Sexually coercive male chimpanzees sire more offspring.

In sexually reproducing animals, male and female reproductive strategies often conflict. In some species, males use aggression to overcome female choice, but debate persists over the extent to which this strategy is successful. Previous studies of male aggression toward females among wild chimpanzees have yielded contradictory results about the relationship between aggression and mating behavior. Critically, however, copulation frequency in primates is not always predictive of reproductive success. We analyzed a 17-year sample of behavioral and genetic data from the Kasekela chimpanzee (Pan troglodytes schweinfurthii) community in Gombe National Park, Tanzania, to test the hypothesis that male aggression toward females increases male reproductive success. We examined the effect of male aggression toward females during ovarian cycling, including periods when the females were sexually receptive (swollen) and periods when they were not. We found that, after controlling for confounding factors, male aggression during a female's swollen periods was positively correlated with copulation frequency. However, aggression toward swollen females was not predictive of paternity. Instead, aggression by high-ranking males toward females during their nonswollen periods was positively associated with likelihood of paternity. This indicates that long-term patterns of intimidation allow high-ranking males to increase their reproductive success, supporting the sexual coercion hypothesis. To our knowledge, this is the first study to present genetic evidence of sexual coercion as an adaptive strategy in a social mammal.

Adaptive Mixture Modelling Metropolis Methods for Bayesian Analysis of Non-linear State-Space Models.

We describe a strategy for Markov chain Monte Carlo analysis of non-linear, non-Gaussian state-space models involving batch analysis for inference on dynamic, latent state variables and fixed model parameters. The key innovation is a Metropolis-Hastings method for the time series of state variables based on sequential approximation of filtering and smoothing densities using normal mixtures. These mixtures are propagated through the non-linearities using an accurate, local mixture approximation method, and we use a regenerating procedure to deal with potential degeneracy of mixture components. This provides accurate, direct approximations to sequential filtering and retrospective smoothing distributions, and hence a useful construction of global Metropolis proposal distributions for simulation of posteriors for the set of states. This analysis is embedded within a Gibbs sampler to include uncertain fixed parameters. We give an example motivated by an application in systems biology. Supplemental materials provide an example based on a stochastic volatility model as well as MATLAB code.

Adaptive intervention design in mobile health: Intervention design and development in the Cell Phone Intervention for You trial.

BACKGROUND/AIMS: The obesity epidemic has spread to young adults, and obesity is a significant risk factor for cardiovascular disease. The prominence and increasing functionality of mobile phones may provide an opportunity to deliver longitudinal and scalable weight management interventions in young adults. The aim of this article is to describe the design and development of the intervention tested in the Cell Phone Intervention for You study and to highlight the importance of adaptive intervention design that made it possible. The Cell Phone Intervention for You study was a National Heart, Lung, and Blood Institute-sponsored, controlled, 24-month randomized clinical trial comparing two active interventions to a usual-care control group. Participants were 365 overweight or obese (body mass index≥25 kg/m2) young adults. METHODS: Both active interventions were designed based on social cognitive theory and incorporated techniques for behavioral self-management and motivational enhancement. Initial intervention development occurred during a 1-year formative phase utilizing focus groups and iterative, participatory design. During the intervention testing, adaptive intervention design, where an intervention is updated or extended throughout a trial while assuring the delivery of exactly the same intervention to each cohort, was employed. The adaptive intervention design strategy distributed technical work and allowed introduction of novel components in phases intended to help promote and sustain participant engagement. Adaptive intervention design was made possible by exploiting the mobile phone's remote data capabilities so that adoption of particular application components could be continuously monitored and components subsequently added or updated remotely. RESULTS: The cell phone intervention was delivered almost entirely via cell phone and was always-present, proactive, and interactive-providing passive and active reminders, frequent opportunities for knowledge dissemination, and multiple tools for self-tracking and receiving tailored feedback. The intervention changed over 2 years to promote and sustain engagement. The personal coaching intervention, alternatively, was primarily personal coaching with trained coaches based on a proven intervention, enhanced with a mobile application, but where all interactions with the technology were participant-initiated. CONCLUSION: The complexity and length of the technology-based randomized clinical trial created challenges in engagement and technology adaptation, which were generally discovered using novel remote monitoring technology and addressed using the adaptive intervention design. Investigators should plan to develop tools and procedures that explicitly support continuous remote monitoring of interventions to support adaptive intervention design in long-term, technology-based studies, as well as developing the interventions themselves.