Menu

Image of the Day: ButterflyNet

Scientists used machine learning to analyze the coevolution of physical traits in butterflies.

Aug 16, 2019
Nicoletta Lanese

ABOVE: Co-mimic pairs from the species Heliconius erato (odd columns) and Heliconius melpomene (even column) sorted by greatest similarity from top left to bottom right.
J HOYAL CUTHILL

A machine learning algorithm revealed that coevolving butterflies “borrow” physical features from each other, such as wing shape and pattern, and use them to generate novel features over time, researchers reported August 14 in Science Advances. The scientists set out to test a model known as Müllerian mimicry, which proposes that species sometimes mimic each other to glean mutual benefits; for instance, a predator might mistake an edible butterfly for a toxic one if the insects look similar, which softens the effect of predation on both species. In the past, it was difficult to quantify phenotypic similarity and thereby test the theory.

“Machine learning is allowing us to enter a new phenomic age, in which we are able to analyse biological phenotypes?—what species actually look like?—at a scale comparable to genomic data,” says coauthor Jennifer Hoyal Cuthill, an evolutionary biologist with joint positions at the University of Cambridge, University of Essex, and Tokyo Institute of Technology, in an announcement. After training their algorithm ButterflyNet on 2,400 photographs of Heliconius butterflies, the researchers found that the insects both closely copy each other and generate brand-new traits from borrowed ones, thus creating biological diversity.

J.F.H. Cuthill et al., “Deep learning on butterfly phenotypes tests evolution’s oldest mathematical model,” doi:10.1126/sciadv.aaw4967, Sci Adv, 2019.

Nicoletta Lanese is an intern at The Scientist. Email her at nlanese@the-scientist.com.

September 2019

Our Inner Neanderthal

Ancient secrets in the human genome

Marketplace

Sponsored Product Updates

Evaluating the Functional Potency of Immunotherapies Targeting Tumors of B Cell Origin
Evaluating the Functional Potency of Immunotherapies Targeting Tumors of B Cell Origin
Download this application note to learn how the ACEA xCELLigence system can evaluate the potency of an immunotherapy against a broad spectrum of liquid tumors and monitor the destruction kinetics of liquid cancers at physiologically relevant effector:target cell ratios.
Kinetics of Tumor Cell Killing by Tumor-Infiltrating Leukocytes
Kinetics of Tumor Cell Killing by Tumor-Infiltrating Leukocytes
Watch this webinar to learn how Dr. Cara Haymaker and her team are assessing the reactivity of tumor-infiltrating lymphocytes and modulating the mechanisms of resistance to therapy and exploring biomarkers involved in the response.
Atlas Antibodies Presents QPrEST Standards for Absolute Quantification of Proteins using Mass Spectrometry
Atlas Antibodies Presents QPrEST Standards for Absolute Quantification of Proteins using Mass Spectrometry
Atlas Antibodies AB, a leading supplier of advanced research reagents, announced today the introduction of pre-quantified QPrEST™ Protein Standards for absolute quantification of proteins in biological samples such as cell lysate and plasma using liquid chromatography (LC)–mass spectrometry (MS).
BIA Separations introduces Cornerstone AAV Process Development Service to accelerate gene therapy production
BIA Separations introduces Cornerstone AAV Process Development Service to accelerate gene therapy production
CORNERSTONE program integrates process development expertise and novel technology to remove development bottlenecks in the manufacture of Gene Therapy Medicinal Products (GTMPs). Portfolio includes novel CIMasphere™ technology for higher yielding processes and safer products in AAV-based programs