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20 results for “morphometric dataset”
Fig. 3. Ammonoid morphology and dataset. A in Morphological disparity of early ammonoids: A geometric morphometric approach to investigate conch geometry
Fig. 3. Ammonoid morphology and dataset. A. Morphology of an ammonoid; as an example, the outline of the whorl profile taken at the maximum conch diameter is highlighted by a thick black line (modified from De Baets et al. 2010). B. Dataset analysed here; compilation of drawings of whorl profile outlines corresponding to Early and Middle Devonian ammonoids from Morocco.
Electron microscopy images and morphometric data of SARS-CoV-2 variants in ultrathin plastic sections - Dataset 06 (SARS-CoV-2 Omicron B.1.1.529; BA.2)
<p>Dataset 06 comprises 164 transmission electron microscopy images of extracellular SARS-CoV-2 (isolate Omicron B.1.1.529; BA.2) particles in ultrathin plastic sections (45 nm) through Vero cell cultures. The images were recorded with dimensions of 4112 x 3008 pixels at a pixel size of 0.1641 nm and stored in 16-bit TIF format. It is recommended that an image viewer capable of reading 16-bit images, such as IrfanView, be used to visualize the images. The image files have been size calibrated and can be opened with the correct size calibration using ImageJ or Fiji with the Bioformats importer. A PDF document is provided with the image files, which describes the methods used for the generation of the images. Additionally, an XLSX file is included, offering morphometric particle measurements and the calculated statistical values for their distribution. The dataset was produced as dataset 06 for a comparative morphometric analysis of evolving SARS-CoV-2 variants. Further datasets used for the analysis are available in this repository (see dataset description document).</p>
Electron microscopy images and morphometric data of SARS-CoV-2 variants in ultrathin plastic sections - Dataset 04 (SARS-CoV-2 Beta B.1.351)
<p>Dataset 04 comprises 132 transmission electron microscopy images of extracellular SARS-CoV-2 (isolate Beta B.1.351) particles in ultrathin plastic sections (45 nm) through Vero cell cultures. The images were recorded with dimensions of 4112 x 3008 pixels at a pixel size of 0.1641 nm and stored in 16-bit TIF format. It is recommended that an image viewer capable of reading 16-bit images, such as IrfanView, be used to visualize the images. The image files have been size calibrated and can be opened with the correct size calibration using ImageJ or Fiji with the Bioformats importer. A PDF document is provided with the image files, which describes the methods used for the generation of the images. Additionally, an XLSX file is included, offering morphometric particle measurements and the calculated statistical values for their distribution. The dataset was produced as dataset 04 for a comparative morphometric analysis of evolving SARS-CoV-2 variants. Further datasets used for the analysis are available in this repository (see dataset description document).</p>
Electron microscopy images and morphometric data of SARS-CoV-2 variants in ultrathin plastic sections - Dataset 02 (SARS-CoV-2 Italy-INMI1)
<p>Dataset 02 comprises 154 transmission electron microscopy images of extracellular SARS-CoV-2 (isolate Italy-INMI1) particles in ultrathin plastic sections (45 nm) through Vero cell cultures. The images were recorded with dimensions of 4112 x 3008 pixels at a pixel size of 0.1641 nm and stored in 16-bit TIF format. It is recommended that an image viewer capable of reading 16-bit images, such as IrfanView, be used to visualize the images. The image files have been size calibrated and can be opened with the correct size calibration using ImageJ or Fiji with the Bioformats importer. A PDF document is provided with the image files, which describes the methods used for the generation of the images. Additionally, an XLSX file is included, offering morphometric particle measurements and the calculated statistical values for their distribution. The dataset was produced as dataset 02 for a comparative morphometric analysis of evolving SARS-CoV-2 variants. Further datasets used for the analysis are available in this repository (see dataset description document).</p>
Dataset of the Geometric Morphometric Analysis of the article "Blank predetermination in the Iberian Acheulean. Insight from the cleaver on flake assemblage from Casal do Azemel site (Leiria, Portugal) by a Geometric Morphometric approach"
<p>Raw data and datasets of the Geometric Morphometric Analysis performed in the article "Blank predetermination in the Iberian Acheulean. Insight from the cleaver on flake assemblage from Casal do Azemel site (Leiria, Portugal) by a Geometric Morphometric approach".</p> <p>Contains the raw data, the Generalised Procrustes Analysis dataset, the Elliptic Fourier Analysis dataset and all the variables tested (on the EFA dataset). </p>
Cryptic sexual dimorphism reveals differing selection pressures on continental islands - Sulawesi Babbler (Pellorneum celebense) Morphometric Dataset
<p>Birds are well known for their sexual dimorphism. But not all forms of dimorphism are the same, and differences in morphology can be so subtle that they aren't detected by casual observation. We report that this is the case with the Sulawesi Babbler (<i>Pellorneum celebense</i>), the first reported instance of sexual dimorphism in this species or any of the ground babblers of the Southeast Asian islands. Our finding is based on a combination of morphometric analyses, genetic sexing, and observation of breeding condition. We highlight the utility of unsupervised clustering approaches, widely used in the biomedical literature, for the investigation of sexual dimorphism in ecological and evolutionary contexts.</p> <p>The sexual dimorphism was weaker on the mainland of Sulawesi and stronger on the continental islands of Kabaena, Muna, and Buton. This suggests that different evolutionary pressures have led the species to partition niches differently in these habitats, which separated only recently in geological history. This kind of intraspecific niche partitioning is an intrinsic part of the ecological niche of such species, one we must not miss if we are to fully understand these endlessly fascinating organisms and systems.</p>
Dataset from: Morphometric analyses of leaf shapes in four sympatric Medi-terranean oaks and hybrids in the Algerian Kabylie fores
<p>Data correspond to the characterization of putative hybrids and oak species coexisting in a highly diversified oak forest in Algeria with four native oak species (<em>Quercus suber</em>, <em>Q. ilex</em>, <em>Q. canariensis</em>, and <em>Q. afares</em>). Sixteen plots, including 89 trees from the four native species and their putative hybrids were sampled. Leaves were scanned and their geometric morphometry analyzed by using 11 landmarks on the right side on their abaxial surface. Related paper: Akli, A.; Lorenzo, Z.; Alia, R.; Rahbi, K.; Torres, E. Morphometric analyses of leaf shapes in four sympatric Mediterranean oaks and hybrids in the Algerian Kabylie forest. <em>Forests </em><strong>2022</strong>, <em>13</em>(4), 508 <a href="https://doi.org/10.3390/f13040508">https://doi.org/10.3390/f13040508</a></p>
Dataset and R code for 'Do Morphometric Data Improve Phylogenetic Reconstruction? A Systematic Review and Assessment'
<p>Dataset of tree (.tre) files and R code for running generalized Robinson-Foulds distance (Smith, 2020a;b) analysis. </p> <p>The .tre files can be read into R (R Core Team., 2023) using the ape::read.tree function (Paradis et al., 2003), full details in R code file. </p> <p> </p> <p> </p> <p>Paradis, E., Claude, J., & Strimmer, K. (2004). APE: analyses of phylogenetics and evolution in R language. Bioinformatics, 20(2), 289-290. </p> <p>R Core Team. (2023). R: A Language and Environment for Statistical Computing. (Version 4.2.2). R Foundation for Statistical Computing, Vienna, Austria: https://www.R-project.org/. </p> <p>Smith, M. R. (2020a). Information theoretic generalized Robinson–Foulds metrics for comparing phylogenetic trees. Bioinformatics, 36(20), 5007-5013. https://doi.org/10.1093/bioinformatics/btaa614 </p> <p>Smith, M. R. (2020b). TreeDist: distances between phylogenetic trees. R package version 2.7.0. doi:10.5281/zenodo.3528124. </p>
Dataset and script to: Morphometric variance, evolutionary constraints and their change through time in Late Devonian Palmatolepis conodonts
<p>Phenotypic variation is the raw material of evolution. Standing variation can facilitate response to selection along "lines of least evolutionary resistance", but selection itself might alter the structure of the variance. Shape was quantified using 2D geometric morphometrics in <i>Palmatolepis </i>conodonts through the Late Devonian period. Patterns of variance were characterized along the record by the variance-covariance matrix (P-matrix) and its first axis (Pmax). The Late Frasnian was marked by environmental oscillations culminating with the Frasnian/Famennian mass extinction. A shape response was associated with these fluctuations, together with a deflection of the Pmax and the P-matrix. Thereafter, along the Famennian, <i>Palmatolepis </i>mean shape shifted from broad elements with a large platform to slender elements devoid of platform. This shift in shape was associated with a reorientation of Pmax and the P-matrix, due to profound changes in the functioning of the elements selecting for new types of variants. Both cases provide empirical evidences that moving adaptive optimum can reorient phenotypic variation, boosting response to environmental changes. On such time scales, the question seems thus not to be whether the P-matrix is stable, but how it is varying in response to changes in selection regimes and shifts in adaptive optimum.</p>
Cryptic sexual dimorphism reveals differing selection pressures on continental islands - Sulawesi Babbler (Pellorneum celebense) Morphometric Dataset
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Data from: Tuatara and a new morphometric dataset for Rhynchocephalia: comments on Herrera‐Flores et al
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Dataset and script to: Morphometric variance, evolutionary constraints and their change through time in Late Devonian Palmatolepis conodonts
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Electron microscopy images and morphometric data of SARS-CoV-2 variants in ultrathin plastic sections - Dataset 05 (SARS-CoV-2 Delta B.1.617.2)
<p>Dataset 05 comprises 153 transmission electron microscopy images of extracellular SARS-CoV-2 (isolate Delta B.1.617.2) particles in ultrathin plastic sections (45 nm) through Vero cell cultures. The images were recorded with dimensions of 4112 x 3008 pixels at a pixel size of 0.1641 nm and stored in 16-bit TIF format. It is recommended that an image viewer capable of reading 16-bit images, such as IrfanView, be used to visualize the images. The image files have been size calibrated and can be opened with the correct size calibration using ImageJ or Fiji with the Bioformats importer. A PDF document is provided with the image files, which describes the methods used for the generation of the images. Additionally, an XLSX file is included, offering morphometric particle measurements and the calculated statistical values for their distribution. The dataset was produced as dataset 05 for a comparative morphometric analysis of evolving SARS-CoV-2 variants. Further datasets used for the analysis are available in this repository (see dataset description document).</p>
Electron microscopy images and morphometric data of SARS-CoV-2 variants in ultrathin plastic sections - Dataset 03 (SARS-CoV-2 Alpha B.1.1.7)
<p>Dataset 03 comprises 147 transmission electron microscopy images of extracellular SARS-CoV-2 (isolate Alpha B.1.1.7) particles in ultrathin plastic sections (45 nm) through Vero cell cultures. The images were recorded with dimensions of 4112 x 3008 pixels at a pixel size of 0.1641 nm and stored in 16-bit TIF format. It is recommended that an image viewer capable of reading 16-bit images, such as IrfanView, be used to visualize the images. The image files have been size calibrated and can be opened with the correct size calibration using ImageJ or Fiji with the Bioformats importer. A PDF document is provided with the image files, which describes the methods used for the generation of the images. Additionally, an XLSX file is included, offering morphometric particle measurements and the calculated statistical values for their distribution. The dataset was produced as dataset 03 for a comparative morphometric analysis of evolving SARS-CoV-2 variants. Further datasets used for the analysis are available in this repository (see dataset description document).</p>
Electron microscopy images and morphometric data of SARS-CoV-2 variants in ultrathin plastic sections - Dataset 01 (SARS-CoV-2 Munich929)
<p>Dataset 01 comprises 150 transmission electron microscopy images of extracellular SARS-CoV-2 (isolate Munich929) particles in ultrathin plastic sections (45 nm) through Vero cell cultures. The images were recorded with dimensions of 4112 x 3008 pixels at a pixel size of 0.1641 nm and stored in 16-bit TIF format. It is recommended that an image viewer capable of reading 16-bit images, such as IrfanView, be used to visualize the images. The image files have been size calibrated and can be opened with the correct size calibration using ImageJ or Fiji with the Bioformats importer. A PDF document is provided with the image files, which describes the methods used for the generation of the images. Additionally, an XLSX file is included, offering morphometric particle measurements and the calculated statistical values for their distribution. The dataset was produced as dataset 01 for a comparative morphometric analysis of evolving SARS-CoV-2 variants. Further datasets used for the analysis are available in this repository (see dataset description document).</p>
Data from: On the comparison of the strength of morphological integration across morphometric datasets
Evolutionary morphologists frequently wish to understand the extent to which organisms are integrated, and whether the strength of morphological integration among subsets of phenotypic variables differ among taxa or other groups. However, comparisons of the strength of integration across datasets are difficult, in part because the summary measures that characterize these patterns (RV and rPLS) are dependent both on sample size and on the number of variables. As a solution to this issue we propose a standardized test statistic (a z-score) for measuring the degree of morphological integration between sets of variables. The approach is based on a partial least squares analysis of trait covariation, and its permutation-based sampling distribution. Under the null hypothesis of a random association of variables, the method displays a constant expected value and confidence intervals for datasets of differing sample sizes and variable number, thereby providing a consistent measure of integration suitable for comparisons across datasets. A two-sample test is also proposed to statistically determine whether levels of integration differ between datasets, and an empirical example examining cranial shape integration in Mediterranean wall lizards illustrates its use. Some extensions of the procedure are also discussed.
Data from: Sharing is caring? measurement error and the issues arising from combining 3D morphometric datasets
Geometric morphometrics is routinely used in ecology and evolution and morphometric datasets are increasingly shared among researchers, allowing for more comprehensive studies and higher statistical power (as a consequence of increased sample size). However, sharing of morphometric data opens up the question of how much nonbiologically relevant variation (i.e., measurement error) is introduced in the resulting datasets and how this variation affects analyses. We perform a set of analyses based on an empirical 3D geometric morphometric dataset. In particular, we quantify the amount of error associated with combining data from multiple devices and digitized by multiple operators and test for the presence of bias. We also extend these analyses to a dataset obtained with a recently developed automated method, which does not require human-digitized landmarks. Further, we analyze how measurement error affects estimates of phylogenetic signal and how its effect compares with the effect of phylogenetic uncertainty. We show that measurement error can be substantial when combining surface models produced by different devices and even more among landmarks digitized by different operators. We also document the presence of small, but significant, amounts of nonrandom error (i.e., bias). Measurement error is heavily reduced by excluding landmarks that are difficult to digitize. The automated method we tested had low levels of error, if used in combination with a procedure for dimensionality reduction. Estimates of phylogenetic signal can be more affected by measurement error than by phylogenetic uncertainty. Our results generally highlight the importance of landmark choice and the usefulness of estimating measurement error. Further, measurement error may limit comparisons of estimates of phylogenetic signal across studies if these have been performed using different devices or by different operators. Finally, we also show how widely held assumptions do not always hold true, particularly that measurement error affects inference more at a shallower phylogenetic scale and that automated methods perform worse than human digitization.
Data from: On the comparison of the strength of morphological integration across morphometric datasets
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Data from: Sharing is caring? measurement error and the issues arising from combining 3D morphometric datasets
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Dataset related to article "Morphometric vertebral fractures in patients with castration-resistant prostate cancer undergoing treatment with radium-223: a longitudinal study in the real-life clinical practice"
<p>This record contains data related to article "Morphometric vertebral fractures in patients with castration-resistant prostate cancer undergoing treatment with radium-223: a longitudinal study in the real-life clinical practice"</p> <p><strong>Abstract</strong></p> <p><strong>Purpose: </strong>Radium-223 was associated with high incidence of non-vertebral fractures in patients with castration-resistant prostate cancer (CRPC). However, it is still unclear whether radium-223 may induce skeletal fragility regardless of other therapies for CRPC. We aimed at evaluating the prevalence, incidence, and determinants of vertebral fractures (VFs), i.e., the most frequent complication of skeletal fragility, in CRCP patients undergoing radium-223 therapy in the real-life clinical practice.</p> <p><strong>Methods: </strong>We retrospectively reviewed 49 CRPC patients with symptomatic bone metastases treated with radium-223. Patients received median number of four radium-223 doses (range: 2-6) and were followed-up for a median period of 11 months (range: 6-44). VFs were assessed by a quantitative morphometry using lateral images of spine 11C-Choline PET/CT, excluding from the analysis the vertebral bodies affected by bone metastases.</p> <p><strong>Results: </strong>Before radium-223 administration, 24 patients (49%) had VFs significantly associated with duration of androgen deprivation therapy (ADT; odds ratio 1.29) and previous abiraterone therapy (odds ratio 3.80). During radium-223 therapy, incident VFs occurred in 25% of patients, in relationship with prevalent VFs (hazard ratio 6.89) and change in serum total alkaline phosphatase values (hazard ratio 0.97), whereas the correlations with ADT and abiraterone therapy were lost. Noteworthy, the risk of VFs did not correlate with the therapeutic end points of radium-223.</p> <p><strong>Conclusions: </strong>This study provides a first evidence that in real-life clinical practice, radium-223 therapy may induce skeletal fragility with high risk of VFs, likely by inhibition of bone formation and independently of ADT and abiraterone therapy.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.