Yellow fibers, L=4 mm, d=0.01 mm, ro=1,07 g/cm3 ;Experimental basin 32×15.2×22.5 cm, distilled prefiltered (100 mkm) water;Heating plate 30×14 cm (2 mm thickness) at the bottom - 10% power, 8 Wt;Average velocity of fibers 2.54
Playback speed 3x
Fibers moving near bottom
This talk was presented for the Machine Learning Reading Group of the Wellcome Trust EPSRC for Interventional and Surgical Sciences of University College London, 11 February 2020.
I gave an overview of self-supervised learning and some applications to computer vision and medical images.
Contributors:Lobo-Silva, Jessica, Cabral, Fernanda J., Murilo S. Amaral, Miyasato, Patrícia A., Freitas, Rafaela Paula De, Pereira, Adriana S. A., Khouri, Mariana I., Barbosa, Mayra M. F., Ramos, Pablo I. P., Leite, Luciana C. C., Oluwatoyin A. Asojo, Nakano, Eliana, Verjovski-Almeida, Sergio, Farias, Leonardo P.
Additional file 1: Figure S1. Comparison of S. mansoni demethylase domain architecture and corresponding human orthologs. Proteins domains were mapped using SMART. This analysis includes only S. mansoni proteins presenting druggability score > 0.8.
Additional file 1. In vitro implantation in agarose. Schematic illustration and video of the in vitro implantation of coated needles and spreading of fluorescently labeled nanoparticles in agarose gel.
The development of capacity is a cornerstone of strength and conditioning programs. The use of a training process that integrates capacity building with skill development to maximise transfer of training is the ultimate key to enhancing not just athlete robustness but athlete performance with their ability to solve a multitude of movement problems. This presentation presented the concept of a "worst case scenario" in sidestepping and discusses the implications for athletes expressing particular movement solutions when limited by physical capacity, movement strategy or a combination. Further, a practical process model for enhancing transfer of training and the importance of the integration between mixing gains in capacity with motor learning. The examples will be focused around developing better cutting movements that enhance performance and also minimise risk during these movements.
This presentation was given at the 2019 World Congress of Science and Football in Melbourne, June 4 - 7, 2019.
Snakes can move through almost any terrain. Although their locomotion on flat surfaces using planar gaits is inherently stable, when snakes deform their body out of plane to traverse complex terrain, maintaining stability becomes a challenge. On trees and desert dunes, snakes grip branches or brace against depressed sand for stability. However, how they stably surmount obstacles like boulders too large and smooth to gain such ‘anchor points’ is less understood. Similarly, snake robots are challenged to stably traverse large, smooth obstacles for search and rescue and building inspection. Our recent study discovered that snakes combine body lateral undulation and cantilevering to stably traverse large steps. Here, we developed a snake robot with this gait and snake-like anisotropic friction and used it as a physical model to understand stability principles. The robot traversed steps as high as a third of its body length rapidly and stably. However, on higher steps, it was more likely to fail due to more frequent rolling and flipping over, which was absent in the snake with a compliant body. Adding body compliance reduced the robot's roll instability by statistically improving surface contact, without reducing speed. Besides advancing understanding of snake locomotion, our robot achieved high traversal speed surpassing most previous snake robots and approaching snakes, while maintaining high traversal probability.