The processes of morphogenesis during animal development are complex and interdependent at all organisational levels. Thus, it is beneficial to visualize morphogenesis at different scales and including 3D imaging and quantification. Here the application of a combination of complementary imaging techniques (micro-computed tomography, histology, and transmission electron microscopy), supplemented by morphometrical analyses, is presented in a study of digestive glands morphogenesis during embryonic and postembryonic development of isopod crustacean. The first pair of gland tubules is formed in mid-stage embryo from gland primordium, separated into two lobes by gland epithelium longitudinally from posterior to anterior direction. The width reduction and volume decrease of the tubules were observed from late embryo through marsupial and postmarsupial mancae stages. The second pair of gland tubules starts to form in mid-stage embryo S14 and elongates from late embryo onwards, with gradual volume increase. Epithelial cells of digestive glands are morphologically modified from cuboidal to the dome-shaped B cells and wedge-shape S cells in late marsupial mancae, which coincides with complete depletion of yolk within gland lumen. Septate junctions in gland epithelium elongate from embryos to postmarsupial mancae, while their ultrastructure does not change considerably. The most intense elongation of septate junction was evident at transition from marsupial to postmarsupial manca stage, which is consistent with release of the animal from marsupium to the external environment. Integration of data acquired by the presented imaging techniques and quantitative analyses allowed us to relate histological and ultrastructural modifications of epithelium to crucial transitions in digestive gland morphogenesis and to the key steps of animal embryonic and postembryonic development.
Correlative light and electron microscopy (CLEM) combines the molecular specificity of fluorescence light microscopy with the ultrastructural resolution of electron microscopy (EM), enabling molecularly defined cellular structures to be localised and interpreted within their cellular ultrastructural context. Although cryo-CLEM, super-resolution CLEM, and volume EM have substantially expanded the spatial, molecular, and three-dimensional capabilities of CLEM, their routine implementation remains constrained by specialised instrumentation, demanding sample-preparation workflows, and the need for expertise in image acquisition, registration, and analysis. This review therefore focuses on conventional CLEM strategies that are accessible to laboratories equipped with standard widefield or confocal fluorescence microscopes and transmission electron microscopes. Pre-embedding and post-embedding approaches are compared, highlighting how the timing of labelling and fluorescence imaging influences key trade-offs among live-cell imaging, correlation accuracy, probe preservation, and ultrastructural integrity. Probes used in pre-embedding workflows are further evaluated, including dual-modality probes such as FluoroNanogold antibodies and quantum dots, fluorescent proteins like GFP (rendered EM-visible through secondary immunolabelling or photoconversion), and luminescent metal complexes, alongside selected protocol examples for both pre-embedding and post-embedding experimental approaches. Overall, successful CLEM depends on careful experimental design, appropriate probe selection, and optimised sample preparation. Conventional workflows remain practical and robust options for routine cell biology studies, particularly when accessibility, reproducibility, and compatibility with existing microscopy infrastructure are priorities.
A variety of methodologies are available for the study of cell migration in vitro which has major function in physiological and pathophysiological processes even in cancer metastasis. The in vitro scratch assay is widely utilized for cell migration analysis. The straightforward, simple, and inexpensive procedure is performed manually; demonstrated to exhibit a high degree of similarity to the in vivo migratory behavior of cells. The scratch area is approximated to a rectangle or averaged to a certain number of parallel distances, performed by the analyst. Being laborious and time-consuming, the visual evaluation process is contingent upon the researcher’s professional expertise, leading to subjective outcomes in the results. Furthermore, factors such as differing experimental setups, imaging equipment and brightness differences in the images can also lead to subjective outcomes. In order to circumvent inherent subjectivity, utilization of fully automatic software is proposed for the segmentation and quantification of scratch assay micrographs. The proposed software’s algorithm is founded upon the principles of morphological image processing and rank filtering, encompassing a series of image processing steps. The results demonstrate that the software generates reproducible outputs even though parameters of the image such as brightness and contrast change. The algorithm offers a novel perspective in this field and functions as a robust and user-friendly computational instrument for end users, thereby reducing the subjectivity of the results.
Weibel-Palade (WP) bodies are often rod shaped endothelial cell organelles playing a role in blood coagulation processes. In 1962, E.R. Weibel first observed a longitudinal transect of one such organelle in an electron micrograph, measuring about 0.06 µm × 1.0 µm. He noticed that the a priori probability of such an event - analogous to that of hitting both ends of a rod by a random plane - should be "extremely low". The main purpose of this note is to make an educated guess of that probability - more precisely, the expected proportion of transects affecting both bases of a finite right circular cylinder, among all possible isotropic and uniform random (IUR) slabs, or planes, hitting the cylinder.
UAV photogrammetry constitutes a fundamental technology for the lifecycle maintenance and digital twin construction of large-scale transport infrastructure. However, standard Structure-from-Motion (SfM) pipelines frequently falter in scenarios characterized by weak textures, such as asphalt, and repetitive patterns. This deficiency leads to severe feature ambiguity and sparse reconstruction voids in large-scale infrastructure scenes. Furthermore, existing deep learning descriptors typically neglect explicit spatial attributes and suffer from the computational burden of quadratic-complexity attention mechanisms, hindering deployment on edge devices. Building on the FeatureBooster-style descriptor enhancement paradigm, this study adapts a lightweight geometry-aware reconstruction framework to unmanned aerial vehicle infrastructure inspection. The methodology integrates a dual-stream descriptor enhancement model. Following the descriptor-boosting idea of combining local descriptors with geometric keypoint attributes, the adapted model embeds spatial attributes into the feature space to alleviate isomorphic ambiguity. Meanwhile, the modified cross-perception module replaces the original Attention-Free Transformer (AFT) -Simple setting with AFT-Full and incorporates SwiGLU to capture global context efficiently. Experiments on real-world datasets of complex interchanges, urban highways, and campus scenes validate that the adapted descriptor enhancement strategy effectively reduces point cloud voids and reduces reprojection error by approximately 17.3% compared with the original SIFT baseline on Dataset 1. Notably, this study empirically identifies an efficiency compensation phenomenon, wherein superior feature quality accelerates downstream geometric verification and optimization stages. Consequently, although feature enhancement introduces marginal overhead, the overall reconstruction time is reduced in certain datasets. This work provides an application-oriented adaptation and evaluation of FeatureBooster-style descriptor enhancement for geometry-consistent and computationally efficient infrastructure digitalization.
