A dataset of iris images synthesized using PCA and super-resolution.

Premise: When divergent lineages come into secondary contact reproductive isolation may be incomplete, thus providing an opportunity to investigate how speciation is manifested in the genome. The Louisiana Irises (Iris, series Hexagonae) comprise a group of three or more ecologically and reproductively divergent lineages that can produce hybrids where they come into contact. In this study we sought to estimate standing genetic variation to understand the current distribution of population structure in the Louisiana Irises.

Methods: We used genotyping-by-sequencing techniques to sample the genomes of Louisiana Iris species across their ranges. Twenty populations were sampled (total n=632) across 11,249 loci. Population genetic data were assessed using ENTROPY and PCA models.

Results: We discovered evidence for interspecific gene flow in parts of the range and revealed patterns of population structure at odds with widely accepted nominal taxonomy. Undescribed hybrid populations w...

Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis/Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though computationally intensive alternative for model selection. Application of the algorithm is illustrated using Fisher's iris data set and Howells' craniometric data set. Mixed ICA/PCA is of potential interest in any field of scientific investigation where the authenticity of blindly separated non-Gaussian sources might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian.

Samples of 0.001 and 0.01%, for the smaller iris and miRNA data sets of 1% and 10%, of the data were drawn once using uniform sampling or 1,000 times using uniform sampling with different seeds, followed by selection of the sample that best matched the original distribution of variables, judged by statistical comparisons of probability density functions. The sampled data were subjected to projection using either PCA or a single-layer autoencoder, and then the projection parameters were used to predict the remaining data that had not been sampled from the original data set. The experiments were performed in 20 replicates starting with different and non-redundant seeds, and the means and standard deviations of the mean square errors of the data reproduction obtained during these replicates are shown.

Despite their crucial role in determining the fate of seeds, the type and breaking mode of seed dormancy in peatland plants in temperate Asia with a continental monsoon climate are rarely known. Fifteen common peatland plant species were used to test their seed germination response to various dormancy-breaking treatments, including dry storage (D), gibberellin acid soaking (GA), cold stratification (CS), warm followed cold stratification (WCS), GA soaking + cold stratification (GA+CS) and GA soaking + warm followed cold stratification (GA+WCS). Germination experiment, viability and imbibition test, and morphological observation of embryos were conducted. Of the 15 species, nine showed physiological dormancy (PD), with non-deep PD being the dominant type. Four species, Angelica pubescens, Cicuta virosa, Iris laevigata and Iris setosa exhibited morphological physiological dormancy. Two species, Lycopus uniflorus and Spiraea salicifolia, demonstrated non-dormancy of seeds. Overall, the effect hierarchy of dormancy-breaking is: CS > GA > WCS > GA+CS > D > GA+WCS. Principal component analysis demonstrated that seed traits, including embryo length: seed length ratio, seed size, and monocot/eudicot divergence, are more likely to influence seed dormancy than environmental factors. Our study suggests that nearly 90% of the tested peatland plant species in the Changbai Mountains demonstrated seed dormancy, and seed traits (e.g. embryo to seed ratio and seed size) and abiotic environmental factors (e.g. pH and temperature seasonality) are related to germination behavior, suggesting seed dormancy being a common adaptation strategy for the peatland plants in the temperate montane environment. Methods All statistical analyses were conducted in R v 4.2.3. Generalized Linear Models (GLMs) were used to analyze the effect of dormancy-breaking treatments (explained factor) on germination (GP on the 28th day, dependent factor). The significance of factors for each experiment was assessed by Wald Chi-Square statistics to the model. Duncan's test was used for multiple comparisons. The difference between germination percentage of control and initial viability was determined by Mann-Whitney-U-tests, to determine whether the seeds were dormant or not. Principal components analysis (PCA) was used to reduce and visualize the variability in the species’ seed germination response and to intercorrelations among seed traits or environmental factors. The PCA was carried out with the package ‘FactoMineR’ using the variance-covariance matrix. The significance level was set to α = 0.05.

Component matrix for principal component analysis of male and female face ratings including dominance ratings.

Principal component analysis.