A general probabilistic model for group independent component analysis and its estimation methods

Independent component analysis (ICA) has become an important tool for analyzing data from functional magnetic resonance imaging (fMRI) studies. ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a prespecified group design matrix and the uncertainty in between-subjects variability in fMRI data. We present a general probabilistic ICA (PICA) model that can accommodate varying group structures of multisubject spatiotemporal processes. An advantage of the proposed model is that it can flexibly model various types of group structures in different underlying neural source signals and under different experimental conditions in fMRI studies. A maximum likelihood (ML) method is used for estimating this general group ICA model. We propose two expectation-maximization (EM) algorithms to obtain the ML estimates. The first method is an exact EM algorithm, which provides an exact E-step and an explicit noniterative M-step. The second method is a variational approximation EM algorithm, which is computationally more efficient than the exact EM. In simulation studies, we first compare the performance of the proposed general group PICA model and the existing probabilistic group ICA approach. We then compare the two proposed EM algorithms and show the variational approximation EM achieves comparable accuracy to the exact EM with significantly less computation time. An fMRI data example is used to illustrate application of the proposed methods.     

Predicting the start of the birch pollen season at London, Derby and Cardiff, United Kingdom, using a multiple regression model, based on data from 1987 to 1997

Recent changes in weather in North-West Europehave been reflected in the start dates ofpollen seasons. It is therefore necessary toupdate previous models, such as the oneproduced by Jones (1995), so that the modelwill be weighted by current weather patterns. Birch pollen data, collected over a period ofeleven years (1987 to 1997 inclusive) fromthree pollen counting stations in the UK,London, Derby and Cardiff, were analysed todetermine the start dates using the Sum75method. The start dates of the birch pollenseasons of the eleven-year period were thentested for significance against ten-dayaggregated variables of temperature andrainfall for each site. The significantvariables were entered into multiple regressionmodels until the most valid equation for eachsite was found. The models were then tested onthree years not included in their data sets. The models showed mean differences betweenactual and predicted start dates, for theeleven years used, of 1.5, 3 and 5 days atDerby, Cardiff and London respectively. Forthe test years the mean difference was 1, 4.5and 7.5 days at Derby, Cardiff and Londonrespectively. The most powerful model was forDerby where the corresponding meteorologicalstation is at 0.5 km distance and the weakestwas for London where the correspondingmeteorological station is much further away at21 km distance. Weather variables from earlyFebruary to mid March were found to be the mostinfluential on the start dates of the birchpollen season at the three sites.     

Pike and salmon as sister taxa: Detailed intraclade resolution and divergence time estimation of Esociformes + Salmoniformes based on whole mitochondrial genome sequences

The increasing number of taxa and loci in molecular phylogenetic studies of basal euteleosts has brought stability in a controversial area. A key emerging aspect to these studies is a sister Esociformes (pike) and Salmoniformes (salmon) relationship. We evaluate mitochondrial genome support for a sister Esociformes and Salmoniformes hypothesis by surveying many potential outgroups for these taxa, employing multiple phylogenetic approaches, and utilizing a thorough sampling scheme. Secondly, we conduct a simultaneous divergence time estimation and phylogenetic inference in a Bayesian framework with fossil calibrations focusing on relationships within Esociformes + Salmoniformes. Our dataset supports a sister relationship between Esociformes and Salmoniformes; however the nearest relatives of Esociformes + Salmoniformes are inconsistent among analyses. Within the order Esociformes, we advocate for a single family, Esocidae. Subfamily relationships within Salmonidae are poorly supported as Salmoninae sister to Thymallinae + Coregoninae.