Tag Archives: computer simulations

Brown recluse spider, one of the world’s most venomous spiders, shows off unique spinning technique

Caption: American Brown Recluse Spider is pictured. Credit: Oxford University

According to scientists from Oxford University this deadly spider could teach us a thing or two about strength. From a Feb. 15, 2017 news item on ScienceDaily,

Brown recluse spiders use a unique micro looping technique to make their threads stronger than that of any other spider, a newly published UK-US collaboration has discovered.

One of the most feared and venomous arachnids in the world, the American brown recluse spider has long been known for its signature necro-toxic venom, as well as its unusual silk. Now, new research offers an explanation for how the spider is able to make its silk uncommonly strong.

Researchers suggest that if applied to synthetic materials, the technique could inspire scientific developments and improve impact absorbing structures used in space travel.

The study, published in the journal Material Horizons, was produced by scientists from Oxford University’s Department of Zoology, together with a team from the Applied Science Department at Virginia’s College of William & Mary. Their surveillance of the brown recluse spider’s spinning behaviour shows how, and to what extent, the spider manages to strengthen the silk it makes.

A Feb. 15, 2017 University of Oxford press release, which originated the news item,  provides more detail about the research,

From observing the arachnid, the team discovered that unlike other spiders, who produce round ribbons of thread, recluse silk is thin and flat. This structural difference is key to the thread’s strength, providing the flexibility needed to prevent premature breakage and withstand the knots created during spinning which give each strand additional strength.

Professor Hannes Schniepp from William & Mary explains: “The theory of knots adding strength is well proven. But adding loops to synthetic filaments always seems to lead to premature fibre failure. Observation of the recluse spider provided the breakthrough solution; unlike all spiders its silk is not round, but a thin, nano-scale flat ribbon. The ribbon shape adds the flexibility needed to prevent premature failure, so that all the microloops can provide additional strength to the strand.”

By using computer simulations to apply this technique to synthetic fibres, the team were able to test and prove that adding even a single loop significantly enhances the strength of the material.

William & Mary PhD student Sean Koebley adds: “We were able to prove that adding even a single loop significantly enhances the toughness of a simple synthetic sticky tape. Our observations open the door to new fibre technology inspired by the brown recluse.”

Speaking on how the recluse’s technique could be applied more broadly in the future, Professor Fritz Vollrath, of the Department of Zoology at Oxford University, expands: “Computer simulations demonstrate that fibres with many loops would be much, much tougher than those without loops. This right away suggests possible applications. For example carbon filaments could be looped to make them less brittle, and thus allow their use in novel impact absorbing structures. One example would be spider-like webs of carbon-filaments floating in outer space, to capture the drifting space debris that endangers astronaut lives’ and satellite integrity.”

Here’s a link to and a citation for the paper,

Toughness-enhancing metastructure in the recluse spider’s looped ribbon silk by
S. R. Koebley, F. Vollrath, and H. C. Schniepp. Mater. Horiz., 2017, Advance Article DOI: 10.1039/C6MH00473C First published online 15 Feb 2017

This paper is open access although you may need to register with the Royal Society of Chemistry’s publishing site to get access.

Deep learning for cosmetics

Deep learning seems to be a synonym for artificial intelligence if a May 24, 2016 Insilico Medicine news release on EurekAlert about its use in the fields of cosmetics and as an alternative to testing animals is to be believed (Note: Links have been removed),

In addition to heading Insilico Medicine, Inc, a big data analytics company focused on applying advanced signaling pathway activation analysis and deep learning methods to biomarker and drug discovery in cancer and age-related diseases, Alex Zhavoronkov, PhD is the co-founder and principal scientist of Youth Laboratories, a company focusing on applying machine learning methods to evaluating the condition of human skin and general health status using multimodal inputs. The company developed an app called RYNKL, a mobile app for evaluating the effectiveness of various anti-aging interventions by analyzing “wrinkleness” and other parameters. The app was developed using funds from a Kickstarter crowdfunding campaign and is now being extensively tested and improved. The company also developed a platform for running online beauty competitions, where humans are evaluated by a panel of robot judges. Teams of programmers also compete on the development of most innovative algorithms to evaluate humans.

“One of my goals in life is to minimize unnecessary animal testing in areas, where computer simulations can be even more relevant to humans. Serendipitously, some of our approaches find surprising new applications in the beauty industry, which has moved away from human testing and is moving towards personalizing cosmetics and beauty products. We are happy to present our research results to a very relevant audience at this major industry event”, said Alex Zhavoronkov, CEO of Insilico Medicine, Inc.

Artificial intelligence is entering every aspect of our daily life. Deep learning systems are already outperforming humans in image and text recognition and we would like to bring some of the most innovative players like Insilico Medicine, who dare to work with gene expression, imaging and drug data to find novel ways to keep us healthy, young and beautiful”, said Irina Kremlin, director of INNOCOS.

Here’s a link to and a citation for the paper,

Deep biomarkers of human aging: Application of deep neural networks to biomarker development by Evgeny Putin, Polina Mamoshina, Alexander Aliper, Mikhail Korzinkin, Alexey Moskalev, Alexey Kolosov, Alexander Ostrovskiy, Charles Cantor, Jan Vijg, and Alex Zhavoronkov. Aging May 2016 vol. 8, no. 5

This is an open access paper.

You can find out more about In Silico Medicine here and RINKL here. I was not able to find a website for Youth Laboratories.