Investigation of the morphological cell structures and their optical significances of Aeshna cyanea.

The transparent wing of the dragonfly Aeshna cyanea has been investigated using scanning electron microscopy (SEM), optical microscopy (OPM), energy-dispersive X-ray spectroscopy (EDS) and reflectance spectroscopy. Four cells (D1-D4) were studied and classified according to their general morphology. The OPM depicted the vein-joint characterised by the distribution of resilin. EDS technique showed common elements such as carbon, oxygen, and chlorine. SEM analysis revealed thin membranes reinforced with a network of hallow veins. Spikes and round shape of microstructures were identified. The roughness of the pruinosity was estimated, which indicates the shape and curvature of the microstructures that essentially play a significant role in the optical response observed. The study can be essential to design and improve micro-air vehicles.

The mechanoreceptors on the endophytic ovipositor of the dragonfly Aeshna cyanea (Odonata, Aeshnidae).

This study investigates the mechanoreceptors located on the cutting valvulae of the ovipositor of the dragonfly Aeshna cyanea (Aeshnidae), using both SEM and TEM, with the aim of providing an overview of the sensory equipment of an odonatan endophytic ovipositor. Four kinds of sensilla have been described. Notwithstanding their different external and internal morphology, they show features typical of mechanoreceptors. Three of them are evident along the external surface of the two cutting valvulae in the form of sub-spherical pegs, pit organs type 1 (holes) and pit organs type 2 (depressions), these last are similar to amphinematic scolopidia, while the fourth type is represented by subintegumental mononematic scolopidia having no direct relationship with the cuticle. In spite of their structural differences, the morphology of the described mechanoreceptors is consistent with performing a main role in allowing the perception of compression/stretching of the thick cuticle of the valvulae and their bending due to the pressure acting on the distal end of the ovipositor during substrate penetration. Such an organization is coherent with the need of endophytic Odonata to be able to evaluate the stiffness of the plant where to lay eggs.

The antennal lobe of Libellula depressa (Odonata, Libellulidae).

Here we describe the antennal lobe of Libellula depressa (Odonata, Libellulidae), identified on the basis of the projections of the afferent sensory neurons stemming from the antennal flagellum sensilla. Immunohistochemical neuropil staining as well as antennal backfills revealed sensory neuron terminal arborizations covering a large portion of the antennal lobe. No clear glomerular structure was identified, thus suggesting an aglomerular antennal lobe condition as previously reported in Palaeoptera. The terminal arbors of backfilled sensory neurons do, however, form spherical knots, probably representing the connections between the few afferent neurons and the antennal lobe interneurons. The reconstruction revealed that the proximal part of the antennal nerve is divided into two branches that innervate two spatially separated areas of the antennal lobe, an anterioventral lobe and a larger posteriodorsal lobe. Our data are consistent with the hypothesis that one tract of the antennal nerve of L. depressa contains olfactory sensory neurons projecting into one of the sublobes, while the other tract contains thermo-hygroreceptive neurons projecting into the other sublobe.

A distance-field based automatic neuron tracing method.

BACKGROUND: Automatic 3D digital reconstruction (tracing) of neurons embedded in noisy microscopic images is challenging, especially when the cell morphology is complex. RESULTS: We have developed a novel approach, named DF-Tracing, to tackle this challenge. This method first extracts the neurite signal (foreground) from a noisy image by using anisotropic filtering and automated thresholding. Then, DF-Tracing executes a coupled distance-field (DF) algorithm on the extracted foreground neurite signal and reconstructs the neuron morphology automatically. Two distance-transform based "force" fields are used: one for "pressure", which is the distance transform field of foreground pixels (voxels) to the background, and another for "thrust", which is the distance transform field of the foreground pixels to an automatically determined seed point. The coupling of these two force fields can "push" a "rolling ball" quickly along the skeleton of a neuron, reconstructing the 3D cell morphology. CONCLUSION: We have used DF-Tracing to reconstruct the intricate neuron structures found in noisy image stacks, obtained with 3D laser microscopy, of dragonfly thoracic ganglia. Compared to several previous methods, DF-Tracing produces better reconstructions.