Functional and neurometabolic asymmetry in SHR and WKY rats following vasoactive treatments

A lateralized distribution of neuropeptidase activities in the frontal cortex of normotensive and hypertensive rats has been described depending on the use of some vasoactive drugs and linked to certain mood disorders. Asymmetrical neuroperipheral connections involving neuropeptidases from the left or right hemisphere and aminopeptidases from the heart or plasma have been suggested to play a role in this asymmetry. We hypothesize that such asymmetries could be extended to the connection between the brain and physiologic parameters and metabolic factors from plasma and urine. To assess this hypothesis, we analyzed the possible correlation between neuropeptidases from the left and right frontal cortex with peripheral parameters in normotensive (Wistar Kyoto [WKY]) rats and hypertensive rats (spontaneously hypertensive rats [SHR]) untreated or treated with vasoactive drugs such as captopril, propranolol and L-nitro-arginine methyl ester. Neuropeptidase activities from the frontal cortex were analyzed fluorometrically using arylamide derivatives as substrates. Physiological parameters and metabolic factors from plasma and urine were determined using routine laboratory techniques. Vasoactive drug treatments differentially modified the asymmetrical neuroperipheral pattern by changing the predominance of the correlations between peripheral parameters and central neuropeptidase activities of the left and right frontal cortex. The response pattern also differed between SHR and WKY rats. These results support an asymmetric integrative function of the organism and suggest the possibility of a different neurometabolic response coupled to particular mood disorders, depending on the selected vasoactive drug.This work was supported by the Ministry of Science and Innovation through project no. SAF 2008 04685 C02 01

Robustness of optimal channel reservation using handover prediction in multiservice wireless networks

The aim of our study is to obtain theoretical limits for the gain that can be expected when using handover prediction and to determine the sensitivity of the system performance against different parameters. We apply an average-reward reinforcement learning approach based on afterstates to the design of optimal admission control policies in mobile multimedia cellular networks where predictive information related to the occurrence of future handovers is available. We consider a type of predictor that labels active mobile terminals in the cell neighborhood a fixed amount of time before handovers are predicted to occur, which we call the anticipation time. The admission controller exploits this information to reserve resources efficiently. We show that there exists an optimum value for the anticipation time at which the highest performance gain is obtained. Although the optimum anticipation time depends on system parameters, we find that its value changes very little when the system parameters vary within a reasonable range. We also find that, in terms of system performance, deploying prediction is always advantageous when compared to a system without prediction, even when the system parameters are estimated with poor precision. © Springer Science+Business Media, LLC 2012.The authors would like to thank the reviewers for their valuable comments that helped to improve the quality of the paper. This work has been supported by the Spanish Ministry of Education and Science and European Comission (30% PGE, 70% FEDER) under projects TIN2008-06739-C04-02 and TIN2010-21378-C02-02 and by Comunidad de Madrid through project S-2009/TIC-1468.Martínez Bauset, J.; Giménez Guzmán, JM.; Pla, V. (2012). Robustness of optimal channel reservation using handover prediction in multiservice wireless networks. Wireless Networks. 18(6):621-633. https://doi.org/10.1007/s11276-012-0423-6S621633186Ji, S., Chen, W., Ding, X., Chen, Y., Zhao, C., & Hu, C. (2010). Potential benefits of GPS/GLONASS/GALILEO integration in an urban canyon–Hong Kong. Journal of Navigation, 63(4), 681–693.Soh, W., & Kim, H. (2006). A predictive bandwidth reservation scheme using mobile positioning and road topology information. IEEE/ACM Transactions on Networking, 14(5), 1078–1091.Kwon, H., Yang, M., Park, A., & Venkatesan, S. (2008). Handover prediction strategy for 3G-WLAN overlay networks. In Proceedings: IEEE network operations and management symposium (NOMS) (pp. 819–822).Huang, C., Shen, H., & Chuang, Y. (2010). An adaptive bandwidth reservation scheme for 4G cellular networks using flexible 2-tier cell structure. Expert Systems with Applications, 37(9), 6414–6420.Wanalertlak, W., Lee, B., Yu, C., Kim, M., Park, S., & Kim, W. (2011). Behavior-based mobility prediction for seamless handoffs in mobile wireless networks. Wireless Networks, 17(3), 645–658.Becvar, Z., Mach, P., & Simak, B. (2011). Improvement of handover prediction in mobile WiMAX by using two thresholds. Computer Networks, 55, 3759–3773.Sgora, A., & Vergados, D. (2009). Handoff prioritization and decision schemes in wireless cellular networks: a survey. IEEE Communications Surveys and Tutorials, 11(4), 57–77.Choi, S., & Shin, K. G. (2002). Adaptive bandwidth reservation and admission control in QoS-sensitive cellular networks. IEEE Transactions on Parallel and Distributed Systems, 13(9), 882–897.Ye, Z., Law, L., Krishnamurthy, S., Xu, Z., Dhirakaosal, S., Tripathi, S., & Molle, M. (2007). Predictive channel reservation for handoff prioritization in wireless cellular networks. Computer Networks, 51(3), 798–822.Abdulova, V., & Aybay, I. (2011). Predictive mobile-oriented channel reservation schemes in wireless cellular networks. Wireless Networks, 17(1), 149–166.Ramjee, R., Nagarajan, R., & Towsley, D. (1997). On optimal call admission control in cellular networks. Wireless Networks, 3(1), 29–41.Bartolini, N. (2001). Handoff and optimal channel assignment in wireless networks. Mobile Networks and Applications, 6(6), 511–524.Bartolini, N., & Chlamtac, I. (2002). Call admission control in wireless multimedia networks. In Proceedings: Personal, indoor and mobile radio communications (PIMRC) (pp. 285–289).Pla, V., & Casares-Giner, V. (2003). Optimal admission control policies in multiservice cellular networks. In Proceedings of the international network optimization conference (INOC) (pp. 466–471).Chu, K., Hung, L., & Lin, F. (2009). Adaptive channel reservation for call admission control to support prioritized soft handoff calls in a cellular CDMA system. Annals of Telecommunications, 64(11), 777–791.El-Alfy, E., & Yao, Y. (2011). Comparing a class of dynamic model-based reinforcement learning schemes for handoff prioritization in mobile communication networks. Expert Systems With Applications, 38(7), 8730–8737.Gimenez-Guzman, J. M., Martinez-Bauset, J., & Pla, V. (2007). A reinforcement learning approach for admission control in mobile multimedia networks with predictive information. IEICE Transactions on Communications , E-90B(7), 1663–1673.Sutton R., Barto A. G. (1998) Reinforcement learning: An introduction. The MIT press, Cambridge, MassachusettsBusoniu, L., Babuska, R., De Schutter, B., & Ernst, D. (2010). Reinforcement learning and dynamic programming using function approximators. Boca Raton, FL: CRC Press.Watkins, C., & Dayan, P. (1992). Q-learning. Machine learning, 8(3–4), 279–292.Brown, T. (2001). Switch packet arbitration via queue-learning. Advances in Neural Information Processing Systems, 14, 1337–1344.Proper, S., & Tadepalli, P. (2006). Scaling model-based average-reward reinforcement learning for product delivery. In Proceedings 17th European conference on machine learning (pp. 735–742).Driessens, K., Ramon, J., & Gärtner, T. (2006). Graph kernels and Gaussian processes for relational reinforcement learning. Machine Learning, 64(1), 91–119.Banerjee, B., & Stone, P. (2007). General game learning using knowledge transfer. In Proceedings 20th international joint conference on artificial intelligence (pp. 672–677).Martinez-Bauset, J., Pla, V., Garcia-Roger, D., Domenech-Benlloch, M. J., & Gimenez-Guzman, J. M. (2008). Designing admission control policies to minimize blocking/forced-termination. In G. Ming, Y. Pan & P. Fan (Eds.), Advances in wireless networks: Performance modelling, analysis and enhancement (pp. 359–390). New York: Nova Science Pub Inc.Biswas, S., & Sengupta, B. (1997). Call admissibility for multirate traffic in wireless ATM networks. In Proceedings IEEE INFOCOM (2, pp. 649–657).Evans, J. S., & Everitt, D. (1999). Effective bandwidth-based admission control for multiservice CDMA cellular networks. IEEE Transactions on Vehicular Technology, 48(1), 36–46.Gilhousen, K., Jacobs, I., Padovani, R., Viterbi, A., Weaver, L. A. J., & Wheatley, C. E., III. (1991). On the capacity of a cellular CDMA system. IEEE Transactions on Vehicular Technology, 40(2), 303–312.Hegde, N., & Altman, E. (2006). Capacity of multiservice WCDMA networks with variable GoS. Wireless Networks, 12, 241–253.Ben-Shimol, Y., Kitroser, I., & Dinitz, Y. (2006). Two-dimensional mapping for wireless OFDMA systems. IEEE Transactions on Broadcasting, 52(3), 388–396.Gao, D., Cai, J., & Ngan, K. N. (2005). Admission control in IEEE 802.11e wireless LANs. IEEE Network, 19(4), 6–13.Liu, T., Bahl, P., & Chlamtac, I. (1998). Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks. IEEE Journal on Selected Areas in Communications, 16(6), 922–936.Hu, F., & Sharma, N. (2004). Priority-determined multiclass handoff scheme with guaranteed mobile qos in wireless multimedia networks. IEEE Transactions on Vehicular Technology, 53(1), 118–135.Chan, J., & Seneviratne, A. (1999). A practical user mobility prediction algorithm for supporting adaptive QoS in wireless networks. In Proceedings IEEE international conference on networks (ICON) (pp. 104–111).Jayasuriya, A., & Asenstorfer, J. (2002). Mobility prediction model for cellular networks based on the observed traffic patterns. In Proceedings of IASTED international conference on wireless and optical communication (WOC) (pp. 386–391).Diederich, J., & Zitterbart, M. (2005). A simple and scalable handoff prioritization scheme. Computer Communications, 28(7), 773–789.Rashad, S., Kantardzic, M., & Kumar, A. (2006). User mobility oriented predictive call admission control and resource reservation for next-generation mobile networks. Journal of Parallel and Distributed Computing, 66(7), 971–988.Soh, W. -S., & Kim, H. (2003). QoS provisioning in cellular networks based on mobility prediction techniques. IEEE Communications Magazine, 41(1), 86 – 92.Lott, M., Siebert, M., Bonjour, S., vonHugo, D., & Weckerle, M. (2004). Interworking of WLAN and 3G systems. Proceedings IEE Communications, 151(5), 507 – 513.Sanabani, M., Shamala, S., Othman, M., & Zukarnain, Z. (2007). An enhanced bandwidth reservation scheme based on road topology information for QoS sensitive multimedia wireless cellular networks. In Proceedings of the 2007 international conference on computational science and its applications—Part II (ICCSA) (pp. 261–274).Mahadevan, S. (1996). Average reward reinforcement learning: Foundations, algorithms, and empirical results. Machine Learning, 22(1–3), 159–196.Puterman, M. L. (1994). Markov decision processes: Discrete stochastic dynamic programming. New York: Wiley.Das, T. K., Gosavi, A., Mahadevan, S., & Marchalleck, N. (1999). Solving semi-markov decision problems using average reward reinforcement learning. Management Science, 45(4), 560–574.Darken, C., Chang, J., & Moody, J. (1992). Learning rate schedules for faster stochastic gradient search. In Proceedings of the IEEE-SP workshop on neural networks for signal processing II. (pp. 3–12)

Intracellular pathways regulating ciliary beating of rat brain ependymal cells

The mammalian brain ventricles are lined with ciliated ependymal cells. As yet little is known about the mechanisms by which neurotransmitters regulate cilia beat frequency (CBF).Application of 5-HT to ependymal cells in cultured rat brainstem slices caused CBF to increase. 5-HT had an EC50 of 30 μM and at 100 μM attained a near-maximal CBF increase of 52.7 ± 4.1 % (mean ± s.d.) (n= 8).Bathing slices in Ca2+-free solution markedly reduced the 5-HT-mediated increase in CBF. Fluorescence measurements revealed that 5-HT caused a marked transient elevation in cytosolic Ca2+ ([Ca2+]c) that then slowly decreased to a plateau level. Analysis showed that the [Ca2+]c transient was due to release of Ca2+ from inositol 1,4,5-trisphosphate (IP3)-sensitive stores; the plateau was probably due to extracellular Ca2+ influx through Ca2+ release-activated Ca2+ (CRAC) channels.Application of ATP caused a sustained decrease in CBF. ATP had an EC50 of about 50 μM and 100 μM ATP resulted in a maximal 57.5 ± 6.5 % (n= 12) decrease in CBF. The ATP-induced decrease in CBF was unaffected by lowering extracellular [Ca2+], and no changes in [Ca2+]c were observed. Exposure of ependymal cells to forskolin caused a decrease in CBF. Ciliated ependymal cells loaded with caged cAMP exhibited a 54.3 ± 7.5 % (n= 9) decrease in CBF following uncaging. These results suggest that ATP reduces CBF by a Ca2+-independent cAMP-mediated pathway.Application of 5-HT and adenosine-5′-O-3-thiotriphosphate (ATP-γ-S) to acutely isolated ciliated ependymal cells resulted in CBF responses similar to those of ependymal cells in cultured slices suggesting that these neurotransmitters act directly on these cells.The opposite response of ciliated ependymal cells to 5-HT and ATP provides a novel mechanism for their active involvement in central nervous system signalling