A Rapid Bootstrap Algorithm for the RAxML Web Servers
Despite recent advances achieved by application of high-performance computing methods and novel algorithmic techniques to maximum likelihood (ML)-based inference programs, the m...
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Despite recent advances achieved by application of high-performance computing methods and novel algorithmic techniques to maximum likelihood (ML)-based inference programs, the m...
One of Italy's most original philosophers aims to connect the problem of pure possibility, potentiality, and power with the problem of political and social ethics in a context w...
Abstract Here, we present a major advance of the OrthoFinder method. This extends OrthoFinder’s high accuracy orthogroup inference to provide phylogenetic inference of orthologs...
Abstract The ff94 force field that is commonly associated with the Amber simulation package is one of the most widely used parameter sets for biomolecular simulation. After a de...
ADVERTISEMENT RETURN TO ISSUEPREVReviewNEXTHoneycomb Carbon: A Review of GrapheneMatthew J. Allen†, Vincent C. Tung‡, and Richard B. Kaner*†‡View Author Information Department o...
"Anthropogenic pressures on the Earth System have reached a scale where abrupt global environmental change can no longer be excluded. We propose a new approach to global sustain...
Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately,...
Robert Nozicka s Anarchy, State, Utopia is a powerful, philosophical challenge to the most widely held political social positions of our age ---- liberal, socialist conservat...
We present a new open source, extensible and flexible software platform for Bayesian evolutionary analysis called BEAST 2. This software platform is a re-design of the popular B...
ABSTRACT Radiocarbon ( 14 C) ages cannot provide absolutely dated chronologies for archaeological or paleoenvironmental studies directly but must be converted to calendar age eq...
When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This "overfitting" is greatly reduced by randomly...