Elliptical Fourier descriptors of outline and morphological analysis in caudal view of foramen magnum of the tropical raccoon (Procyon cancrivorus) (Linnaeus, 1758)

Compromised bilaterality in the small African pangolin (Phataginus tricuspis) - An expression of or compensation for developmental errors; asymmetry, elliptical Fourier analyses and modularity

We present a postnatal evaluation of skull developmental signaling in small African pangolin emphasizing structural, and cognitive trend in ontogeny for assessment of developmental instability, proper identification and classification, forty digitally processed skulls and foramen magnum from different geo-locations were assessed for asymmetry, foramen magnum shape and modularity using geometric and Elliptical Fourier analyses. Multivariate analysis of regression demonstrated low (p < 0.5) but directional fluctuating asymmetry (F₁₅₃₉ = 3.4045, F₈₈₂ = 3.2665, dorsal and ventral views). Allometric trajectories followed rostrocaudal, caudolateral directions; Intercepts for shape/size predictions were parallel. Mahalanobis distances between centroids (2.42) were significant (p < 0.01). The variance-covariance matrix in ontogeny lies between 0.0017 and 0.56. Foramen magnum outline descriptors by incremental harmonics revealed first 4 effective PCs defined 96.98% of shape properties and 3.02% constituted finerdetails. 74.1% accuracy decline after size factor elimination. Modulation PCA of covariance matrix and asymmetry component was 88.38% and 7.48% (PC1 and 2), respectively, the variance % predicted 10.08%. The studied samples confirmed 'handedness' and fluctuating asymmetries. Foramen magnum shape assumed priority over size in ontogeny with profound asymmetry (from the 5th harmonic), suggestive of compensations to lateralization in neural integration modules.

MOrgAna: accessible quantitative analysis of organoids with machine learning

Recent years have seen a dramatic increase in the application of organoids to developmental biology, biomedical and translational studies. Organoids are large structures with high phenotypic complexity and are imaged on a wide range of platforms, from simple benchtop stereoscopes to high-content confocal-based imaging systems. The large volumes of images, resulting from hundreds of organoids cultured at once, are becoming increasingly difficult to inspect and interpret. Hence, there is a pressing demand for a coding-free, intuitive and scalable solution that analyses such image data in an automated yet rapid manner. Here, we present MOrgAna, a Python-based software that implements machine learning to segment images, quantify and visualize morphological and fluorescence information of organoids across hundreds of images, each with one object, within minutes. Although the MOrgAna interface is developed for users with little to no programming experience, its modular structure makes it a customizable package for advanced users. We showcase the versatility of MOrgAna on several in vitro systems, each imaged with a different microscope, thus demonstrating the wide applicability of the software to diverse organoid types and biomedical studies.