Mutation bias interacts with composition bias to influence adaptive evolution
نویسندگان
چکیده
منابع مشابه
An Adaptive Self-adjusting Bandwidth Bandpass Filter without IIR Bias
In this paper we introduce a simple, computationally inxepentsive, adaptive recursive structure for enhancing bandpass signals highly corrupted by broad-band noise. This adaptive algorithm, enhancing input signals, enables us to estimate the center frequency and the bandwidth of the input signal. In addition, an important feature of the proposed structure is that the conventional bias existing ...
متن کاملAn Adaptive Self-adjusting Bandwidth Bandpass Filter without IIR Bias
In this paper we introduce a simple, computationally inxepentsive, adaptive recursive structure for enhancing bandpass signals highly corrupted by broad-band noise. This adaptive algorithm, enhancing input signals, enables us to estimate the center frequency and the bandwidth of the input signal. In addition, an important feature of the proposed structure is that the conventional bias existing ...
متن کاملThe Effect of Cognitive Bias Modification in the Attention Bias of Students With Test Anxiety
Abstract Introduction: Many of the students chr('39')educational problems, including academic failure and maladaptive behaviors, stem from exam anxiety caused by studentschr('39') cognitive bias. Cognitive bias modification can reduce attention biases students to be negative. Therefore, the purpose of the present study was to investigate the effectiveness of cognitive bias modification interven...
متن کاملForces that influence the evolution of codon bias.
The frequencies of alternative synonymous codons vary both among species and among genes from the same genome. These patterns have been inferred to reflect the action of natural selection. Here we evaluate this in bacteria. While intragenomic variation in many species is consistent with selection favouring translationally optimal codons, much of the variation among species appears to be due to ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLOS Computational Biology
سال: 2020
ISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1008296