽  
Historical Factors ◽  
Life Mode ◽  
Molluscan Fauna ◽  
Excellent Preservation ◽  
Architectural Constraints

The excellent preservation of the molluscan fauna from the Gosport Sand (Eocene) at Little Stave Creek, Alabama, has made it possible to describe the preserved color patterns of 15 species. In this study the functional significance of these color patterns is tested in the context of the current adaptationist controversy. The pigment of the color pattern is thought to be a result of metabolic waste disposal. Therefore, the presence of the pigment is functional, although the patterns formed by the pigment may or may not have been adaptive. In this investigation the criteria proposed by Seilacher (1972) for testing the functionality of color patterns were applied to the Gosport fauna and the results compared with life mode as interpreted from knowledge of extant relatives and functional morphology. Using Seilacher's criteria of little ontogenetic and intraspecific variability, the color patterns appear to have been functional. However, the functional morphology studies indicate an infaunal life mode which would preclude functional color patterns. Particular color patterns are instead interpreted to be the result of historical factors, such as multiple adaptive peaks or random fixation of alleles, or of architectural constraints including possibly pleiotropy or allometry. The low variability of color patterns, which was noted within species and genera, suggests that color patterns may also serve a useful taxonomic purpose.

Variable Precision Depth Encoding for 3D Range Geometry Compression

2020 ◽  
Vol 2020 (17) ◽  
pp. 34-1-34-7
Author(s):  
Matthew G. Finley ◽  
Tyler Bell
Keyword(s):  
Normal Distribution ◽  
Arbitrary Distribution ◽  
File Size ◽  
Geometry Compression ◽  
Variable Precision ◽  
Wide Range ◽  
Novel Method ◽  
Encoding Method ◽  
Rgb Image ◽  
Color Channels

This paper presents a novel method for accurately encoding 3D range geometry within the color channels of a 2D RGB image that allows the encoding frequency—and therefore the encoding precision—to be uniquely determined for each coordinate. The proposed method can thus be used to balance between encoding precision and file size by encoding geometry along a normal distribution; encoding more precisely where the density of data is high and less precisely where the density is low. Alternative distributions may be followed to produce encodings optimized for specific applications. In general, the nature of the proposed encoding method is such that the precision of each point can be freely controlled or derived from an arbitrary distribution, ideally enabling this method for use within a wide range of applications.

Expressing the Self

2018 ◽  
Keyword(s):  
Philosophy Of Mind ◽  
The Self ◽  
Epistemic Status ◽  
First Person ◽  
Semantic Categories ◽  
Wide Range ◽  
Person Reference ◽  
De Se ◽  
Novel Method ◽  
Cultural Pragmatics

This book addresses different linguistic and philosophical aspects of referring to the self in a wide range of languages from different language families, including Amharic, English, French, Japanese, Korean, Mandarin, Newari (Sino-Tibetan), Polish, Tariana (Arawak), and Thai. In the domain of speaking about oneself, languages use a myriad of expressions that cut across grammatical and semantic categories, as well as a wide variety of constructions. Languages of Southeast and East Asia famously employ a great number of terms for first-person reference to signal honorification. The number and mixed properties of these terms make them debatable candidates for pronounhood, with many grammar-driven classifications opting to classify them with nouns. Some languages make use of egophors or logophors, and many exhibit an interaction between expressing the self and expressing evidentiality qua the epistemic status of information held from the ego perspective. The volume’s focus on expressing the self, however, is not directly motivated by an interest in the grammar or lexicon, but instead stems from philosophical discussions of the special status of thoughts about oneself, known as de se thoughts. It is this interdisciplinary understanding of expressing the self that underlies this volume, comprising philosophy of mind at one end of the spectrum and cross-cultural pragmatics of self-expression at the other. This unprecedented juxtaposition results in a novel method of approaching de se and de se expressions, in which research methods from linguistics and philosophy inform each other. The importance of this interdisciplinary perspective on expressing the self cannot be overemphasized. Crucially, the volume also demonstrates that linguistic research on first-person reference makes a valuable contribution to research on the self tout court, by exploring the ways in which the self is expressed, and thereby adding to the insights gained through philosophy, psychology, and cognitive science.

scholarly journals Overview of Popular Techniques of Raman Spectroscopy and Their Potential in the Study of Plant Tissues

Molecules ◽  
2021 ◽  
Vol 26 (6) ◽  
pp. 1537
Author(s):  
Aneta Saletnik ◽  
Bogdan Saletnik ◽  
Czesław Puchalski
Keyword(s):  
Raman Spectroscopy ◽  
Cell Walls ◽  
Structural Changes ◽  
Spatial Information ◽  
Technological Development ◽  
Analytical Techniques ◽  
High Sensitivity ◽  
Plant Tissues ◽  
Wide Range ◽  
Identification Technique

Raman spectroscopy is one of the main analytical techniques used in optical metrology. It is a vibration, marker-free technique that provides insight into the structure and composition of tissues and cells at the molecular level. Raman spectroscopy is an outstanding material identification technique. It provides spatial information of vibrations from complex biological samples which renders it a very accurate tool for the analysis of highly complex plant tissues. Raman spectra can be used as a fingerprint tool for a very wide range of compounds. Raman spectroscopy enables all the polymers that build the cell walls of plants to be tracked simultaneously; it facilitates the analysis of both the molecular composition and the molecular structure of cell walls. Due to its high sensitivity to even minute structural changes, this method is used for comparative tests. The introduction of new and improved Raman techniques by scientists as well as the constant technological development of the apparatus has resulted in an increased importance of Raman spectroscopy in the discovery and defining of tissues and the processes taking place in them.

Incorporating Neural Network Material Models Within Finite Element Analysis for Rheological Behavior Prediction

2006 ◽  
Vol 129 (1) ◽  
pp. 58-65 ◽  
Author(s):  
B. Scott Kessler ◽  
A. Sherif El-Gizawy ◽  
Douglas E. Smith
Keyword(s):  
Finite Element ◽  
Effective Means ◽  
Element Model ◽  
Operating Conditions ◽  
Behavior Prediction ◽  
Element Analysis ◽  
Material Models ◽  
Rheological Models ◽  
Wide Range ◽  
Novel Method

The accuracy of a finite element model for design and analysis of a metal forging operation is limited by the incorporated material model’s ability to predict deformation behavior over a wide range of operating conditions. Current rheological models prove deficient in several respects due to the difficulty in establishing complicated relations between many parameters. More recently, artificial neural networks (ANN) have been suggested as an effective means to overcome these difficulties. To this end, a robust ANN with the ability to determine flow stresses based on strain, strain rate, and temperature is developed and linked with finite element code. Comparisons of this novel method with conventional means are carried out to demonstrate the advantages of this approach.

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