A hidden Markov movement model for rapidly identifying behavioral states from animal tracks

Electronic telemetry is frequently used to document animal movement through time. Methods that can identify underlying behaviors driving specific movement patterns can help us understand how and why animals use available space, thereby aiding conservation and management efforts. For aquatic animal tracking data with significant measurement error, a Bayesian state‐space model called the first‐Difference Correlated Random Walk with Switching (DCRWS) has often been used for this purpose. However, for aquatic animals, highly accurate tracking data are now becoming more common. We developed a new hidden Markov model (HMM) for identifying behavioral states from animal tracks with negligible error, called the hidden Markov movement model (HMMM). We implemented as the basis for the HMMM the process equation of the DCRWS, but we used the method of maximum likelihood and the R package TMB for rapid model fitting. The HMMM was compared to a modified version of the DCRWS for highly accurate tracks, the DCRWS, and to a common HMM for animal tracks fitted with the R package moveHMM. We show that the HMMM is both accurate and suitable for multiple species by fitting it to real tracks from a grey seal, lake trout, and blue shark, as well as to simulated data. The HMMM is a fast and reliable tool for making meaningful inference from animal movement data that is ideally suited for ecologists who want to use the popular DCRWS implementation and have highly accurate tracking data. It additionally provides a groundwork for development of more complex modeling of animal movement with TMB. To facilitate its uptake, we make it available through the R package swim.     

On the necessity of different statistical treatment for Illumina BeadChip and Affymetrix GeneChip data and its significance for biological interpretation

Background  

The original spotted array technology with competitive hybridization of two experimental samples and measuring relative expression levels is increasingly displaced by more accurate platforms that allow determining absolute expression values for a single sample (for example, Affymetrix GeneChip and Illumina BeadChip). Unfortunately, cross-platform comparisons show a disappointingly low concordance between lists of regulated genes between the latter two platforms.     

A Toxoplasma type 2C serine-threonine phosphatase is involved in parasite growth in the mammalian host cell

Toxoplasma gondii is a human protozoan parasite that belongs to the phylum of Apicomplexa and causes toxoplasmosis. As the other members of this phylum, T. gondii obligatory multiplies within a host cell by a peculiar type of mitosis that leads to daughter cell assembly within a mother cell. Although parasite growth and virulence have been linked for years, few molecules controlling mitosis have been yet identified and they include a couple of kinases but not the counteracting phosphatases. Here, we report that in contrast to other animal cells, type 2C is by far the major type of serine threonine phosphatase activity both in extracellular and in intracellular dividing parasites. Using wild type and transgenic parasites, we characterized the 37 kDa TgPP2C molecule as an abundant cytoplasmic and nuclear enzyme with activity being under tight regulation. In addition, we showed that the increase in TgPP2C activity significantly affected parasite growth by impairing cytokinesis while nuclear division still occurred. This study supports for the first time that type 2C protein phosphatase is an important regulator of cell growth in T. gondii.