A class of statistical models for the motion of Daphnia over small time scales.

A common question in the aquatic sciences is that of how zooplankter movement can be modeled. It is well-established in the literature that there exists a randomness to this movement, but the question is how to characterize this randomness. The most common methods for doing this involve the random walk and correlated random walk (CRW) models. Here, we present a time series model that allows a better description the randomness in Daphnia motion when the amount of time that elapses between observations of their position is small. Our approach is adaptable to description of tracks of a multitude of animal species through re-estimation of model parameters. The model we propose uses information about how the animal moved during the previous two time intervals to explain how it moves currently. We demonstrate that the proposed model provides better predictive accuracy and fit than do the CRW and random walk models.

An extended model for phylogenetic maximum likelihood based on discrete morphological characters.

Maximum likelihood is a common method of estimating a phylogenetic tree based on a set of genetic data. However, models of evolution for certain types of genetic data are highly flawed in their specification, and this misspecification can have an adverse impact on phylogenetic inference. Our attention here is focused on extending an existing class of models for estimating phylogenetic trees from discrete morphological characters. The main advance of this work is a model that allows unequal equilibrium frequencies in the estimation of phylogenetic trees from discrete morphological character data using likelihood methods. Possible extensions of the proposed model will also be discussed.

Antioxidant activities of novel resveratrol analogs in breast cancer.

The objective of the present study was to characterize the role of novel resveratrol (Res) analogs: 4-(E)-{(4-hydroxyphenylimino)-methylbenzene, 1, 2-diol} (HPIMBD) and 4-(E)-{(p-tolylimino)-methylbenzene-1,2-diol} (TIMBD) as potent antioxidants against breast cancer. Non-neoplastic breast epithelial cell lines MCF-10A and MCF-10F were treated with 17ß-estradiol (E2), Res, HPIMBD, and TIMBD for up to 72 h. mRNA and protein levels of antioxidant genes, superoxide dismutase 3 (SOD3) and N-quinoneoxidoreductase-1 (NQO1) and transcription factors, nuclear factor erythroid 2-related factor (Nrf) 1, 2 and 3 were quantified after the above treatments. Generation of reactive oxygen species (ROS) was measured by CM-H2-DCFDA and oxidative-DNA damage was determined by measuring 8-hydroxy-2-deoxyguanosine (8-OHdG). HPIMBD and TIMBD scavenged cellular ROS production, attenuated oxidative DNA damage, increased mRNA and protein expression levels of SOD3 and NQO1 and activated Nrf signaling pathway. Our studies demonstrate that HPIMBD and TIMBD have the potential as novel antioxidants to prevent development of breast cancer.

Geometric ergodicity of a hybrid sampler for Bayesian inference of phylogenetic branch lengths.

One of the fundamental goals in phylogenetics is to make inferences about the evolutionary pattern among a group of individuals, such as genes or species, using present-day genetic material. This pattern is represented by a phylogenetic tree, and as computational methods have caught up to the statistical theory, Bayesian methods of making inferences about phylogenetic trees have become increasingly popular. Bayesian inference of phylogenetic trees requires sampling from intractable probability distributions. Common methods of sampling from these distributions include Markov chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) methods, and one way that both of these methods can proceed is by first simulating a tree topology and then taking a sample from the posterior distribution of the branch lengths given the tree topology and the data set. In many MCMC methods, it is difficult to verify that the underlying Markov chain is geometrically ergodic, and thus, it is necessary to rely on output-based convergence diagnostics in order to assess convergence on an ad hoc basis. These diagnostics suffer from several important limitations, so in an effort to circumvent these limitations, this work establishes geometric convergence for a particular Markov chain that is used to sample branch lengths under a fairly general class of nucleotide substitution models and provides a numerical method for estimating the time this Markov chain takes to converge.