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In contrast to the among species analysis, the within D. simulans clusters lack genes evidencing recurrent adaptive amino acid evolution (nominal MKtest p-value < 0.05; polarized, 15% fewer adaptively evolving genes, p-value < 0.001; unpolarized 30% fewer adaptively evolving genes, p-value < 0.001).
In short, the proponents of HAT propose that humans possess a number of specific, evolved adaptations for killing that have been selected for because they managed to successfully solve recurrent adaptive problems in our ancestral past.
For example, the Drosophila seminal fluid protein gene Acp26Aa was first inferred to have a history of recurrent adaptive protein evolution in the melanogaster subgroup [11].
We found only weak evidence of recurrent adaptive protein evolution at the Relish locus in three sister species pairs, suggesting that this key component of the insect immune system has an idiosyncratic evolutionary history in Drosophila.
This allows one to ask the general question of whether a gene with a history of recurrent adaptive protein evolution in one species is likely to have a similar selective history in other species; that is, is directional selection idiosyncratic or predictable?
Genes with higher rates of amino-acid evolution show lower levels of neutral diversity, a pattern predicted by recurrent adaptive protein evolution.
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That is, is the rate of adaptation great enough that, on average, a recent adaptive fixation will have occurred in a gene undergoing recurrent adaptation?
The fully-recurrent Robust Adaptive Gradient Descent (RAGD) algorithm, is tuned initially using synthesised crank kinematics, and then tested on real engine data to assess the reconstruction capability.
The absence of recurrent positive adaption within the Drosophila clade indicates that adaptive change of Sxl to its new role in sex determination occurred prior to the divergence of the Drosophila species.
This paper attempts to predict the Total Electron Content using adaptive recurrent Neural Network at three different locations of India.
This paper presents the design, development and implementation of an adaptive recurrent neural networks (ARNN) controller suitable for real-time manipulator control applications.
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