scholarly journals Coloration Characteristic and Population Genetic Analysis of Wild-Captured Giant Tiger Shrimp (Penaeus monodon) from Aceh Timur

2018 ◽  
Vol 23 (3) ◽  
pp. 123
Author(s):  
Indriatmoko Indriatmoko ◽  
Dimas A. Hedianto ◽  
Sari Budi Moria ◽  
Didik WH Tjahjo
Keyword(s):  
Penaeus Monodon ◽  
Color Space ◽  
Quantitative Information ◽  
Color Analysis ◽  
Population Genetic Analysis ◽  
Tiger Shrimp ◽  
16S Rdna Pcr ◽  
Aceh Province ◽  
Image Pixels ◽  
Space Data

Giant tiger shrimp (Penaeus monodon) has become a prime commodity in Indonesia which was produced by aquaculture and capture fisheries activities. Aceh Province, in this case mostly represented by Aceh Timur District, was well-known as the center of wild-captured-adult giant tiger shrimp. Several previous investigations had proved for its high-quality shrimp spawner in producing good eggs in quality and quantity under artificial spawning condition. Two main interesting points of wild giant tiger shrimp from Aceh Timur came from their coloration and population clusters. This report was aimed to provide that information pre-preliminary and highlighted quantitative information of coloration characteristic through RGB (Red Green Blue) and CIE Lab color space data analysis, as well as, 16S rDNA-PCR-RFLP genetic comparison among four population clusters in Aceh Timur Waters. The color analysis resulted in significant differences between wild-captured and pond-cultured giant tiger shrimp which produced R value 0.1524±0.0091 and 0.1268±0.0004, respectively. Total pixel analysis through L* a* b* color space has distinguished detailed differentiation between wild-captured and pond-cultured giant tiger shrimp acquired images. It is known that most of the wild-captured image pixels were concentrated in quadrant I (+a, +b) while pond-cultured in quadrant II (-a, +b) and III (-a, -b).Genotyping of represented samples from 4 population clusters, i.e. Aceh Tamiang, Langsa, Peudawa, and Julok produce 2 haplotype composite, AAA and AAB. Among 4 clusters, it was found that Julok has become the only cluster which has a different haplotype composite ratio (1:1) (D 0.0348, V 0,9501) from the others (4:1)(V 0.9504).

scholarly journals Quantitative color profiling of images in a comparative framework using the R package colordistance

2018 ◽  
Author(s):  
Hannah Weller ◽  
Mark Westneat
Keyword(s):  
Profile Analysis ◽  
Color Space ◽  
Distance Matrix ◽  
R Package ◽  
Color Variation ◽  
Statistical Testing ◽  
Color Analysis ◽  
Color Histograms ◽  
Color Profile ◽  
Image Pixels

Color is a central aspect of biology, with important impacts on ecology and evolution. Organismal color may be adaptive or incidental, seasonal or permanent, species- or population-specific, or modified for breeding, defense or camouflage. Thus, measuring and comparing color among organisms provides important biological insights. However, color comparison is limited by color categorization methods, with few universal tools available for quantitative color profiling and comparison. We present a package of R tools for processing images of organisms (or other objects) in order to quantify color profiles, gather color trait data, and compare color palettes in a reproducible way. The package treats image pixels as 3D coordinates in “color space", producing a multidimensional color histogram for each image. Pairwise distances between histograms are computed using earth mover's distance or a combination of more conventional distance metrics. The user sets parameters for generating color histograms, and comparative color profile analysis is performed through pairwise comparisons to produce a color distance matrix for a set of images. The toolkit provided in the colordistance R package can be used for analyses involving quantitative color variation in organisms with statistical testing. We illustrate the use of colordistance with three biological examples: hybrid coloration in butterflyfishes; mimicry in wing coloration in Heliconius butterflies; and analysis of background matching in camouflaging flounder fish. The tools presented for quantitative color analysis may be applied to a broad range of questions in biology and other disciplines.

scholarly journals Quantitative color profiling of digital images with earth mover’s distance using the R package colordistance

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6398 ◽  
Author(s):  
Hannah I. Weller ◽  
Mark W. Westneat
Keyword(s):  
Color Space ◽  
Pairwise Comparison ◽  
Distance Matrix ◽  
R Package ◽  
Comparison Method ◽  
Earth Mover’S Distance ◽  
Color Analysis ◽  
Earth Mover's Distance ◽  
Color Similarity ◽  
Image Pixels

Biological color may be adaptive or incidental, seasonal or permanent, species- or population-specific, or modified for breeding, defense or camouflage. Although color is a hugely informative aspect of biology, quantitative color comparisons are notoriously difficult. Color comparison is limited by categorization methods, with available tools requiring either subjective classifications, or expensive equipment, software, and expertise. We present an R package for processing images of organisms (or other objects) in order to quantify color profiles, gather color trait data, and compare color palettes on the basis of color similarity and amount. The package treats image pixels as 3D coordinates in a “color space,” producing a multidimensional color histogram for each image. Pairwise distances between histograms are computed using earth mover’s distance, a technique borrowed from computer vision, that compares histograms using transportation costs. Users choose a color space, parameters for generating color histograms, and a pairwise comparison method to produce a color distance matrix for a set of images. The package is intended as a more rigorous alternative to subjective, manual digital image analyses, not as a replacement for more advanced techniques that rely on detailed spectrophotometry methods unavailable to many users. Here, we outline the basic functions of colordistance, provide guidelines for the available color spaces and quantification methods, and compare this toolkit with other available methods. The tools presented for quantitative color analysis may be applied to a broad range of questions in biology and other disciplines.

scholarly journals Quantitative color profiling of digital images with earth mover's distance using the R package colordistance

2018 ◽  
Author(s):  
Hannah Weller ◽  
Mark Westneat
Keyword(s):  
Color Space ◽  
Pairwise Comparison ◽  
Distance Matrix ◽  
R Package ◽  
Comparison Method ◽  
Earth Mover’S Distance ◽  
Color Analysis ◽  
Earth Mover's Distance ◽  
Color Similarity ◽  
Image Pixels

Biological color may be adaptive or incidental, seasonal or permanent, species- or population-specific, or modified for breeding, defense or camouflage. Although color is a hugely informative aspect of biology, quantitative color comparisons are notoriously difficult. Color comparison is limited by categorization methods, with available tools requiring either subjective classifications, or expensive equipment, software, and expertise. We present an R package for processing images of organisms (or other objects) in order to quantify color profiles, gather color trait data, and compare color palettes on the basis of color similarity and amount. The package treats image pixels as 3D coordinates in a “color space", producing a multidimensional color histogram for each image. Pairwise distances between histograms are computed using earth mover's distance, a technique borrowed from computer vision that compares histograms using transportation costs. Users choose a color space, parameters for generating color histograms, and a pairwise comparison method to produce a color distance matrix for a set of images. The package is intended as a more rigorous alternative to subjective, manual digital image analyses, not as a replacement for more advanced techniques that rely on detailed spectrophotometry methods unavailable to many users. Here, we outline the basic functions colordistance, provide guidelines for the available color spaces and quantification methods, and compare this toolkit with other available methods. The tools presented for quantitative color analysis may be applied to a broad range of questions in biology and other disciplines.

scholarly journals Quantitative color profiling of digital images with earth mover's distance using the R package colordistance

2018 ◽  
Author(s):  
Hannah Weller ◽  
Mark Westneat
Keyword(s):  
Color Space ◽  
Pairwise Comparison ◽  
Distance Matrix ◽  
R Package ◽  
Comparison Method ◽  
Earth Mover’S Distance ◽  
Color Analysis ◽  
Earth Mover's Distance ◽  
Color Similarity ◽  
Image Pixels

Biological color may be adaptive or incidental, seasonal or permanent, species- or population-specific, or modified for breeding, defense or camouflage. Although color is a hugely informative aspect of biology, quantitative color comparisons are notoriously difficult. Color comparison is limited by categorization methods, with available tools requiring either subjective classifications, or expensive equipment, software, and expertise. We present an R package for processing images of organisms (or other objects) in order to quantify color profiles, gather color trait data, and compare color palettes on the basis of color similarity and amount. The package treats image pixels as 3D coordinates in a “color space", producing a multidimensional color histogram for each image. Pairwise distances between histograms are computed using earth mover's distance, a technique borrowed from computer vision that compares histograms using transportation costs. Users choose a color space, parameters for generating color histograms, and a pairwise comparison method to produce a color distance matrix for a set of images. The package is intended as a more rigorous alternative to subjective, manual digital image analyses, not as a replacement for more advanced techniques that rely on detailed spectrophotometry methods unavailable to many users. Here, we outline the basic functions colordistance, provide guidelines for the available color spaces and quantification methods, and compare this toolkit with other available methods. The tools presented for quantitative color analysis may be applied to a broad range of questions in biology and other disciplines.

Acute toxicity, accumulation and sublethal effects of four neonicotinoids on juvenile Black Tiger Shrimp (Penaeus monodon)

Chemosphere ◽  
2021 ◽  
Vol 275 ◽  
pp. 129918
Author(s):  
Peter Butcherine ◽  
Brendan P. Kelaher ◽  
Matthew D. Taylor ◽  
Corinne Lawson ◽  
Kirsten Benkendorff
Keyword(s):  
Acute Toxicity ◽  
Sublethal Effects ◽  
Penaeus Monodon ◽  
Black Tiger Shrimp ◽  
Tiger Shrimp

Characterization and functional analysis of fibrinogen-related protein (FreP) in the black tiger shrimp, Penaeus monodon

2021 ◽  
Vol 109 ◽  
pp. 87-96
Author(s):  
Prawit Oangkhana ◽  
Piti Amparyup ◽  
Anchalee Tassanakajon ◽  
Elumalai Preetham ◽  
Ratree Wongpanya
Keyword(s):  
Functional Analysis ◽  
Penaeus Monodon ◽  
Related Protein ◽  
Black Tiger Shrimp ◽  
Tiger Shrimp

A key gene of the RNA interference pathway in the black tiger shrimp, Penaeus monodon: Identification and functional characterisation of Dicer-1

2008 ◽  
Vol 24 (2) ◽  
pp. 223-233 ◽  
Author(s):  
Jianguo Su ◽  
Dang T.H. Oanh ◽  
Russell E. Lyons ◽  
Lisa Leeton ◽  
Marielle C.W. van Hulten ◽  
...  
Keyword(s):  
Rna Interference ◽  
Penaeus Monodon ◽  
Black Tiger Shrimp ◽  
Tiger Shrimp ◽  
Functional Characterisation

Molecular cloning and expression of progestin membrane receptor component 1 (Pgmrc1) of the giant tiger shrimp Penaeus monodon

2010 ◽  
Vol 168 (3) ◽  
pp. 440-449 ◽  
Author(s):  
Rachanimuk Preechaphol ◽  
Sirawut Klinbunga ◽  
Keisuke Yamano ◽  
Piamsak Menasveta
Keyword(s):  
Molecular Cloning ◽  
Penaeus Monodon ◽  
Membrane Receptor ◽  
Cloning And Expression ◽  
Tiger Shrimp ◽  
Receptor Component

Digital color analysis for colorimetric signal processing: towards an analytically justified choice of acquisition technique and color space

2021 ◽  
pp. 130274
Author(s):  
Nikolai Yu. Tiuftiakov ◽  
Andrey V. Kalinichev ◽  
Nadezhda V. Pokhvishcheva ◽  
Maria A. Peshkova
Keyword(s):  
Signal Processing ◽  
Color Space ◽  
Color Analysis ◽  
Acquisition Technique
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