Intracellular gene transfer in action: Dual transcription and multiple silencings of nuclear and mitochondrial cox2 genes in legumes

The respiratory gene cox2, normally present in the mitochondrion, was previously shown to have been functionally transferred to the nucleus during flowering plant evolution, possibly during the diversification of legumes. To search for novel intermediate stages in the process of intracellular gene transfer and to assess the evolutionary timing and frequency of cox2 transfer, activation, and inactivation, we examined nuclear and mitochondrial (mt) cox2 presence and expression in over 25 legume genera and mt cox2 presence in 392 genera. Transfer and activation of cox2 appear to have occurred during recent legume evolution, more recently than previously inferred. Many intermediate stages of the gene transfer process are represented by cox2 genes in the studied legumes. Nine legumes contain intact copies of both nuclear and mt cox2, although transcripts could not be detected for some of these genes. Both cox2 genes are transcribed in seven legumes that are phylogenetically interspersed with species displaying only nuclear or mt cox2 expression. Inactivation of cox2 in each genome has taken place multiple times and in a variety of ways, including loss of detectable transcripts or transcript editing and partial to complete gene loss. Phylogenetic evidence shows about the same number (3–5) of separate inactivations of nuclear and mt cox2, suggesting that there is no selective advantage for a mt vs. nuclear location of cox2 in plants. The current distribution of cox2 presence and expression between the nucleus and mitochondrion in the studied legumes is probably the result of chance mutations silencing either cox2 gene.

Working memory constrains human cooperation in the Prisoner’s Dilemma

Many problems in human society reflect the inability of selfish parties to cooperate. The “Iterated Prisoner’s Dilemma” has been used widely as a model for the evolution of cooperation in societies. Axelrod’s computer tournaments and the extensive simulations of evolution by Nowak and Sigmund and others have shown that natural selection can favor cooperative strategies in the Prisoner’s Dilemma. Rigorous empirical tests, however, lag behind the progress made by theorists. Clear predictions differ depending on the players’ capacity to remember previous rounds of the game. To test whether humans use the kind of cooperative strategies predicted, we asked students to play the iterated Prisoner’s Dilemma game either continuously or interrupted after each round by a secondary memory task (i.e., playing the game “Memory”) that constrained the students’ working-memory capacity. When playing without interruption, most students used “Pavlovian” strategies, as predicted, for greater memory capacity, and the rest used “generous tit-for-tat” strategies. The proportion of generous tit-for-tat strategies increased when games of Memory interfered with the subjects’ working memory, as predicted. Students who continued to use complex Pavlovian strategies were less successful in the Memory game, but more successful in the Prisoner’s Dilemma, which indicates a trade-off in memory capacity for the two tasks. Our results suggest that the set of strategies predicted by game theorists approximates human reality.

Approximation algorithms

Increasing global competition, rapidly changing markets, and greater consumer awareness have altered the way in which corporations do business. To become more efficient, many industries have sought to model some operational aspects by gigantic optimization problems. It is not atypical to encounter models that capture 106 separate “yes” or “no” decisions to be made. Although one could, in principle, try all 2106 possible solutions to find the optimal one, such a method would be impractically slow. Unfortunately, for most of these models, no algorithms are known that find optimal solutions with reasonable computation times. Typically, industry must rely on solutions of unguaranteed quality that are constructed in an ad hoc manner. Fortunately, for some of these models there are good approximation algorithms: algorithms that produce solutions quickly that are provably close to optimal. Over the past 6 years, there has been a sequence of major breakthroughs in our understanding of the design of approximation algorithms and of limits to obtaining such performance guarantees; this area has been one of the most flourishing areas of discrete mathematics and theoretical computer science.