Neuroendocrine cells are present in virtually all organs of the vertebrate body; however, it is yet uncertain whether they exist in the ovaries. Previous reports of ovarian neurons and neuron-like cells in mammals and birds might have resulted from misidentification. The aim of the present work was to determine the identity of neuron-like cells in immature ovaries of the domestic fowl. Cells immunoreactive to neurofilaments, synaptophysin, and chromogranin-A, with small, dense-core secretory granules, were consistently observed throughout the sub-cortical ovarian medulla and cortical interfollicular stroma. These cells also displayed immunoreactivity for tyrosine, tryptophan and dopamine b-hydroxylases, as well as to aromatic L-DOPA decarboxylase, implying their ability to synthesize both catecholamines and indolamines. Our results support the argument that the ovarian cells previously reported as neuron-like in birds, are neuroendocrine cells.

Digital Elevation Models (DEMs) are used to derive information from the morphology of a land. The topographic attributes obtained from the DEM data allow the construction of watershed delineation useful for predicting the behavior of systems and for studying hydrological processes. Imagery acquired from Unmanned Aerial Vehicles (UAVs) and 3D photogrammetry techniques offer cost-effective advantages over other remote sensing methods such as LIDAR or RADAR. In particular, a high spatial resolution for measuring the terrain microtopography. In this work, we propose a Structure from Motion (SfM) pipeline using UAVs for generating high-resolution, high-quality DEMs for developing a rainfall-runoff model to study flood areas. SfM is a computer vision technique that simultaneously estimates the 3D coordinates of a scene and the pose of a camera that moves around it. The result is a 3D point cloud which we process to obtain a georeference model from the GPS information of the camera and ground control points. The pipeline is based on open source software OpenSfM and OpenDroneMap. Encouraging experimental results on a test land show that the produced DEMs meet the metrological requirements for developing a surface-runoff model.

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