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Günter Wagner’s Followers (3)
Günter Wagner
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An exegetical Bibliography of the New Testament: John and 1, 2, 3 John
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published
1987
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An Exegetical Bibliography of the New Testament: Luke and Acts
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published
1986
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2 editions
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An Exegetical Bibliography of the New Testament: Matthew and Mark
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published
1983
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The Bantu of Western Kenya: With special reference to the Vugusu and Logoli
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published
1970
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An Exegetical Bibliography of the New Testament: Romans and Galations
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published
1996
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Eine Bombenelf. ( Ab 10 J.)
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published
1993
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Festschrift Günter Wagner
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published
1994
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Waschmittel: Chemie, Umwelt, Nachhaltigkeit
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published
2010
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12 editions
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Jahrbuch des Staatlichen Instituts für Musikforschung Preussischer Kulturbesitz
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Fission-Track Dating
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published
2012
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“Modeling the evolution of modularity became significantly easier after a kind of genetic variation was discovered by quantitative trait locus (QTL) mapping in the lab of James Cheverud at Washington University called 'relationship QTL' or r-QTL for short. An r-QTL is a genetic locus that affects the correlations between two quantitative traits (i.e. their variational relationship, and therefore, 'relationship' loci). Surprisingly, a large fraction of these so-mapped loci are also neutral with respect to the character mean. This means one can select on these 'neutral' r-QTLs without simultaneously changing the character mean in a certain way.
It was easy to show that differential directional selection on a character could easily lead a decrease in genetic correlation between characters. Of course, it is not guaranteed that each and every population has the right kind of r-QTL polymorphisms, nor is it yet clear what kind of genetic architecture allows for the existence of an r-QTL.
Nevertheless, these findings make it plausible that differential directional selection can enhance the genetic/variational individuality of traits and, thus, may play a role in the origin of evolutionary novelties by selecting for variational individuality.
It must be added, though, that there has been relatively little research in this area and that we will need to see more to determine whether we understand what is going on here, if anything. In particular, one difficulty is the mathematical modeling of gene interaction (epistasis), because the details of an epistasis model determine the outcome of the evolution by natural selection. One result shows that natural selection increases or decreases mutational variance, depending on whether the average epistatic effects are positive or negative. This means that the genetic architecture is more determined by the genetic architecture that we start with than by the nature of the selection forces that act upon it. In other words, the evolution of a genetic architecture could be arbitrary with respect to selection.”
― Homology, Genes, and Evolutionary Innovation
It was easy to show that differential directional selection on a character could easily lead a decrease in genetic correlation between characters. Of course, it is not guaranteed that each and every population has the right kind of r-QTL polymorphisms, nor is it yet clear what kind of genetic architecture allows for the existence of an r-QTL.
Nevertheless, these findings make it plausible that differential directional selection can enhance the genetic/variational individuality of traits and, thus, may play a role in the origin of evolutionary novelties by selecting for variational individuality.
It must be added, though, that there has been relatively little research in this area and that we will need to see more to determine whether we understand what is going on here, if anything. In particular, one difficulty is the mathematical modeling of gene interaction (epistasis), because the details of an epistasis model determine the outcome of the evolution by natural selection. One result shows that natural selection increases or decreases mutational variance, depending on whether the average epistatic effects are positive or negative. This means that the genetic architecture is more determined by the genetic architecture that we start with than by the nature of the selection forces that act upon it. In other words, the evolution of a genetic architecture could be arbitrary with respect to selection.”
― Homology, Genes, and Evolutionary Innovation
“Deeper phylogenetic relationships that are notoriously difficult to reconstruct with conventional sequence comparison methods are being resolved with miRNAs. The reason is that normal gene sequences continue to evolve after a lineage split, and, thus, the phylogenetic signal can erode by later evolution. In contrast, miRNAs stay put and, this, are like molecular fossils identifying related lineages. The only drawback is that miRNA inventories are expensive to determine and some of the data is based on the lack of certain miRNAs in certain species, which can always be a detection artifact.”
― Homology, Genes, and Evolutionary Innovation
― Homology, Genes, and Evolutionary Innovation
“Selection on one of two genetically correlated characters will lead to a change in the unselected character, a phenomenon called 'correlated selection response.' This means that selection on one character may lead to a loss of adaptation at a genetically correlated character. If these two characters often experience directional selection independently of each other, then a decrease in correlation will be beneficial. This seems to be a reasonably intuitive idea, although it turned out to be surprisingly difficult to model this process. One of the first successful attempts to simulate the evolution of variational modularity was the study by Kashtan and Alon (2005) in which they used logical circuits as model of the genotype.
A logical circuit consists of elements that take two or more inputs and transform them into one output according to some rule. The inputs and outputs are binary, either 0 or 1 as in a digital computer, and the rule can be a logical (Boolean) function. A genome then consists of a number of these logical elements and the connections among them. Mutations change the connections among the elements and selection among mutant genotypes proceeds according to a given goal. The goal for the network is to produce a certain output for each possible input configuration.
For example, their circuit had four inputs: x,y,z, and w. The network was selected to calculate the following logical function: G1 = ((x XOR y) AND (z XOR w)). When the authors selected for this goal, the network evolved many different possible solutions (i.e. networks that could calculate the function G1). In this experiment, the evolved networks were almost always non-modular.
In another experiment, the authors periodically changed the goal function from G1 to G2 = ((x XOR y) or (z XOR w)). In this case, the networks always evolved modularity, in the sense that there were sub-circuits dedicated to calculating the functions shared between G1 and G2, (x XOR y) and (z XOR w), and another part that represented the variable part if the function: either the AND or the OR function connecting (x XOR y) and (z XOR w). Hence, if the fitness function was modular, that is, if there were aspects that remained the same and others that changed, then the system evolved different parts that represented the constant and the variable parts of the environment.
This example was intriguing because it overcame some of the difficulties of earlier attempts to simulate the evolution of variational modularity, although it did use a fairly non-standard model of a genotype-phenotype map: logical circuits. In a second example, Kashtan and Alon (2005) used a neural network model with similar results. Hence, the questions arise, how generic are these results? And can one expect that similar processes occur in real life?”
― Homology, Genes, and Evolutionary Innovation
A logical circuit consists of elements that take two or more inputs and transform them into one output according to some rule. The inputs and outputs are binary, either 0 or 1 as in a digital computer, and the rule can be a logical (Boolean) function. A genome then consists of a number of these logical elements and the connections among them. Mutations change the connections among the elements and selection among mutant genotypes proceeds according to a given goal. The goal for the network is to produce a certain output for each possible input configuration.
For example, their circuit had four inputs: x,y,z, and w. The network was selected to calculate the following logical function: G1 = ((x XOR y) AND (z XOR w)). When the authors selected for this goal, the network evolved many different possible solutions (i.e. networks that could calculate the function G1). In this experiment, the evolved networks were almost always non-modular.
In another experiment, the authors periodically changed the goal function from G1 to G2 = ((x XOR y) or (z XOR w)). In this case, the networks always evolved modularity, in the sense that there were sub-circuits dedicated to calculating the functions shared between G1 and G2, (x XOR y) and (z XOR w), and another part that represented the variable part if the function: either the AND or the OR function connecting (x XOR y) and (z XOR w). Hence, if the fitness function was modular, that is, if there were aspects that remained the same and others that changed, then the system evolved different parts that represented the constant and the variable parts of the environment.
This example was intriguing because it overcame some of the difficulties of earlier attempts to simulate the evolution of variational modularity, although it did use a fairly non-standard model of a genotype-phenotype map: logical circuits. In a second example, Kashtan and Alon (2005) used a neural network model with similar results. Hence, the questions arise, how generic are these results? And can one expect that similar processes occur in real life?”
― Homology, Genes, and Evolutionary Innovation
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