Overactivation of Notch1 Signaling Induces Ectopic Hair Cells in the Mouse Inner Ear in an Age-Dependent Manner

Background: During mouse inner ear development, Notch1 signaling first specifies sensory progenitors, and subsequently controls progenitors to further differentiate into either hair cells (HCs) or supporting cells (SCs). Overactivation of NICD (Notch1 intracellular domain) at early embryonic stages leads to ectopic HC formation. However, it remains unclear whether such an effect can be elicited at later embryonic or postnatal stages, which has important implications in mouse HC regeneration by reactivation of Notch1 signaling. Methodology/Principal Findings: We performed comprehensive in vivo inducible overactivation of NICD at various developmental stages. In CAG CreER+; Rosa26-NICD loxp/+ mice, tamoxifen treatment at embryonic day 10.5 (E10.5) generated ectopic HCs in the non-sensory regions in both utricle and cochlea, whereas ectopic HCs only appeared in the utricle when tamoxifen was given at E13. When tamoxifen was injected at postnatal day 0 (P0) and P1, no ectopic HCs were observed in either utricle or cochlea. Interestingly, Notch1 signaling induced new HCs in a non-cell-autonomous manner, because the new HCs did not express NICD. Adjacent to the new HCs were cells expressing the SC marker Sox10 (either NICD+ or NICDnegative). Conclusions/Significance: Our data demonstrate that the developmental stage determines responsiveness of embryonic otic precursors and neonatal non-sensory epithelial cells to NICD overactivation, and that Notch 1 signaling in the wild type, postnatal inner ear is not sufficient for generating new HCs. Thus, our genetic mouse model is suitable to test additiona

Optimal foraging and community structure: implications for a guild of generalist grassland herbivores

A particular linear programming model is constructed to predict the diets of each of 14 species of generalist herbivores at the National Bison Range, Montana. The herbivores have body masses ranging over seven orders of magnitude and belonging to two major taxa: insects and mammals. The linear programming model has three feeding constraints: digestive capacity, feeding time and energy requirements. A foraging strategy that maximizes daily energy intake agrees very well with the observed diets. Body size appears to be an underlying determinant of the foraging parameters leading to diet selection. Species that possess digestive capacity and feeding time constraints which approach each other in magnitude have the most generalized diets. The degree that the linear programming models change their diet predictions with a given percent change in parameter values (sensitivity) may reflect the observed ability of the species to vary their diets. In particular, the species which show the most diet variability are those whose diets tend to be balanced between monocots and dicots. The community-ecological parameters of herbivore body-size ranges and species number can possibly be related to foraging behavior.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47765/1/442_2004_Article_BF00377109.pd

Overview and State-of-the-Art of Uncertainty Visualization

International audienceThe goal of visualization is to effectively and accurately communicate data. Visualization research has often overlooked the errors and uncertainty which accompany the scientific process and describe key characteristics used to fully understand the data. The lack of these representations can be attributed, in part, to the inherent difficulty in defining, characterizing, and controlling this uncertainty, and in part, to the difficulty in including additional visual metaphors in a well designed, potent display. However, the exclusion of this information cripples the use of visualization as a decision making tool due to the fact that the display is no longer a true representation of the data. This systematic omission of uncertainty commands fundamental research within the visualization community to address, integrate, and expect uncertainty information. In this chapter, we outline sources and models of uncertainty, give an overview of the state-of-the-art, provide general guidelines, outline small exemplary applications, and finally, discuss open problems in uncertainty visualization