Insulin and insulin receptor (IR) kinase are found in abundance in discrete brain regions yet insulin signaling in the CNS is not understood. Because it is known that the highest brain insulin-binding affinities, insulin-receptor density, and IR kinase activity are localized to the olfactory bulb, we sought to explore the downstream substrates for IR kinase in this region of the brain to better elucidate the function of insulin signaling in the CNS. First, we demonstrate that IR is postnatally and developmentally expressed in specific lamina of the highly plastic olfactory bulb (OB). ELISA testing confirms that insulin is present in the developing and adult OB. Plasma insulin levels are elevated above that found in the OB, which perhaps suggests a differential insulin pool. Olfactory bulb insulin levels appear not to be static, however, but are elevated as much as 15-fold after a 72-h fasting period. Bath application of insulin to cultured OB neurons acutely induces outward current suppression as studied by the use of traditional whole-cell and single-channel patch-clamp recording techniques. Modulation of OB neurons is restricted to current magnitude; IR kinase activation does not modulate current kinetics of inactivation or deactivation. Transient transfection of human embryonic kidney cells with cloned Kv1.3 ion channel, which carries a large proportion of the outward current in these neurons, revealed that current suppression was the result of multiple tyrosine phosphorylation of Kv1.3 channel. Y to F single-point mutations in the channel or deletion of the kinase domain in IR blocks insulin-induced modulation and phosphorylation of Kv1.3. Neuromodulation of Kv1.3 current in OB neurons is activity dependent and is eliminated after 20 days of odor/sensory deprivation induced by unilateral naris occlusion at postnatal day 1. IR kinase but not Kv1.3 expression is downregulated in the OB ipsilateral to the occlusion, as demonstrated in cryosections of right (control) and left (sensory-deprived) OB immunolabeled with antibodies directed against these proteins, respectively. Collectively, these data support the hypothesis that the hormone insulin acts as a multiply functioning molecule in the brain: IR signaling in the CNS could act as a traditional growth factor during development, be altered during energy metabolism, and simultaneously function to modulate electrical activity via phosphorylation of voltage-gated ion channels.

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With stock surpluses and shortages representing one of the greatest elements of risk to wholesalers, a solution to the multiretailer supply chain management problem would result in tremendous economic benefits.In this problem, a single wholesaler with multiple retailer customers must find an optimal balance of quantities ordered from suppliers and acceptable lead time costs, while taking into account limiting factors such as the time each retailer will wait for a backorder. The following four evolutionary computations (EC) are utilized to find a solution: evolutionary programming (EP), genetic algorithms (GA), particle swarm optimizers (PSO), and estimation of distribution algorithms (EDA). In addition, problem-specific modifications to each are created. Of the 32 attempted algorithms, the following proved to be best with respect to the client-mandated test-suite: Probabilistic Dual-Topology Full-Model PSO, Star-Topology Full-Model PSO using dynamically-adjusting learning rates, Outof-the-Box Star-Topology Full-Model PSO, and a Gaussian-based Star-Topology Full-Model PSO with the Constriction Coefficient. A secondary test-suite was also developed to test the effectiveness of the best algorithms on the problem. With respect to the clientmandated and the developed test suite's fitness threshold and maximum number of function evaluations, the best algorithm had an 87% and 90% success rate, respectively. Considering the flexibility and high performance of the solution and the generality of the problem, these results represent a significant contribution to commercial wholesaling.

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This article presents an agent‐based simulation study that explores the effects of team behavior on the efficiency and effectiveness of software development organizations that pursue incremental and iterative processes such as the Rational Unified Process (RUP). The conceptual model underlying the simulation framework is based on the fundamental tenets of organization theory. We present the simulation framework Team‐RUP and use it to examine (a) which team archetypes and associated organizational cooperation mechanisms are effective in incremental and iterative software development strategies such as the RUP and (b) the extent of the impact of turbulence (i.e. change in requirements and employee turnover) on the effectiveness of software development under various team archetypes in small organizations. Using the model, we observe that ‘agility’ via incremental and iterative development strategy is a valid and useful counterbalance to the inevitable change involved in most software projects. Also, we observe that the autonomous and concurrent team archetypes are better suited for large, rather than small, enterprises. We believe that the findings reported in this study form the basis for future hypotheses that will facilitate further empirical studies on software team behavior. Copyright © 2007 John Wiley & Sons, Ltd.