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FIGURE 3 in Revision of the Calceolaria tripartita s. l. species complex (Calceolariaceae) using multivariate analyses of morphological characters
FIGURE 3. Bivariate scatter plot formed by the two first components of the PCA of 13 characters from 95 specimens assigned to Calceolaria tripartita s.s. Each point corresponds to a single specimen; shape of the symbols indicates country of origin (see legend); filled circles indicate different specimens from a single population in SE Peru.
Intravoxel incoherent motion model of diffusion weighted imaging and diffusion kurtosis imaging in differentiating of local colorectal cancer recurrence from scar/fibrosis tissue by multivariate logistic regression analysis
<p>We uploaded mean of diffusion coefficient (MD) and mean of diffusional Kurtosis values of 56 patients related to the manuscript: Fusco, Roberta, Vincenza Granata, Mario Sansone, Robert Grimm, Paolo Delrio, Daniela Rega, Fabiana Tatangelo, Antonio Avallone, Nicola Raiano, Giuseppe Totaro, Vincenzo Cerciello, Biagio Pecori, and Antonella Petrillo. 2020. "Intravoxel Incoherent Motion Model of Diffusion Weighted Imaging and Diffusion Kurtosis Imaging in Differentiating of Local Colorectal Cancer Recurrence from Scar/Fibrosis Tissue by Multivariate Logistic Regression Analysis" Applied Sciences 10, no. 23: 8609. https://doi.org/10.3390/app10238609</p>
Figure 2 in Disentangling prey-handling efficiency of larval newts through multivariate prey trait analysis
Figure 2. Biplots of handling efficiency traits ("concealment", "body shape" and "body flexibility") obtained from a fuzzy principal component analysis. See legend of Figure 1 for further explanations.
Figure 1 in Disentangling prey-handling efficiency of larval newts through multivariate prey trait analysis
Figure 1. Biplots of handling efficiency traits ("mobility", "trajectory" and "agility") obtained from a fuzzy principal component analysis. Position of species depending on the prey present in gut contents (A, C, E) and axes interpretation, including the histogram of eigenvalues (B, D, F).
Figure 3 in Disentangling prey-handling efficiency of larval newts through multivariate prey trait analysis
Figure 3. Component analysis of the scores of the eigenvalues grouped by traits ("mobility", "trajectory", "agility", "concealment", "body shape" and "body flexibility"). Eigenvalues are represented by numbers of each axis (1, 2, 3 and 4).
Figure 13 in Geometric and traditional morphometrics for the assessment of character state identity: multivariate statistical analyses of character variation in the genus Arrenurus (Acari, Hydrachnidia, Arrenuridae)
Figure 13. Multivariate variation in the eight interlandmark distances measured between ventroglandularia V1, V2 and V3 in posterior view (distance set 3). A, scatter plot of canonical variate scores (root 1 vs. root 2), distances with highest standardized factors are illustrated for both axes. B, overall pattern of similarity among 11 Megaluracarus species and two Dadayella species based on Mahalanobis distances computed from the canonical variate analysis. This UPGMA phenogram (unweighted-pair grouping method using averages) groups the nine character states discovered in the set of distances from the ventroglandularia. Branches are labelled according to discrimination order defined by the canonical variates. Symbols in the plot and in the phenogram are as listed in Figure 11.
Figure 12 in Geometric and traditional morphometrics for the assessment of character state identity: multivariate statistical analyses of character variation in the genus Arrenurus (Acari, Hydrachnidia, Arrenuridae)
Figure 12. Multivariate variation in the 15 interlandmark distances measured between postocularia R2 and dorsoglandularia D2, D3 and D4 (distance set 2). (A) scatter plot of canonical variate scores (root 1 vs. root 2); distances with the highest standardized factors are illustrated for both axes. (B) overall pattern of similarity among 11 Megaluracarus species and two Dadayella species based on Mahalanobis distances computed from the canonical variate analysis. This UPGMA phenogram (unweighted-pair grouping method using averages) groups the 13 character states discovered in the collection of distances from the postocularia and dorsoglandularia. Branches are labelled according to discrimination order defined by the canonical variates. Symbols in the plot and in the phenogram are as listed in Fig. 11.
Figure 11 in Geometric and traditional morphometrics for the assessment of character state identity: multivariate statistical analyses of character variation in the genus Arrenurus (Acari, Hydrachnidia, Arrenuridae)
Figure 11. Multivariate variation in the 13 interlandmark distances sampled from the dorsal view of the idiosoma (distance set 1). (A) scatter plot of canonical variate scores (root 1 vs. root 2); distances with highest standardized factors are illustrated for both axes. (B) overall pattern of similarity among 11 Megaluracarus species and two Dadayella species based on Mahalanobis distances computed from the canonical variate analysis. This UPGMA phenogram (unweighted-pair grouping method using averages) groups the 12 character states discovered in the distances from the idiosoma. Branches are labelled according to discrimination order defined by the canonical variates. Symbols in the plot and in the phenogram are as follows: open blue circles, Arrenurus (Dadayella) adrianae; open green rhombuses, Dadayella aztecus; open red squares, Arrenurus (Megaluracarus) anae; solid green triangles, Megaluracarus anitahoffmannae; open pink triangles, Megaluracarus catoi; solid black circles, Megaluracarus colitus; solid grey squares, Megaluracarus costeroae; blue asterisks, Megaluracarus maya; solid red rhombuses, Megaluracarus neoexpansus; purple plus signs, Megaluracarus olmeca; lilac endashes, Megaluracarus tabascoensis; horizontal blue lines, Megaluracarus urbanus; green hyphens, Megaluracarus zitavus.
Figure 14 in Geometric and traditional morphometrics for the assessment of character state identity: multivariate statistical analyses of character variation in the genus Arrenurus (Acari, Hydrachnidia, Arrenuridae)
Figure 14. Single most-parsimonious tree selected in our phylogenetic analysis of the combined morphometric data with TNT (score 95.8119). The character matrix included continuous values of three distance sets and five landmark configurations. Only one shape character, the cauda outline (data set 2), is optimized in this tree. Landmark configurations of cauda shape at terminal nodes are as observed in each species. The numbers on each configuration indicate shapes that are significantly different, as evaluated by the canonical variate analysis and MANOVA for landmark data set 2, as illustrated and labelled in Fig. 7B. In all hypothetical landmark configurations of the cauda shape at internal nodes, deformation vectors at each point indicate displacements relative to the ancestral shape as optimized with TNT.
Figure 8 in Geometric and traditional morphometrics for the assessment of character state identity: multivariate statistical analyses of character variation in the genus Arrenurus (Acari, Hydrachnidia, Arrenuridae)
Figure 8. Shape variation of the postocularia and dorsoglandularia D2 and D3 in dorsal view (data set 3). (A) scatter plot of canonical variate scores (root 1 vs. root 2); shape changes relative to the mean shape are shown for both roots. (B) overall pattern of shape similarity among 11 Megaluracarus species and two Dadayella species based on Mahalanobis distances computed from the canonical variate analysis. This UPGMA phenogram (unweighted-pair grouping method using averages) groups the nine character states discovered in the postocularia and dorsoglandularia. Branches are labelled according to discrimination order defined by the canonical variates. Symbols in the plot and in the phenogram are as follows: open blue circles, Arrenurus (Dadayella) adrianae; open red squares, Dadayella aztecus; open green rhombuses Arrenurus (Megaluracarus) anae; blue asterisks, Megaluracarus anitahoffmannae; solid grey squares, Megaluracarus catoi; open pink triangles, Megaluracarus colitus; solid green triangles, Megaluracarus costeroae; solid red rhombuses, Megaluracarus maya; solid black circles, Megaluracarus neoexpansus; purple plus signs, Megaluracarus olmeca; lilac en-dashes, Megaluracarus tabascoensis; horizontal blue lines, Megaluracarus urbanus; green hyphens, Megaluracarus zitavus.
Figure 4 in Geometric and traditional morphometrics for the assessment of character state identity: multivariate statistical analyses of character variation in the genus Arrenurus (Acari, Hydrachnidia, Arrenuridae)
Figure 4. Sets of landmark (LM) and semi-landmark (SLM) configurations designed to register the shape of the dorsal (A–D) and posterior (E) views. (A) collection of 23 points for the anterior idiosoma outline, with three LMs and 20 SLMs (data set 1). (B) chain of 17 points for the cauda outline, with three LMs and 14 SLMs (data set 2). (C) string of ten points for the postocularia R2 and dorsoglandularia D2 and D3 (data set 3). (D) configuration of four points for the dorsoglandularia D4 (data set 4). (E) Arrangement of ten points for the ventroglandularia V1, V2, and V3 (data set 5).
Figure 7 in Geometric and traditional morphometrics for the assessment of character state identity: multivariate statistical analyses of character variation in the genus Arrenurus (Acari, Hydrachnidia, Arrenuridae)
Figure 7. Shape variation of the cauda outline in dorsal view (data set 2). (A) scatter plot of canonical variate scores (root 1 vs. root 2), deformation grids of shape changes relative to the mean shape are shown for both axes. (B) overall pattern of shape similarity among 11 Megaluracarus species and two Dadayella species based on Mahalanobis distances computed from the canonical variate analysis. All species were significantly different from each other, and therefore 13 character states were discovered in the cauda outline. Branches in the UPGMA phenogram (unweighted-pair grouping method using averages) are labelled according to discrimination order defined by the canonical variates. Symbols in the plot and in the phenogram are as described in Fig. 6.
Figure 6 in Geometric and traditional morphometrics for the assessment of character state identity: multivariate statistical analyses of character variation in the genus Arrenurus (Acari, Hydrachnidia, Arrenuridae)
Figure 6. Shape variation of the anterior idiosoma outline in dorsal view (data set 1). (A) scatter plot of canonical variate scores (root 1 vs. root 2); the deformation grid shows shape changes explained by the first discriminant axis. (B) overall pattern of shape similarity among 11 Megaluracarus species and two Dadayella species based on Mahalanobis distances computed from the canonical variate analysis. This UPGMA phenogram (unweighted-pair grouping method using averages) groups the ten character states discovered in the anterior idiosoma outline. Branches are labelled according to discrimination order, as defined by the canonical variates. Symbols in the plot and in the phenogram are as follows: open blue circles, Arrenurus (Dadayella) adrianae; open red squares, Dadayella aztecus; open green rhombuses, Arrenurus (Megaluracarus) anae; red solid rhombuses, Megaluracarus anitahoffmannae; black solid circles, Megaluracarus catoi; open pink triangles, Megaluracarus colitus; horizontal blue lines, Megaluracarus costeroae; solid grey squares, Megaluracarus maya; lilac en-dashes, Megaluracarus neoexpansus; purple plus signs, Megaluracarus olmeca; blue asterisks, Megaluracarus tabascoensis; solid green triangles, Megaluracarus urbanus; green hyphens, Megaluracarus zitavus.
Figure 5 in Geometric and traditional morphometrics for the assessment of character state identity: multivariate statistical analyses of character variation in the genus Arrenurus (Acari, Hydrachnidia, Arrenuridae)
Figure 5. Interlandmark distances (1–36) collected in dorsal (A, B) and posterior (C) views: (A) measurements 1–13 assembled for the idiosoma (distance set 1; (B) distances 14–28 measured between postocularia R2 and dorsoglandularia D2, D3, and D4 (distance set 2); (C) measurements 29–36 between ventroglandularia V1, V2, and V3 (distance set 3). Lines indicate the interlandmark distances sampled and numbers identify each distance, as listed in Table 3. Numbers with asterisks label distances for which the mean was calculated with the measurements of left and right idiosoma sides.
Figure 1 in Geometric and traditional morphometrics for the assessment of character state identity: multivariate statistical analyses of character variation in the genus Arrenurus (Acari, Hydrachnidia, Arrenuridae)
Figure 1. Species of Arrenurus (Megaluracarus) included in the morphometric analyses, in dorsal view: (A) Arrenurus anae; (B) Arrenurus colitus; (C) Arrenurus neoexpansus; (D) Arrenurus zitavus; (E) Arrenurus maya; (F) Arrenurus catoi; (G) Arrenurus tabascoensis; (H) Arrenurus anitahoffmannae; (I) Arrenurus urbanus; (J) Arrenurus olmeca; (K) Arrenurus costeroae. The pairs of postocularia R2 and dorsoglandularia D2, D3, and D4 in (F) are indicated with arrows pointing at gland openings and setae insertions. Scale bars: 100 µm.
Figure 9 in Geometric and traditional morphometrics for the assessment of character state identity: multivariate statistical analyses of character variation in the genus Arrenurus (Acari, Hydrachnidia, Arrenuridae)
Figure 9. Shape variation of dorsoglandularia D4 in dorsal view (data set 4). (A) scatter plot of canonical variate scores (root 1 vs. root 2). (B) overall pattern of shape similarity among 11 Megaluracarus species and two Dadayella species based on Mahalanobis distances computed from the canonical variate analysis. This UPGMA phenogram (unweightedpair grouping method using averages) groups the three character states discovered in the dorsoglandularia. Branches are labelled according to discrimination order defined by the canonical variates. Symbols in the plot and in the phenogram are as listed in Fig. 8.
Figure 10 in Geometric and traditional morphometrics for the assessment of character state identity: multivariate statistical analyses of character variation in the genus Arrenurus (Acari, Hydrachnidia, Arrenuridae)
Figure 10. Shape variation of ventroglandularia V1, V2 and V3 (data set 5). (A) scatter plot of canonical variate scores (root 1 vs. root 2); shape changes relative to the mean shape are shown for both roots. (B) overall pattern of shape similarity among 11 Megaluracarus species and two Dadayella species based on Mahalanobis distances computed from the canonical variate analysis. This UPGMA phenogram (unweighted-pair grouping method using averages) groups the nine character states discovered in the distribution patterns of ventroglandularia. Branches are labelled according to discrimination order defined by the canonical variates. Symbols in the plot and in the phenogram are as follows: solid green triangles, Arrenurus (Dadayella) adrianae; solid red rhombuses, Dadayella aztecus; lilac en-dashes, Arrenurus (Megaluracarus) anae; open blue circles, Megaluracarus anitahoffmannae; horizontal blue lines, Megaluracarus catoi; open red squares, Megaluracarus colitus; solid grey squares, Megaluracarus costeroae; open green rhombuses, Megaluracarus maya; solid black circles, Megaluracarus neoexpansus; blue asterisks, Megaluracarus olmeca; green hyphens, Megaluracarus tabascoensis; open pink triangles, Megaluracarus urbanus; purple plus signs, Megaluracarus zitavus.
Figure 3 in Geometric and traditional morphometrics for the assessment of character state identity: multivariate statistical analyses of character variation in the genus Arrenurus (Acari, Hydrachnidia, Arrenuridae)
Figure 3. Two Arrenurus (Dadayella) species included for comparison with the 11 species of Arrenurus (Megaluracarus) in the morphometric analyses: (A, B) Dadayella adrianae in dorsal and posterior view, respectively; (C, D) Dadayella aztecus in dorsal and posterior view, respectively. The arrows in (C) and (D) are as described in Figs 1F and 2F, respectively. Scale bars: 100 µm.
Data from: Quantifying (non)parallelism of microbial community change using multivariate vector analysis
<p>Parallel evolution of phenotypic traits is regarded as strong evidence for natural selection and has been studied extensively in a variety of taxa. However, we have limited knowledge of whether parallel evolution of host organisms is accompanied by parallel changes of their associated microbial communities (i.e., microbiotas), which are crucial for their hosts' ecology and evolution. Determining the extent of microbiota parallelism in nature can improve our ability to identify the factors that are associated with (putatively adaptive) shifts in microbial communities. While it has been emphasized that (non)parallel evolution is better considered as a quantitative continuum rather than a binary phenomenon, quantitative approaches have rarely been used to study microbiota parallelism. We advocate using multivariate vector analysis (i.e., phenotypic change vector analysis) to quantify direction and magnitude of microbiota changes and discuss the applicability of this approach for studying parallelism. We exemplify its use by reanalyzing gut microbiota data from multiple fish species that exhibit parallel shifts in trophic ecology. This approach provides an analytical framework for quantitative comparisons across host lineages, thereby providing the potential to advance our capacity to predict microbiota changes. Hence, we encourage the development and application of quantitative measures, such as multivariate vector analysis, to better understand the role of microbiota dynamics during their hosts' adaptive evolution, particularly in settings of parallel evolution.</p>
Density-dependent patterns of multivariate selection on sperm motility and morphology in a broadcast spawning mussel
<p>Sperm cells exhibit extraordinary phenotypic variation, both among taxa and within individual species, yet our understanding of the adaptive value of sperm trait variation across multiple contexts is incomplete. For species without the opportunity to choose mating partners, such as sessile broadcast spawning invertebrates, fertilisation depends on gamete interactions, which in turn can be strongly influenced by local environmental conditions that alter the concentration of sperm and eggs. However, the way in which such environmental factors impact phenotypic selection on functional gamete traits remains unclear in most systems. Here, we analyse patterns of linear and nonlinear multivariate selection under experimentally altered local sperm densities (densities within the capture zone of eggs) on a range of functionally important sperm traits in the broadcast spawning marine mussel, <i>Mytilus galloprovincialis</i>. Specifically, we assay components of sperm motility and morphology across two fertilisation environments that simulate either sperm limitation (when there are too few sperm to fertilise all available eggs), or sperm saturation (when there are many more sperm than required for fertilisation, and the risk of polyspermy and embryonic failure is heightened). Our findings reveal that the strength, form, and targets of selection on sperm depend on the prevailing fertilisation environment. In particular, our analyses revealed multiple significant axes of nonlinear selection on sperm motility traits under sperm limitation, but only significant negative directional selection on flagellum length under sperm saturation. These findings highlight the importance of local sperm densities in driving the adaptation of sperm phenotypes, particularly those related to sperm motility, in broadcast spawning invertebrates.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.