Envisaging a global infrastructure to exploit the potential of digitised collections.

Tens of millions of images from biological collections have become available online over the last two decades. In parallel, there has been a dramatic increase in the capabilities of image analysis technologies, especially those involving machine learning and computer vision. While image analysis has become mainstream in consumer applications, it is still used only on an artisanal basis in the biological collections community, largely because the image corpora are dispersed.

Cell wall polysaccharide and glycoprotein content tracks growth-form diversity and an aridity gradient in the leaf-succulent genus Crassula.

Cell wall traits are believed to be a key component of the succulent syndrome, an adaptive syndrome to drought, yet the variability of such traits remains largely unknown. In this study, we surveyed the leaf polysaccharide and glycoprotein composition in a wide sampling of Crassula species that occur naturally along an aridity gradient in southern Africa, and we interpreted its adaptive significance in relation to growth form and arid adaptation. To study the glycomic diversity, we sampled leaf material from 56 Crassula taxa and performed comprehensive microarray polymer profiling to obtain the relative content of cell wall polysaccharides and glycoproteins.