What is a β cell? - Chapter I in the Human Islet Research Network (HIRN) review series

BACKGROUND: The pancreatic β cell, as the sole source of the vital hormone insulin, has been under intensive study for more than a century. Given the potential of newly created insulin-producing cells as a treatment or even cure of type 1 diabetes (T1D) and possibly in severe cases of type 2 diabetes (T2D), multiple academic and commercial laboratories are working to derive surrogate glucose-responsive, insulin-producing cells. SCOPE OF REVIEW: The recent development of advanced phenotyping technologies, including molecular, epigenomic, histological, or functional, have greatly improved our understanding of the critical properties of human β cells. Using this information, here we summarize the salient features of normal, fully functional adult human β cells, and propose minimal criteria for what should rightfully be termed 'β cells' as opposed to insulin-producing but not fully-functional surrogates that we propose should be referred to as 'β-like' cells or insulin-producing cells. MAJOR CONCLUSIONS: Clear criteria can be established to differentiate fully functional, mature β cells from 'β-like' surrogates. In addition, we outline important knowledge gaps that must be addressed to enable a greater understanding of the β cell

Fast semi-supervised segmentation of in-situ tree color images

International audienceIn this paper we present an original semi-supervised method for the segmentation of in situ tree color images which combines color quantization, adaptive fragmentation of learning areas defined by the human operator and labeling propagation. A mathematical morphology post-processing is introduced to emphasize the narrow and thin structures which characterize branches. Applied in the L*a*b* color system, this method is well adapted to easily adjust the learning set so that the resultant labeling corresponds to the accuracy achieved by the human operator. The method has been embarked and evaluated on a tablet to help tree professionals in their expertise or diagnosis. The images, acquired and processed with a mobile device, present more or less complex background both in terms of content and lightness, more or less dense foliage and more or less thick branches. Results are good on images with soft lightness without direct sunlight