Despite their interest and threat status, the number of whales in world's oceans remains highly uncertain. Whales detection is normally carried out from costly sighting surveys, acoustic surveys or through high-resolution orthoimages. Since deep convolutional neural networks (CNNs) achieve great performance in object-recognition in images, here we propose a robust and generalizable CNN-based system for automatically detecting and counting whales from space based on open data and tools. A test of the system on Google Earth images in ten global whale-watching hotspots achieved a performance (F1-measure) of 84% in detecting and 97% in counting 80 whales. Applying this cost-effective method worldwide could facilitate the assessment of whale populations to guide conservation actions.Free and global access to high-resolution imagery for conservation purposes would boost this process.

Fluorescent in situ hybridization (FISH) using X and Y chromosome-specific alpha satellite DNA probes hybridizing to loci DXZ1 and DYZ3 was performed to identify the origin of ring/marker chromosomes in 6 patients with Ullrich-Turner syndrome (UTS). All patients had a mosaic karyotype, 5 with 45,X/46,X,r(?) and one with 45,X/46,X,mar. We demonstrated that the marker/ring chromosome in each of these 6 patients originated from the X. A timely knowledge of the X or Y origin of ring and marker chromosomes can be crucial in genetic counseling and medical management since the presence of Y chromosome material in phenotypic females is known to increase the risk for developing gonadoblastoma.