A KaiC mutation, RC, which has been shown to affect the affinity of KaiB for KaiC and lengthen the period in a bioluminescence rhythm assay, is found within the middle of the predicted KaiBC interface. Klass paber, papp kartong ja nendest valmistatud tooted, mis ei kuulu teistesse klassidesse; trükised; trükitooted; kalendrid, käsiraamatud, märkmikud; diagrammid, graafilised kujutised, arvutustabelid, loendid, nimestikud, registrid, plaanid, projektid; köitematerjal; fotod; kontoritarbed; kirjatarbed; majapidamis- ja kantseleiliimid; kunstnikutarbed; pintslid maalimiseks ; kirjutusmasinad ja kantseleitarbed v.

A KaiC mutation, RC, which has been shown to affect the affinity of KaiB for KaiC and lengthen the period in a bioluminescence rhythm assay, is found within the middle of the predicted KaiBC interface.

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We demonstrate here that the AV KaiC mutant sheds light on the former mechanism. It was previously reported that AV is less sensitive to dark pulse-induced phase resetting and has a reduced amplitude of the KaiC phosphorylation rhythm in vivo.

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A maps to a loop loop that continues toward the phosphorylation sites. By pulling on the C-terminal peptide of KaiC A-loopKaiA removes restraints from the adjacent loop whose increased flexibility indirectly promotes kinase activity.

Supertrees can be seen as meta-analytical tools that can be used to make inferences based on results of previous scientific studies. Their meta-analytical application has increased in popularity since it was realised that the power of statistical tests for the study of evolutionary trends critically depends on the use of taxon-dense phylogenies.

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Further to that, supertrees have found applications in phylogenomics where they are used to combine gene trees and recover species phylogenies based on genome-scale data sets.

Here, we present the L. St package, a python tool for approximate maximum likelihood supertree inference and illustrate its application using a genomic data set for the placental mammals. St allows the calculation of the approximate likelihood of a supertree, given a set of input trees, performs heuristic searches to look for the supertree of highest likelihood, and performs statistical tests of two or more supertrees.

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