A new method of scenario analysis for strategic planning

We first present scenario analysis as a qualitative forecasting technique useful for strategic planning. Then we develop an overview of the two classes of methods for scenario analysis described in the literature. Based on both classes, a new method is developed which especially fits the needs of strategic planning. The method can be divided into three stages: 1. Determination of compatible scenarios, 2. Determination of scenario probabilities, and 3. Determination of main scenarios. An example is given to illustrate the method.     

Fine-scale phylogenetic architecture of a complex bacterial community

Although molecular data have revealed the vast scope of microbial diversity, two fundamental questions remain unanswered even for well-defined natural microbial communities: how many bacterial types co-exist, and are such types naturally organized into phylogenetically discrete units of potential ecological significance? It has been argued that without such information, the environmental function, population biology and biogeography of microorganisms cannot be rigorously explored. Here we address these questions by comprehensive sampling of two large 16S ribosomal RNA clone libraries from a coastal bacterioplankton community. We show that compensation for artefacts generated by common library construction techniques reveals fine-scale patterns of community composition. At least 516 ribotypes (unique rRNA sequences) were detected in the sample and, by statistical extrapolation, at least 1,633 co-existing ribotypes in the sampled population. More than 50% of the ribotypes fall into discrete clusters containing less than 1% sequence divergence. This pattern cannot be accounted for by interoperon variation, indicating a large predominance of closely related taxa in this community. We propose that such microdiverse clusters arise by selective sweeps and persist because competitive mechanisms are too weak to purge diversity from within them.     

Trichomonas hydrogenosomes contain the NADH dehydrogenase module of mitochondrial complex I

Hydrogenosomes are double-membraned ATP-producing and hydrogen-producing organelles of diverse anaerobic eukaryotes. In some versions of endosymbiotic theory they are suggested to be homologues of mitochondria, but alternative views suggest they arose from an anaerobic bacterium that was distinct from the mitochondrial endosymbiont. Here we show that the 51-kDa and 24-kDa subunits of the NADH dehydrogenase module in complex I, the first step in the mitochondrial respiratory chain, are active in hydrogenosomes of Trichomonas vaginalis. Like mitochondrial NADH dehydrogenase, the purified Trichomonas enzyme can reduce a variety of electron carriers including ubiquinone, but unlike the mitochondrial enzyme it can also reduce ferredoxin, the electron carrier used for hydrogen production. The presence of NADH dehydrogenase solves the long-standing conundrum of how hydrogenosomes regenerate NAD+ after malate oxidation. Phylogenetic analyses show that the Trichomonas 51-kDa homologue shares common ancestry with the mitochondrial enzyme. Recruitment of complex I subunits into a H2-producing pathway provides evidence that mitochondria and hydrogenosomes are aerobic and anaerobic homologues of the same endosymbiotically derived organelle.     

Temporal difference models describe higher-order learning in humans

The ability to use environmental stimuli to predict impending harm is critical for survival. Such predictions should be available as early as they are reliable. In pavlovian conditioning, chains of successively earlier predictors are studied in terms of higher-order relationships, and have inspired computational theories such as temporal difference learning. However, there is at present no adequate neurobiological account of how this learning occurs. Here, in a functional magnetic resonance imaging (fMRI) study of higher-order aversive conditioning, we describe a key computational strategy that humans use to learn predictions about pain. We show that neural activity in the ventral striatum and the anterior insula displays a marked correspondence to the signals for sequential learning predicted by temporal difference models. This result reveals a flexible aversive learning process ideally suited to the changing and uncertain nature of real-world environments. Taken with existing data on reward learning, our results suggest a critical role for the ventral striatum in integrating complex appetitive and aversive predictions to coordinate behaviour.     

Emergence of cooperation and evolutionary stability in finite populations

To explain the evolution of cooperation by natural selection has been a major goal of biologists since Darwin. Cooperators help others at a cost to themselves, while defectors receive the benefits of altruism without providing any help in return. The standard game dynamical formulation is the 'Prisoner's Dilemma', in which two players have a choice between cooperation and defection. In the repeated game, cooperators using direct reciprocity cannot be exploited by defectors, but it is unclear how such cooperators can arise in the first place. In general, defectors are stable against invasion by cooperators. This understanding is based on traditional concepts of evolutionary stability and dynamics in infinite populations. Here we study evolutionary game dynamics in finite populations. We show that a single cooperator using a strategy like 'tit-for-tat' can invade a population of defectors with a probability that corresponds to a net selective advantage. We specify the conditions required for natural selection to favour the emergence of cooperation and define evolutionary stability in finite populations.