Carotenoid-based plumage colors and immune function: Is there a trade-off for rare carotenoids?

Theory suggests that carotenoid-based signals are used in animal communication because they contain specific information about parasite resistance or immunocompetence. This implies that honesty of carotenoid-based signals is maintained by a trade-off between pigmentation and immune function for carotenoids, assuming that the carotenoids used for coloration are also immunoenhancing. We tested this hypothesis by altering the diets of nestling great tits (Paris major) with supplementary beadlets containing the carotenoids that are naturally ingested with food or beadlets containing the carotenoids that are incorporated into the feathers; a control group received beadlets containing no carotenoids. We simultaneously immune challenged half of the nestlings of each supplementation group, using a two-factorial design. Activatior of the immune system led to reduced color expression. However, only nestlings fed with the naturally ingested carotenoids and not with the carotenoids deposited in the feathers showed an increased cellular immune response. This shows that the carotenoids used for ornamentation do not promote the immune function, which conflicts with the trade-off hypothesis. Our results indicate that honesty of carotenoid-based signals is maintained by an individual's physiological limitation to absorb and/or transport carotenoids and by access to carotenoids, indicating that preferences for carotenoid-based traits in sexual selection or parent-offspring interactions select for competitive individuals, rather than specifically for immune function.

Time-course of "off-line" prefrontal rTMS effects--a PET study.

Low-frequency "off-line" repetitive transcranial magnetic stimulation (rTMS) over the course of several minutes has attained considerable attention as a research tool in cognitive neuroscience due to its ability to induce functional disruptions of brain areas. This disruptive rTMS effect is highly valuable for revealing a causal relationship between brain and behavior. However, its influence on remote interconnected areas and, more importantly, the duration of the induced neurophysiological effects, remain unknown. These aspects are critical for a study design in the context of cognitive neuroscience. In order to investigate these issues, 12 healthy male subjects underwent 8 H(2)(15)O positron emission tomography (PET) scans after application of long-train low-frequency rTMS to the right dorsolateral prefrontal cortex (DLPFC). Immediately after the stimulation train, regional cerebral blood flow (rCBF) increases were present under the stimulation site as well as in other prefrontal cortical areas, including the ventrolateral prefrontal cortex (VLPFC) ipsilateral to the stimulation site. The mean increases in rCBF returned to baseline within 9 min. The duration of this unilateral prefrontal rTMS effect on rCBF is of particular interest to those who aim to influence behavior in cognitive paradigms that use an "off-line" approach.

Differentiating the Multipoint Expected Improvement for Optimal Batch Design

This work deals with parallel optimization of expensive objective functions which are modelled as sample realizations of Gaussian processes. The study is formalized as a Bayesian optimization problem, or continuous multi-armed bandit problem, where a batch of q > 0 arms is pulled in parallel at each iteration. Several algorithms have been developed for choosing batches by trading off exploitation and exploration. As of today, the maximum Expected Improvement (EI) and Upper Confidence Bound (UCB) selection rules appear as the most prominent approaches for batch selection. Here, we build upon recent work on the multipoint Expected Improvement criterion, for which an analytic expansion relying on Tallis’ formula was recently established. The computational burden of this selection rule being still an issue in application, we derive a closed-form expression for the gradient of the multipoint Expected Improvement, which aims at facilitating its maximization using gradient-based ascent algorithms. Substantial computational savings are shown in application. In addition, our algorithms are tested numerically and compared to state-of-the-art UCB-based batchsequential algorithms. Combining starting designs relying on UCB with gradient-based EI local optimization finally appears as a sound option for batch design in distributed Gaussian Process optimization.