Pregnancy-induced maternal microchimerism shapes neurodevelopment and behavior in mice

Life-long brain function and mental health are critically determined by developmental processes occurring before birth. During mammalian pregnancy, maternal cells are transferred to the fetus. They are referred to as maternal microchimeric cells (MMc). Among other organs, MMc seed into the fetal brain, where their function is unknown. Here, we show that, in the offspring's developing brain in mice, MMc express a unique signature of sensome markers, control microglia homeostasis and prevent excessive presynaptic elimination. Further, MMc facilitate the oscillatory entrainment of developing prefrontal-hippocampal circuits and support the maturation of behavioral abilities. Our findings highlight that MMc are not a mere placental leak out, but rather a functional mechanism that shapes optimal conditions for healthy brain function later in life

The role of competition and clustering in population dynamics

A simple argument based on the distribution of individuals amongst discrete resource sites is used to show how the form of single species population models depends on the type of competition between, and the spatial clustering of, the individuals. For scramble competition between individuals, we confirm earlier demonstrations that the Ricker model is a direct consequence of a uniform random distribution of individuals across resources. By introducing spatial clustering of individuals according to a negative binomial distribution, we are able to derive the Hassell model. Furthermore, the tent map model is seen to be a consequence of scramble competition and an ideal-free distribution of individuals. To model contest competition under different degrees of spatial clustering we derive a new three-parameter model, of which the Beverton–Holt and Skellam models are special cases, where one of the parameters relates directly to the clustering distribution. Other population models, such as the quadratic model and the theta-Ricker models, cannot be derived in our framework. Taken together our derivations of population models allows us to make a more rigorous prescription for model choice when fitting to particular datasets

Vegetation growth models improve surface layer flux simulations of a temperate Grassland

Grassland models represent interactions of plant growth with soil and agricultural management based on underlying processes in different degrees of detail. To better understand the impact of these differences on the simulation of energy and matter exchange at the land-surface layer, we compared the ability of five land-surface models with different degrees of complexity to simulate energy fluxes in an intensively managed grassland in Switzerland. The aim was to evaluate the impacts of biomass growth, biomass harvest, soil profile characterization, and rooting depth on the dynamics of simulated near-surface soil moisture contents and energy fluxes. The case study included a comparison of model results with continuous observations of latent heat, sensible heat, and net radiation for a site-year. Energy fluxes were simulated more accurately by including a biomass growth model, encompassing the abrupt decline in leaf area caused by harvest. Sitespecific soil parametrization in combination with the absence of restrictions on rooting depth also improved the simulation results. The simulated energy fluxes of the five models differed significantly in the hot, dry month of July 2010 but were negligible under moist conditions in May. We conclude that the application of dynamic vegetation growth models improves energy flux simulations at the field scale in intensively managed grasslands during summer if biomass harvest dates and site-specific soil profile descriptions are considered. Our results imply that regional-scale simulations of grasslands will benefit significantly from high-resolution input information on soil properties, land use, and management