Bayesian spectral modeling for multiple time series

We develop a novel Bayesian modeling approach to spectral density estimation for multiple time series. The log-periodogram distribution for each series is modeled as a mixture of Gaussian distributions with frequency-dependent weights and mean functions. The implied model for the log-spectral density is a mixture of linear mean functions with frequency-dependent weights. The mixture weights are built through successive differences of a logit-normal distribution function with frequency-dependent parameters. Building from the construction for a single spectral density, we develop a hierarchical extension for multiple time series. Specifically, we set the mean functions to be common to all spectral densities and make the weights specific to the time series through the parameters of the logit-normal distribution. In addition to accommodating flexible spectral density shapes, a practically important feature of the proposed formulation is that it allows for ready posterior simulation through a Gibbs sampler with closed form full conditional distributions for all model parameters. The modeling approach is illustrated with simulated datasets, and used for spectral analysis of multichannel electroencephalographic recordings (EEGs), which provides a key motivating application for the proposed methodology

A Bayesian Variable Selection Approach Yields Improved Detection of Brain Activation From Complex-Valued fMRI

Voxel functional magnetic resonance imaging (fMRI) time courses are complex-valued signals giving rise to magnitude and phase data. Nevertheless, most studies use only the magnitude signals and thus discard half of the data that could potentially contain important information. Methods that make use of complex-valued fMRI (CV-fMRI) data have been shown to lead to superior power in detecting active voxels when compared to magnitude-only methods, particularly for small signal-to-noise ratios (SNRs). We present a new Bayesian variable selection approach for detecting brain activation at the voxel level from CV-fMRI data. We develop models with complex-valued spike-and-slab priors on the activation parameters that are able to combine the magnitude and phase information. We present a complex-valued EM variable selection algorithm that leads to fast detection at the voxel level in CV-fMRI slices and also consider full posterior inference via Markov chain Monte Carlo (MCMC). Model performance is illustrated through extensive simulation studies, including the analysis of physically based simulated CV-fMRI slices. Finally, we use the complex-valued Bayesian approach to detect active voxels in human CV-fMRI from a healthy individual who performed unilateral finger tapping in a designed experiment. The proposed approach leads to improved detection of activation in the expected motor-related brain regions and produces fewer false positive results than other methods for CV-fMRI. Supplementary materials for this article are available online

Bayesian time-varying autoregressions: Theory, methods and applications

We review the class of time-varying autoregressive (TVAR) models and a range of related recent developments of Bayesian time series modelling

Bayesian Mixed Effect Sparse Tensor Response Regression Model with Joint Estimation of Activation and Connectivity

Brain activation and connectivity analyses in task-based functional magnetic resonance imaging (fMRI) experiments with multiple subjects are currently at the forefront of data-driven neuroscience. In such experiments, interest often lies in understanding activation of brain voxels due to external stimuli and strong association or connectivity between the measurements on a set of pre-specified group of brain voxels, also known as regions of interest (ROI). This article proposes a joint Bayesian additive mixed modeling framework that simultaneously assesses brain activation and connectivity patterns from multiple subjects. In particular, fMRI measurements from each individual obtained in the form of a multi-dimensional array/tensor at each time are regressed on functions of the stimuli. We impose a low-rank PARAFAC decomposition on the tensor regression coefficients corresponding to the stimuli to achieve parsimony. Multiway stick breaking shrinkage priors are employed to infer activation patterns and associated uncertainties in each voxel. Further, the model introduces region specific random effects which are jointly modeled with a Bayesian Gaussian graphical prior to account for the connectivity among pairs of ROIs. Empirical investigations under various simulation studies demonstrate the effectiveness of the method as a tool to simultaneously assess brain activation and connectivity. The method is then applied to a multi-subject fMRI dataset from a balloon-analog risk-taking experiment in order to make inference about how the brain processes risk.Comment: 27 pages, 7 figure

Screening acute cytotoxicity biomarkers using a microalga as test organism

The present study checked the suitability of the integration of flow cytometry (FCM) as technique and a freshwater microalga (Chlamydomonas moewusii) as cell system model for ecotoxicological studies, looking for sensitive biomarkers of acute cytotoxicity of potential contaminants in aquatic systems. The detection of the potential acute toxicity of a pollutant is of interest because pulse discharges of contaminants to natural waters could lead to high concentrations of these substances that are only present for short periods of time but can affect aquatic organisms such as microalgae. Physiological alterations in C. moewusii cells were analysed after 1 h of exposure to different concentrations of the herbicide paraquat. Cell viability was not affected, but the acute toxicity of paraquat was evident at other levels of cell physiology. Herbicide-treated cells showed lower autofluorescence and higher size and internal complexity, lower esterase activity and lower mitochondrial membrane potential. Paraquat induced the depolarisation of the plasma membrane and the increase of intracellular free calcium level and cytosolic pH in a concentration-dependent percentage of cells. All these effects can be related to the oxidative stress induced by the herbicide, as revealed the significantly increased intracellular levels of reactive oxygen species in cultures exposed to paraquat concentrations which induced the physiological alterations mentioned above. Excluding cell viability and mitochondrial membrane potential, these cytotoxicity endpoints could be considered sensitive biomarkers for the short-term exposure to pollutants such as herbicides. Highlights: • This study examine the acute cytotoxicity of paraquat on microalgal physiology. • Flow cytometric protocols assayed allow the screening of different toxicity cellular endpoints. ► Cell viability is not a sensitive biomarker. • Short-term paraquat exposure induces alteration in the overall cellular ionic homeostasis. • Most alterations observed could be related with the overproduction of ROS.Xunta de Galicia; 08MDS020103P

Population growth study of the rotifer Brachionus sp. fed with triazine-exposed microalgae

[Abstract]Few data exist on potential toxic effects that pollutants may have on zooplankton fed microalgae exposed to pesticides. For that reason, microalgal cultures were exposed to different concentrations of the triazine herbicide terbutryn, and used as exclusive food source to Brachionus sp. females, with the aim to evaluate potential deleterious effects upon population growth, survival, reproduction and feeding of the rotifer.Chlorella vulgaris cells were able to accumulate terbutryn, removing more than 90% of the total amount of herbicide in all the exposed cultures. Growth curves of Brachionus sp. showed that population density decreased as terbutryn concentration increased in the microalgal cells. In fact, this species of rotifer did not survive beyond four days when fed with microalgae exposed to 500 nM terbutryn. Percentage of reproductive females in rotifer populations fed with terbutryn-exposed microalgae decreased significantly as herbicide concentration increased. In control cultures, reproductive females laid a maximum of three eggs per individual; whereas in 100 nM cultures, reproductive females laid only one egg per individual throughout the treatment period. Terbutryn accumulated in C. vulgaris provoked a decrease in the feeding rate ofBrachionus sp. cultures fed with these microalgae with respect to control cultures. After this treatment period, all the rotifer populations, except those fed with 500 nM terbutryn-exposed microalgae, showed recovery patterns when they were returned to fresh medium containing herbicide-free microalga. Taking into account the results obtained, uptake of pesticides by phytoplankton can directly affect higher trophic levels.Xunta de Galicia; PGIDIT04RFO103946P

Cytotoxic effects of pesticides on microalgae determined by flow cytometry

As much as ca. 99.7% of the applied load of pesticides is dispersed in the environment, not reaching the target pests. In this way, pesticides enter into aquatic ecosystems from agricultural runoff or leaching and, as a consequence, have become some of the most frequently ocurring organic pollutants in aquatic ecosystems. Most phytotoxicological research with herbicides has been conducted on target plants. The sensitivity of algae to many herbicides is very high, and a better understanding of their environmental effects is probably acquired by using test species representing non-target groups. Because of their short generation times, microalgae respond rapidly to environmental changes. Furthermore, microalgal tests are generally sensitive, rapid and low-cost effective. For these reasons, the use of microalgal toxicity tests is increasing, and today these tests are frequently required by authorities for notifications of chemicals and are also increasingly being used to manage chemical discharges. Cytotoxic effects of aquatic pollutants on microalgae are very heterogeneous, and they are influenced by the environmental conditions and the test species. Growth, photosynthesis, chlorophyll fluorescence and others parameters reflect the toxic effects of pollutants on microalgae; however, other relevant endpoints are less known because experimental difficulties, especially under in vivo conditions. Flow cytometry offers multiparametric analysis of cells on a cell-by-cell basis in near in vivo conditions. Applied in the study of the aquatic environments since the 80's, this technique has achieved extensive use for the study of microalgae and has been introduced as an alternative to more traditional techniques of analyzing cells in culture and from natural populations. Several parameters determined by flow cytometry, such as cell volume and granularity, chlorophyll a fluorescence, cell viability, cell proliferation, oxidative stress, membrane potential or intracellular calcium level, were employed to assess changes in the physiological status of different microalgae as a consequence of the toxic action of herbicides. The variety of results obtained in the present study reveals that flow cytometry is a useful tool in the toxicity tests with microalgae.Xunta de Galicia; PGIDIT04RFO103946PRMinisterio de Educación y Ciencia CGL2004-02037BO