In this work, we present an experimental setup and guide to enable the perching of large flapping-wing robots. The combination of forward flight, limited payload, and flight oscillations imposes challenging conditions for localized perching. The described method details the different operations that are concurrently performed within the 4 second perching flight. We validate this experiment with a 700 g ornithopter and demonstrate the first autonomous perching flight of a flapping-wing robot on a branch. This work paves the way towards the application of flapping-wing robots for long-range missions, bird observation, manipulation, and outdoor flight.

Teams of researchers across 18 institutes spread over 11 countries have worked together to assess the utility of AlphaFold2 (AF2) predictions in the analysis of distinctive structural elements, the impact of missense variants, the prediction of function and ligand binding sites, the modeling of interactions, and the modeling of experimental structural data. A significant biological macromolecule involved in every cellular activity is the protein. It is crucial for interaction, protein function, and how missense mutations (point mutations where a single nucleotide change results in a codon that codes for a different amino acid) can affect a protein's functionality. The primary structure of amino acids, through protein folding, forms a three-dimensional tertiary or quaternary structure. Although the experimental methods for figuring out protein structures have advanced tremendously, most of the protein universe remained unidentified.

Proteins are the building blocks for all living things, providing structure and managing processes in cells. Understanding how these molecules fold into specific 3D shapes is key to understanding their function but requires expensive equipment and lots of time, limiting the progress of research and development. A new artificial intelligence programme called AlphaFold has been shown to accurately predict protein structure in minutes, solving a decades old challenge. Its success is built on the availability of thousands of experimentally determined protein structures, a result of long-term research funding, infrastructure investment and data-sharing policies. DeepMind, the developers of AlphaFold, have made the AlphaFold code and protein structure predictions openly available to the global scientific community.

If you are even remotely interested in science, you will have probably already heard about DeepMind's latest leap. Their AI system Alphafold 2 has cracked predicting proteins' 3D structure. There are plenty of great articles about it. Since I have written about machine learning/AI in an earlier series of posts, I decided to write a brief post about this development as well. For more details, do check the Nature/New Scientist/DeepMind articles linked above.

It's these genetic definitions that circumscribe their three-dimensional structures, which in turn determines their capabilities. But protein "folding," as it's called, is notoriously difficult to figure out from a corresponding genetic sequence alone. DNA contains only information about chains of amino acid residues and not those chains' final form. In December 2018, DeepMind attempted to tackle the challenge of protein folding with a machine learning system called AlphaFold. The product of two years of work, the Alphabet subsidiary said at the time that AlphaFold could predict structures more precisely than prior solutions.