On the edge: Disease and habitat loss is decimating wild amphibian populations globally, with more than 200 species needing urgent intervention through captive breeding, says Dr. Simon Clulow. In a south-eastern suburb in Melbourne, there’s a zoo. It has no visitors, and there are no animals anywhere inside it. Rather, the Australian Frozen Zoo houses living cells and genetic material from Australian native and rare and exotic species. This place, and others like it, could be a big part of the future of conservation. Department of Biological Sciences’ Simon Clulow and his colleagues make the case for ‘biobanking’ in a recent piece in Conservation Letters. Clulow is keen to stress that this doesn’t mean getting rid of conventional zoos or captive breeding programs. “Captive breeding has had some wonderful successes, and there will always be a huge place for it,” he says. PhD student and lead author Lachlan Howell agrees. “It was captive breeding that brought the giant panda back from
Low-dimensional uniform manifold approximation projection showing symmetry-aware image similarity from a database of greater than 25,000 piezoresponse force microscopy images. Credit: Joshua Agar/Lehigh University A novel neural network to understand symmetry, speed materials research. Using a large, unstructured dataset gleaned from 25,000 images, scientists demonstrate a novel machine learning technique to identify structural similarities and trends in materials for the first time. Understanding structure-property relations is a key goal of materials research, according to Joshua Agar, a faculty member in Lehigh University’s Department of Materials Science and Engineering. And yet currently no metric exists to understand the structure of materials because of the complexity and multidimensional nature of structure. Artificial neural networks, a type of machine learning, can be trained to identify similarities―and even correlate parameters such as structure and properties―but there a