Previous studies have shown that prepubertal olfactory bulbectomy will prevent the testicular regression associated with short photoperiod in golden hamsters. The gonadal regression which normally occurs in hamsters on short photoperiod is known to be due in part to an increased responsiveness of the reproductive neuroendocrine system to the negative feedback actions of testosterone on LH and FSH secretion. The present study tested whether the olfactory bulbs influence the feedback effects of testosterone on gonadotropin secretion. Twenty-four- to 26-day-old male golden hamsters were either olfactory-bulbectomized (BX) or sham-olfactory-bulbectomized. Eight weeks later, all hamsters were castrated, and one half of each group was placed in LD 10:14 (this was called week -8 of the study), while the other half was returned to long photoperiod (LD 14:10). Eight weeks following castration (week 0 of the study), all animals were implanted with silastic capsules containing 0, 4, 8 or 16 mm of testosterone. All hamsters were bled by cardiac puncture at -8, -4, 0, +2, +4, +6 and + 8 weeks. The concentration of LH and FSH in these samples was then determined by RIA. BX completely prevented the negative feedback of testosterone on gonadotropin secretion in hamsters on either long or short photoperiod at all levels of testosterone tested in this study. In addition, there were seemingly steroid-independent effects of BX on gonadotropin levels in the castrated hamsters prior to testosterone replacement at weeks -4 and 0. These results are the first indication that the olfactory bulbs have an important role in regulating the responsiveness of the reproductive neuroendocrine axis to the feedback of testosterone on LH and FSH secretion. The data indicate that the ability of BX to prevent short-photoperiod-induced testicular regression may be one part of a much larger effect of the olfactory bulbs, and that the olfactory bulbs have an important influence on gonadotropin secretion in hamsters maintained on long or short photoperiod.
The miscibility, lattice parameter, and thermophysical properties of (Th0.2U0.8)N and (Th0.5U0.5)N have been investigated. It is shown that additions of thorium nitride (ThN) to uranium nitride (UN) increases the thermophysical performance of the mixed nitride fuel form in comparison to reference UN. In the more dilute limit, additions of ThN serve as a burnable neutronic poison and reduces the change in keff over the lifecycle of the fuel. At higher concentrations, additions of ThN serve as a significant fertile source of 233U. Where appropriate, comparisons to previous work on UN + PuN mixtures are made, as this is a comparable fuel form for potential fast reactor concepts, and a suitable point of contrast in the possible design space afforded by mixed (ThxU1 − x)N fuel forms. The data from this work are the input parameters for finite element modeling of the temperature distribution in a compact reactor. The results of modeling and simulation of this core design are shown for the case of steady-state operation and during double, adjacent heat pipe failure.
Various urban planning and managing activities required by a Smart City are feasible because of traffic monitoring. As such, this project proposes a network tomography-based approach that can be applied to road networks to achieve a cost-efficient, flexible, and scalable monitor deployment. Due to the algebraic approach of network tomography, the selection of monitoring intersections can be solved through the use of matrices, with its rows representing paths between two intersections, and its columns representing links in the road network. Because the goal of the algorithm is to provide an inexpensive monitor set, this problem can be translated into a minimization problem over a matroid, which can be solved efficiently by a greedy algorithm. This approach is applied to both real road networks, based on downtown San Francisco, and synthetic, QuadTree-based road networks. The solution retrieved by the proposed approach is compared to the results of Clarkson's algorithm, a greedy 2approximation algorithm designed to solve the weighted vertex cover problem.
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