One angle scientists have taken to explore how protein functions arise is to trace family evolution and relatedness, which is difficult. In eLife, Roman Sloutsky of UMass Amherst and his former advisor Kristen Naegle, now at UVA, propose an unusual, new and more accurate way to trace how proteins diverged over time. "It can yield powerful insights into the relationship between protein sequence, structure and function for that family," he says.
Because of imprinted preferences, strawberry poison frog females mate more with similar colored males, and less with differently colored males. Over time, the behavior could lead to two color types becoming separate species.
Predicting and controlling disease outbreaks would be easier and more reliable with the wider application of mathematical modelling, according to a new study. The study was conducted by researchers at the University of Waterloo, University of Maryland and Yale's School of Public Health.
The Institute of Mathematics at the Martin Luther University Halle-Wittenberg (MLU) is to coordinate a new European Training Network (ETN) for doctoral students. The 14 research projects will examine how complex mechanical systems can be better modelled and simulated on the computer. The European Union will provide around 3.6 million euros over 4 years as part of the Horizon 2020 programme. MLU is joined by eleven other universities and research institutions from 8 european countries.
SIAM Symposium on Algorithm Engineering and Experiments (ALENEX20), SIAM Symposium on Simplicity in Algorithms (SOSA20), SIAM Symposium on Algorithmic Principles of Computer Systems (APOCS20), and the Theory Underlying Algorithms Workshop (TUNGA) will take place at the same location.SODA is jointly sponsored by the SIAM Activity Group on Discrete Mathematics and the ACM Special Interest Group on Algorithms and Computation Theory.
For decades, scientists seeking to explain the emergence of complex group behaviors, such as schooling in fish, have been divided into two camps. Now, a way out of this dichotomy has been found with a novel AI model that bridges across the two.
An artificial intelligence/machine learning model to predict which scientific advances are likely to eventually translate to the clinic has been developed by Ian Hutchins and colleagues in the Office of Portfolio Analysis (OPA), a team led by George Santangelo at the National Institutes of Health (NIH). The work is described in a Meta-Research article published Oct. 10 in the open-access journal PLOS Biology,
The lifespan of a liquid droplet which is transforming into vapour can now be predicted thanks to a theory developed at the University of Warwick. The new understanding can now be exploited in a myriad of natural and industrial settings where the lifetime of liquid drops governs a process' behaviour and efficiency.