The effects of osmotic stress on the structure and function of the cell nucleus.

Osmotic stress is a potent regulator of the normal function of cells that are exposed to osmotically active environments under physiologic or pathologic conditions. The ability of cells to alter gene expression and metabolic activity in response to changes in the osmotic environment provides an additional regulatory mechanism for a diverse array of tissues and organs in the human body. In addition to the activation of various osmotically- or volume-activated ion channels, osmotic stress may also act on the genome via a direct biophysical pathway. Changes in extracellular osmolality alter cell volume, and therefore, the concentration of intracellular macromolecules. In turn, intracellular macromolecule concentration is a key physical parameter affecting the spatial organization and pressurization of the nucleus. Hyper-osmotic stress shrinks the nucleus and causes it to assume a convoluted shape, whereas hypo-osmotic stress swells the nucleus to a size that is limited by stretch of the nuclear lamina and induces a smooth, round shape of the nucleus. These behaviors are consistent with a model of the nucleus as a charged core/shell structure pressurized by uneven partition of macromolecules between the nucleoplasm and the cytoplasm. These osmotically-induced alterations in the internal structure and arrangement of chromatin, as well as potential changes in the nuclear membrane and pores are hypothesized to influence gene transcription and/or nucleocytoplasmic transport. A further understanding of the biophysical and biochemical mechanisms involved in these processes would have important ramifications for a range of fields including differentiation, migration, mechanotransduction, DNA repair, and tumorigenesis.

Latent Stick-Breaking Processes.

We develop a model for stochastic processes with random marginal distributions. Our model relies on a stick-breaking construction for the marginal distribution of the process, and introduces dependence across locations by using a latent Gaussian copula model as the mechanism for selecting the atoms. The resulting latent stick-breaking process (LaSBP) induces a random partition of the index space, with points closer in space having a higher probability of being in the same cluster. We develop an efficient and straightforward Markov chain Monte Carlo (MCMC) algorithm for computation and discuss applications in financial econometrics and ecology. This article has supplementary material online.

Thermodynamic and dynamic contributions to future changes in regional precipitation variance: focus on the Southeastern United States

© 2014, Springer-Verlag Berlin Heidelberg.The frequency and severity of extreme events are tightly associated with the variance of precipitation. As climate warms, the acceleration in hydrological cycle is likely to enhance the variance of precipitation across the globe. However, due to the lack of an effective analysis method, the mechanisms responsible for the changes of precipitation variance are poorly understood, especially on regional scales. Our study fills this gap by formulating a variance partition algorithm, which explicitly quantifies the contributions of atmospheric thermodynamics (specific humidity) and dynamics (wind) to the changes in regional-scale precipitation variance. Taking Southeastern (SE) United States (US) summer precipitation as an example, the algorithm is applied to the simulations of current and future climate by phase 5 of Coupled Model Intercomparison Project (CMIP5) models. The analysis suggests that compared to observations, most CMIP5 models (~60 %) tend to underestimate the summer precipitation variance over the SE US during the 1950–1999, primarily due to the errors in the modeled dynamic processes (i.e. large-scale circulation). Among the 18 CMIP5 models analyzed in this study, six of them reasonably simulate SE US summer precipitation variance in the twentieth century and the underlying physical processes; these models are thus applied for mechanistic study of future changes in SE US summer precipitation variance. In the future, the six models collectively project an intensification of SE US summer precipitation variance, resulting from the combined effects of atmospheric thermodynamics and dynamics. Between them, the latter plays a more important role. Specifically, thermodynamics results in more frequent and intensified wet summers, but does not contribute to the projected increase in the frequency and intensity of dry summers. In contrast, atmospheric dynamics explains the projected enhancement in both wet and dry summers, indicating its importance in understanding future climate change over the SE US. The results suggest that the intensified SE US summer precipitation variance is not a purely thermodynamic response to greenhouse gases forcing, and cannot be explained without the contribution of atmospheric dynamics. Our analysis provides important insights to understand the mechanisms of SE US summer precipitation variance change. The algorithm formulated in this study can be easily applied to other regions and seasons to systematically explore the mechanisms responsible for the changes in precipitation extremes in a warming climate.