How costly is clutch formation in the Audouin's Gull Larus audouinii?

During the Audouin's Gull's breeding season at the Ebro Delta in 1993, 24 fresh eggs from eight three-egg clutches (modal clutch-size) were collected at the peak of the laying period. Eggs were processed to obtain formalin-fixed yolks, which were halved and stained using the potassium dichromate method. Digitized images of the yolks were examined to assess the daily rates of yolk deposition. We used these data in combination with egg compositional analysis to build a model of energy demands during the formation of an average clutch in Audouin's Gull. To show how the different parameters of clutch formation affect the daily energy investment peak, we performed a simulation analysis in which the rapid yolk development (RYD) period, the follicle triggering interval (FTI), the laying interval (LI) and the albumen synthesis period (ASP) were allowed to vary simultaneously. In our sample, the mean RYD period was seven days with a range from six to eight days. There were no significant differences in yolk volume among eggs in a clutch, but albumen volume was significantly smaller in third eggs. According to our model the albumen synthesis of the a-egg coincides with the energy demand peak for clutch formation. This peak represents an increase by ca. 42% in female energy requirements. Values obtained from the simulation analysis showed that only the ASP of the a-egg and the RYD durations of the second and third follicles produced noticeable reductions in peak energy investment. We predict that in gulls, whose laying intervals seem to be kept constant, significant increases of the durations of the RYD periods of second and third eggs, or even significant reductions of yolk size of these eggs, may operate simultaneously to match the energy demands during clutch formation to the prevailing food conditions.     

Population Level Inference for Multivariate MEG Analysis

Multivariate analysis is a very general and powerful technique for analysing Magnetoencephalography (MEG) data. An outstanding problem however is how to make inferences that are consistent over a group of subjects as to whether there are condition-specific differences in data features, and what are those features that maximise these differences. Here we propose a solution based on Canonical Variates Analysis (CVA) model scoring at the subject level and random effects Bayesian model selection at the group level. We apply this approach to beamformer reconstructed MEG data in source space. CVA estimates those multivariate patterns of activation that correlate most highly with the experimental design; the order of a CVA model is then determined by the number of significant canonical vectors. Random effects Bayesian model comparison then provides machinery for inferring the optimal order over the group of subjects. Absence of a multivariate dependence is indicated by the null model being the most likely. This approach can also be applied to CVA models with a fixed number of canonical vectors but supplied with different feature sets. We illustrate the method by identifying feature sets based on variable-dimension MEG power spectra in the primary visual cortex and fusiform gyrus that are maximally discriminative of data epochs before versus after visual stimulation.     

Note Onset Deviations as Musical Piece Signatures

A competent interpretation of a musical composition presents several non-explicit departures from the written score. Timing variations are perhaps the most important ones: they are fundamental for expressive performance and a key ingredient for conferring a human-like quality to machine-based music renditions. However, the nature of such variations is still an open research question, with diverse theories that indicate a multi-dimensional phenomenon. In the present study, we consider event-shift timing variations and show that sequences of note onset deviations are robust and reliable predictors of the musical piece being played, irrespective of the performer. In fact, our results suggest that only a few consecutive onset deviations are already enough to identify a musical composition with statistically significant accuracy. We consider a mid-size collection of commercial recordings of classical guitar pieces and follow a quantitative approach based on the combination of standard statistical tools and machine learning techniques with the semi-automatic estimation of onset deviations. Besides the reported results, we believe that the considered materials and the methodology followed widen the testing ground for studying musical timing and could open new perspectives in related research fields.     

Pluripotency and Epigenetic Factors in Mouse Embryonic Stem Cell Fate Regulation

Embryonic stem cells (ESCs) are characterized by their ability to self-renew and to differentiate into all cell types of a given organism. Understanding the molecular mechanisms that govern the ESC state is of great interest not only for basic research—for instance, ESCs represent a perfect system to study cellular differentiation in vitro—but also for their potential implications in human health, as these mechanisms are likewise involved in cancer progression and could be exploited in regenerative medicine. In this minireview, we focus on the latest insights into the molecular mechanisms mediated by the pluripotency factors as well as their roles during differentiation. We also discuss recent advances in understanding the function of the epigenetic regulators, Polycomb and MLL complexes, in ESC biology.