Abstract:ABSTRACT.Crustacean growth studies typically use modal analysis rather than focusing on the growth of individuals. In the present work, we use geometric morphometrics to determine how organism shape and size varies during the life of the freshwater crab, Aegla uruguayana Schmitt, 1942. A total of 66 individuals from diverse life cycle stages were examined daily and each exuvia was recorded. Digital images of the dorsal region of the cephalothorax were obtained for each exuvia and were subsequently used to reco… Show more
“…We analyzed the landmark data with geometric morphometrics in R. 18 As described previously, 19 we detected outliers using the Procrustes distance to the mean and the within-individual variance of the deviations from the average position of each landmark, and optimized the outlier threshold by running the classification analysis at different thresholds. We used principal components analysis (PCA), linear models implemented for landmark data, 20 , 21 and canonical variates analysis (CVA) in geomorph, 22 Morpho, 23 shapes, 24 Evomorph, 25 and various custom functions in R. 26 , 27…”
Purpose
Deep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30–40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces.
Methods
We analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images.
Results
Unrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative.
Conclusion
Deep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of “unaffected” relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance.
“…We analyzed the landmark data with geometric morphometrics in R. 18 As described previously, 19 we detected outliers using the Procrustes distance to the mean and the within-individual variance of the deviations from the average position of each landmark, and optimized the outlier threshold by running the classification analysis at different thresholds. We used principal components analysis (PCA), linear models implemented for landmark data, 20 , 21 and canonical variates analysis (CVA) in geomorph, 22 Morpho, 23 shapes, 24 Evomorph, 25 and various custom functions in R. 26 , 27…”
Purpose
Deep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30–40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces.
Methods
We analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images.
Results
Unrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative.
Conclusion
Deep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of “unaffected” relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance.
“…Hard structures such as the shells of some crustaceans are widely used in studies of shape diversity due to their stability in terms of shape (Diawol et al, 2015;Rufino et al, 2004). In the phenotype studies of Cephalopods, the shapes of various structures can be used to infer their functional diversity and the patterns of phylogeny and evolution (Carreño Castilla et al, 2020;Kear, 1994;Roscian et al, 2022;Uyeno & Kier, 2007).…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, due to the rapid development of quantitative morphometrics and the increasing popularity and effectiveness of genetic tools, it allows researchers to evaluate the organism's genotype‐phenotype relationship and efficiently classify taxa based on morphological and molecular markers (Díaz‐Santana‐Iturrios et al, 2022; Dwivedi & De, 2023; Weinhardt et al, 2018). Hard structures such as the shells of some crustaceans are widely used in studies of shape diversity due to their stability in terms of shape (Diawol et al, 2015; Rufino et al, 2004). In the phenotype studies of Cephalopods, the shapes of various structures can be used to infer their functional diversity and the patterns of phylogeny and evolution (Carreño Castilla et al, 2020; Kear, 1994; Roscian et al, 2022; Uyeno & Kier, 2007).…”
The feeding organ of cephalopod species, the beak, can be used to reveal important ecological information. In this study, geometric morphometric approaches were employed to investigate the phylogenetic relevance and classification effect of beak lateral profile shape. The two‐dimensional beak morphologies of 1164 pairs of 24 species from 13 genera and five families were constructed, and their evolutionary relationships and taxonomic status were confirmed using geometric morphometrics and molecular biology approaches. We also assessed the phylogenetic signals of beak shape. The analysis results show shape variation in the beak mainly in the rostrum, hood, and lateral wall. The overall shape parameters (all PCs) of the upper and lower beak are more useful for species identification. The shapes of the upper and lower beak show a strong phylogenetic signal, and the phenogram based on the beak shape basically reflected the families’ taxonomic positions. We also hypothesized that the shape variation in the beaks of cephalopods may be ascribed to genetic and environmental differences. In summary, beaks are a reliable material for the classification of cephalopod species. Geometric morphometric approaches are a powerful tool to reveal the identification, phylogenetic relevance and phenotypic diversity of beak shape in cephalopods.
“…Estos rasgos variables morfológica se puede cuantificar y analizar por medio de la morfometría geométrica (Slice, 2005(Slice, , 2007; esta, captura la geometría de las estructuras y mantiene esta información en los análisis, combinando geometría, estadística y biología, para con ello lograr visualizar variaciones morfológicas de la estructura estudiada (Rohlf & Marcus, 1993;Adams et al, 2004;Diawol et al, 2015). En crustáceos invasores dulceacuícolas, no existen precedentes del uso de la morfometría geométrica en poblaciones de distintas regiones, debido a que su enfoque de estudio se centra en cómo actúa esta especie como organismo invasor y que afectaciones da este hacía el ecosistema (Arias-Pineda & Pedroza-Martínez, 2018).…”
Procambarus clarkii (Girard, 1852) es un decápodo cosmopolita invasor. Su tolerancia a una amplia gama de condiciones ambientales, su elevada capacidad de adaptación y su estrategia de alimentación flexible favorecen su establecimiento en los lugares en los que es introducido. Por medio de la morfometría geométrica se estudiaron diferencias morfológicas en tres segmentos corporales -pereion, propodio y uropodo- entre dos poblaciones de P. clarkii, una nativa en Luisiana (EE.UU) y otra invasora en Macanal (Colombia). No se encontraron diferencias significativas entre sexos o poblaciones en el propodio o el uropodo, pero sí en el pereion, que fue significativamente más ancho en la población de Luisiana. Estos cambios morfológicos indican la capacidad de ajuste fenotípico a distintas condiciones ambientales. Futuros estudios genéticos y de "jardín común" deberían desentrañar los componentes genético y ambiental de las diferencias encontradas.
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