1,428 research outputs found

    What do we know about the transient receptor potential vanilloid 2 (TRPV2) ion channel?

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    Transient receptor potential (TRP) ion channels are emerging as a new set of membrane proteins involved in a vast array of cellular processes and regulated by a large number of physical and chemical stimuli, which involves them with sensory cell physiology. The vanilloid TRP subfamily (TRPV) named after the vanilloid receptor 1 (TRPV1) consists of six members, and at least four of them (TRPV1–TRPV4) have been related to thermal sensation. One of the least characterized members of the TRP subfamily is TRPV2. Although initially characterized as a noxious heat sensor, TRPV2 now seems to have little to do with temperature sensing but a much more complex physiological profile. Here we review the available information and research progress on the structure, physiology and pharmacology of TRPV2 in an attempt to shed some light on the physiological and pharmacological deorphanization of TRPV2.Molecular and Cellular Biolog

    The brain signature of paracetamol in healthy volunteers: a double-blind randomized trial

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    International audienceBackground: Paracetamol’s (APAP) mechanism of action suggests the implication of supraspinal structures but no neuroimaging study has been performed in humans.Methods and results: This randomized, double-blind, crossover, placebo-controlled trial in 17 healthy volunteers (NCT01562704) aimed to evaluate how APAP modulates pain-evoked functional magnetic resonance imaging signals. We used behavioral measures and functional magnetic resonance imaging to investigate the response to experimental thermal stimuli with APAP or placebo administration. Region-of-interest analysis revealed that activity in response to noxious stimulation diminished with APAP compared to placebo in prefrontal cortices, insula, thalami, anterior cingulate cortex, and periaqueductal gray matter.Conclusion: These findings suggest an inhibitory effect of APAP on spinothalamic tracts leading to a decreased activation of higher structures, and a top-down influence on descending inhibition. Further binding and connectivity studies are needed to evaluate how APAP modulates pain, especially in the context of repeated administration to patients with pain

    Cervical artery dissection: An atypical presentation with Ehlers-Danlos-like collagen pathology?

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    The authors took skin biopsies of the macroscopically normal skin of seven consecutive patients with spontaneous cervical artery dissection (SCAD). Histologically, alterations of the collagen and elastic fiber networks were found in six patients. In five, the histologic, immunohistochemical, and ultrastructural changes were similar to those usually found in Ehlers-Danlos syndrome (EDS). This suggests that SCAD is frequently associated with the dermal alterations seen in EDS

    A kinetic view of nonlocal self-avoiding processes

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    Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2025, Tutor: Oriol Artime VilaSelf-avoiding walks (SAWs) are central to modeling excluded-volume effects in polymers and related systems. SAWs are typically defined with local, nearest-neighbor steps, and their statistical behavior is well studied. In contrast, less is known about SAWs involving nonlocal motion. We study how introducing nonlocality — in the form of fixed-length jumps inspired by the knight’s move in chess — affects kinetic self-avoiding walks (GSAWs), where paths grow irreversibly. The resulting model, called the Self-Avoiding Random Knight (SARK), replaces local propagation with constrained long-range steps. Using large-scale simulations, we examine how this dynamic influences key properties of the walk. We find that nonlocality increases the walker’s lifetime and spatial extent. The end-to-end distance shows a crossover in scaling behavior, approaching that of the GSAW at long times. Clustering analysis reveals a dominant connected component in small lattices, which vanishes in larger ones. These results offer insight into how nonlocal constraints shape the geometry and growth of SAWs, with possible applications in ecological foraging and transport in constrained environments

    The LQLP Calcineurin-docking Site Is a Major Determinant of the Calcium-dependent Activation of Human TRESK Background K+ Channel

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    Calcium-dependent activation of human TRESK (TWIK-related spinal cord K+ channel, K2P18.1) depends on direct targeting of calcineurin to the PQIIIS motif. In the present study we demonstrate that TRESK also contains another functionally relevant docking site for the phosphatase, the LQLP amino acid sequence. Combined mutations of the PQIIIS and LQLP motifs were required to eliminate the calcium-dependent regulation of the channel. In contrast to the alanine substitutions of PQIIIS, the mutation of LQLP to AQAP alone did not significantly change the amplitude of TRESK activation evoked by the substantial elevation of cytoplasmic calcium concentration. However, the AQAP mutation slowed down the response to high calcium. In addition, modest elevation of [Ca2+], which effectively regulated the wild type channel, failed to activate TRESK-AQAP. This indicates that the AQAP mutation diminished the sensitivity of TRESK to calcium. Even if PQIIIS was replaced by the PVIVIT sequence of high calcineurin-binding affinity, the effect of the AQAP mutation was clearly detected in this TRESK-PVIVIT context. Substitution of the LQLP region with the corresponding fragment of NFAT transcription factor, perfectly matching the previously described LxVP calcineurin-binding consensus sequence, increased the calcium-sensitivity of TRESK-PVIVIT. Thus the enhancement of the affinity of TRESK for calcineurin by the incorporation of PVIVIT could not compensate for or prevent the effects of LQLP sequence modifications, suggesting that the two calcineurin-binding regions play distinct roles in the regulation. Our results indicate that the LQLP site is a fundamental determinant of the calcium-sensitivity of human TRESK

    Inferring efficient operating rules in multireservoir water resource systems: A review

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    [EN] Coordinated and efficient operation of water resource systems becomes essential to deal with growing demands and uncertain resources in water-stressed regions. System analysis models and tools help address the complexities of multireservoir systems when defining operating rules. This paper reviews the state of the art in developing operating rules for multireservoir water resource systems, focusing on efficient system operation. This review focuses on how optimal operating rules can be derived and represented. Advantages and drawbacks of each approach are discussed. Major approaches to derive optimal operating rules include direct optimization of reservoir operation, embedding conditional operating rules in simulation-optimization frameworks, and inferring rules from optimization results. Suggestions on which approach to use depend on context. Parametrization-simulation-optimization or rule inference using heuristics are promising approaches. Increased forecasting capabilities will further benefit the use of model predictive control algorithms to improve system operation. This article is categorized under: Engineering Water > Water, Health, and Sanitation Engineering Water > MethodsThe study has been partially funded by the ADAPTAMED project (RTI2018-101483-B-I00) from the Ministerio de Ciencia, Innovacion Universidades (MICINN) of Spain, and by the postdoctoral program (PAID-10-18) of the Universitat Politecnica de Valencia (UPV).Macian-Sorribes, H.; Pulido-Velazquez, M. (2019). Inferring efficient operating rules in multireservoir water resource systems: A review. Wiley Interdisciplinary Reviews Water. 7(1):1-24. https://doi.org/10.1002/wat2.1400S12471Aboutalebi, M., Bozorg Haddad, O., & Loáiciga, H. A. (2015). Optimal Monthly Reservoir Operation Rules for Hydropower Generation Derived with SVR-NSGAII. Journal of Water Resources Planning and Management, 141(11), 04015029. doi:10.1061/(asce)wr.1943-5452.0000553Ahmad, A., El-Shafie, A., Razali, S. F. M., & Mohamad, Z. S. (2014). Reservoir Optimization in Water Resources: a Review. Water Resources Management, 28(11), 3391-3405. doi:10.1007/s11269-014-0700-5Ahmadi, M., Bozorg Haddad, O., & Mariño, M. A. (2013). Extraction of Flexible Multi-Objective Real-Time Reservoir Operation Rules. Water Resources Management, 28(1), 131-147. doi:10.1007/s11269-013-0476-zAndreu, J., Capilla, J., & Sanchís, E. (1996). AQUATOOL, a generalized decision-support system for water-resources planning and operational management. Journal of Hydrology, 177(3-4), 269-291. doi:10.1016/0022-1694(95)02963-xAndreu, J., & Sahuquillo, A. (1987). Efficient Aquifer Simulation in Complex Systems. Journal of Water Resources Planning and Management, 113(1), 110-129. doi:10.1061/(asce)0733-9496(1987)113:1(110)Ashbolt, S. C., Maheepala, S., & Perera, B. J. C. (2016). Using Multiobjective Optimization to Find Optimal Operating Rules for Short-Term Planning of Water Grids. Journal of Water Resources Planning and Management, 142(10), 04016033. doi:10.1061/(asce)wr.1943-5452.0000675Ashbolt, S. C., & Perera, B. J. C. (2018). Multiobjective Optimization of Seasonal Operating Rules for Water Grids Using Streamflow Forecast Information. Journal of Water Resources Planning and Management, 144(4), 05018003. doi:10.1061/(asce)wr.1943-5452.0000902Azari, A., Hamzeh, S., & Naderi, S. (2018). Multi-Objective Optimization of the Reservoir System Operation by Using the Hedging Policy. Water Resources Management, 32(6), 2061-2078. doi:10.1007/s11269-018-1917-5Becker, L., & Yeh, W. W.-G. (1974). Optimization of real time operation of a multiple-reservoir system. Water Resources Research, 10(6), 1107-1112. doi:10.1029/wr010i006p01107Bellman, R. E., & Dreyfus, S. E. (1962). Applied Dynamic Programming. doi:10.1515/9781400874651Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust Optimization. doi:10.1515/9781400831050Bessler, F. T., Savic, D. A., & Walters, G. A. (2003). Water Reservoir Control with Data Mining. Journal of Water Resources Planning and Management, 129(1), 26-34. doi:10.1061/(asce)0733-9496(2003)129:1(26)Bhaskar, N. R., & Whitlatch, E. E. (1980). Derivation of monthly reservoir release policies. Water Resources Research, 16(6), 987-993. doi:10.1029/wr016i006p00987Bianucci, P., Sordo-Ward, Á., Moralo, J., & Garrote, L. (2015). Probabilistic-Multiobjective Comparison of User-Defined Operating Rules. Case Study: Hydropower Dam in Spain. Water, 7(12), 956-974. doi:10.3390/w7030956Biglarbeigi, P., Giuliani, M., & Castelletti, A. (2018). Partitioning the Impacts of Streamflow and Evaporation Uncertainty on the Operations of Multipurpose Reservoirs in Arid Regions. Journal of Water Resources Planning and Management, 144(7), 05018008. doi:10.1061/(asce)wr.1943-5452.0000945Bolouri-Yazdeli, Y., Bozorg Haddad, O., Fallah-Mehdipour, E., & Mariño, M. A. (2014). Evaluation of Real-Time Operation Rules in Reservoir Systems Operation. Water Resources Management, 28(3), 715-729. doi:10.1007/s11269-013-0510-1Borgomeo, E., Mortazavi-Naeini, M., Hall, J. W., O’Sullivan, M. J., & Watson, T. (2016). Trading-off tolerable risk with climate change adaptation costs in water supply systems. Water Resources Research, 52(2), 622-643. doi:10.1002/2015wr018164Bozorg-Haddad, O., Azarnivand, A., Hosseini-Moghari, S.-M., & Loáiciga, H. A. (2017). WASPAS Application and Evolutionary Algorithm Benchmarking in Optimal Reservoir Optimization Problems. Journal of Water Resources Planning and Management, 143(1), 04016070. doi:10.1061/(asce)wr.1943-5452.0000716Bozorg-Haddad, O., Karimirad, I., Seifollahi-Aghmiuni, S., & Loáiciga, H. A. (2015). Development and Application of the Bat Algorithm for Optimizing the Operation of Reservoir Systems. Journal of Water Resources Planning and Management, 141(8), 04014097. doi:10.1061/(asce)wr.1943-5452.0000498Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324Brown, C., Ghile, Y., Laverty, M., & Li, K. (2012). Decision scaling: Linking bottom-up vulnerability analysis with climate projections in the water sector. Water Resources Research, 48(9). doi:10.1029/2011wr011212Brown, C. M., Lund, J. R., Cai, X., Reed, P. M., Zagona, E. A., Ostfeld, A., … Brekke, L. (2015). The future of water resources systems analysis: Toward a scientific framework for sustainable water management. Water Resources Research, 51(8), 6110-6124. doi:10.1002/2015wr017114Cai, X., McKinney, D. C., & Lasdon, L. S. (2001). Piece-by-Piece Approach to Solving Large Nonlinear Water Resources Management Models. Journal of Water Resources Planning and Management, 127(6), 363-368. doi:10.1061/(asce)0733-9496(2001)127:6(363)Cai, X., Vogel, R., & Ranjithan, R. (2013). Special Issue on the Role of Systems Analysis in Watershed Management. Journal of Water Resources Planning and Management, 139(5), 461-463. doi:10.1061/(asce)wr.1943-5452.0000341Cancelliere, A., Giuliano, G., Ancarani, A., & Rossi, G. (2002). Water Resources Management, 16(1), 71-88. doi:10.1023/a:1015563820136Caseri, A., Javelle, P., Ramos, M. H., & Leblois, E. (2015). Generating precipitation ensembles for flood alert and risk management. Journal of Flood Risk Management, 9(4), 402-415. doi:10.1111/jfr3.12203Castelletti, A., Galelli, S., Restelli, M., & Soncini-Sessa, R. (2010). Tree-based reinforcement learning for optimal water reservoir operation. Water Resources Research, 46(9). doi:10.1029/2009wr008898Castelletti, A., Pianosi, F., & Restelli, M. (2013). A multiobjective reinforcement learning approach to water resources systems operation: Pareto frontier approximation in a single run. Water Resources Research, 49(6), 3476-3486. doi:10.1002/wrcr.20295Castelletti, A., Pianosi, F., & Soncini-Sessa, R. (2008). Water reservoir control under economic, social and environmental constraints. Automatica, 44(6), 1595-1607. doi:10.1016/j.automatica.2008.03.003Castelletti, A., & Soncini-Sessa, R. (2007). Bayesian networks in water resource modelling and management. Environmental Modelling & Software, 22(8), 1073-1074. doi:10.1016/j.envsoft.2006.06.001Castelletti, A., & Soncini-Sessa, R. (2007). Bayesian Networks and participatory modelling in water resource management. Environmental Modelling & Software, 22(8), 1075-1088. doi:10.1016/j.envsoft.2006.06.003Celeste, A. B., & Billib, M. (2009). Evaluation of stochastic reservoir operation optimization models. Advances in Water Resources, 32(9), 1429-1443. doi:10.1016/j.advwatres.2009.06.008Celeste, A. B., Curi, W. F., & Curi, R. C. (2009). Implicit Stochastic Optimization for deriving reservoir operating rules in semiarid Brazil. Pesquisa Operacional, 29(1), 223-234. doi:10.1590/s0101-74382009000100011Chandramouli, V., & Raman, H. (2001). Multireservoir Modeling with Dynamic Programming and Neural Networks. Journal of Water Resources Planning and Management, 127(2), 89-98. doi:10.1061/(asce)0733-9496(2001)127:2(89)Chang, L.-C., & Chang, F.-J. (2001). Intelligent control for modelling of real-time reservoir operation. Hydrological Processes, 15(9), 1621-1634. doi:10.1002/hyp.226Chazarra, M., García-González, J., Pérez-Díaz, J. I., & Arteseros, M. (2016). Stochastic optimization model for the weekly scheduling of a hydropower system in day-ahead and secondary regulation reserve markets. Electric Power Systems Research, 130, 67-77. doi:10.1016/j.epsr.2015.08.014Chen, D., Leon, A. S., Fuentes, C., Gibson, N. L., & Qin, H. (2018). Incorporating Filters in Random Search Algorithms for the Hourly Operation of a Multireservoir System. Journal of Water Resources Planning and Management, 144(2), 04017088. doi:10.1061/(asce)wr.1943-5452.0000876Coerver, H. M., Rutten, M. M., & van de Giesen, N. C. (2018). Deduction of reservoir operating rules for application in global hydrological models. Hydrology and Earth System Sciences, 22(1), 831-851. doi:10.5194/hess-22-831-2018Côté, P., & Leconte, R. (2016). Comparison of Stochastic Optimization Algorithms for Hydropower Reservoir Operation with Ensemble Streamflow Prediction. Journal of Water Resources Planning and Management, 142(2), 04015046. doi:10.1061/(asce)wr.1943-5452.0000575Cui, L., & Kuczera, G. (2005). Optimizing water supply headworks operating rules under stochastic inputs: Assessment of genetic algorithm performance. Water Resources Research, 41(5). doi:10.1029/2004wr003517Culley, S., Noble, S., Yates, A., Timbs, M., Westra, S., Maier, H. R., … Castelletti, A. (2016). A bottom-up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate. Water Resources Research, 52(9), 6751-6768. doi:10.1002/2015wr018253Cunha, M. C., & Antunes, A. (2012). Simulated annealing algorithms for water systems optimization. WIT Transactions on State of the Art in Science and Engineering, 57-73. doi:10.2495/978-1-84564-664-6/04Dariane, A. B., & Momtahen, S. (2009). Optimization of Multireservoir Systems Operation Using Modified Direct Search Genetic Algorithm. Journal of Water Resources Planning and Management, 135(3), 141-148. doi:10.1061/(asce)0733-9496(2009)135:3(141)Das, B., Singh, A., Panda, S. N., & Yasuda, H. (2015). Optimal land and water resources allocation policies for sustainable irrigated agriculture. Land Use Policy, 42, 527-537. doi:10.1016/j.landusepol.2014.09.012Davidsen, C., Liu, S., Mo, X., Rosbjerg, D., & Bauer-Gottwein, P. (2016). The cost of ending groundwater overdraft on the North China Plain. Hydrology and Earth System Sciences, 20(2), 771-785. doi:10.5194/hess-20-771-2016Ehteram, M., Karami, H., & Farzin, S. (2018). Reservoir Optimization for Energy Production Using a New Evolutionary Algorithm Based on Multi-Criteria Decision-Making Models. Water Resources Management, 32(7), 2539-2560. doi:10.1007/s11269-018-1945-1Eisel, L. M. (1972). Chance constrained reservoir model. Water Resources Research, 8(2), 339-347. doi:10.1029/wr008i002p00339European Commission(2007). Communication from the Commission to the European Parliament and the Council: Addressing the challenge of water scarcity and droughts in the European Union COM(2007) 414 final. Brussels Belgium.European Commission. (2012a). Communication from the Commission to the European Parliament the Council the European Economic and Social Committee and the Committee of the Regions: A Blueprint to Safeguard Europe's Water Resources COM(2012) 673 final. Brussels Belgium.European Commission. (2012b). Communication from the Commission to the European Parliament the Council the European Economic and Social Committee and the Committee of the Regions: Report on the Review of the European Water Scarcity and Droughts Policy COM(2012) 672 final. Brussels Belgium.Fallah-Mehdipour, E., Bozorg Haddad, O., & Mariño, M. A. (2012). Real-Time Operation of Reservoir System by Genetic Programming. Water Resources Management, 26(14), 4091-4103. doi:10.1007/s11269-012-0132-zFazlali, A., & Shourian, M. (2017). A Demand Management Based Crop and Irrigation Planning Using the Simulation-Optimization Approach. Water Resources Management, 32(1), 67-81. doi:10.1007/s11269-017-1791-6Ficchì, A., Raso, L., Dorchies, D., Pianosi, F., Malaterre, P.-O., Van Overloop, P.-J., & Jay-Allemand, M. (2016). Optimal Operation of the Multireservoir System in the Seine River Basin Using Deterministic and Ensemble Forecasts. Journal of Water Resources Planning and Management, 142(1), 05015005. doi:10.1061/(asce)wr.1943-5452.0000571Fu, Q., Li, T., Cui, S., Liu, D., & Lu, X. (2017). Agricultural Multi-Water Source Allocation Model Based on Interval Two-Stage Stochastic Robust Programming under Uncertainty. Water Resources Management, 32(4), 1261-1274. doi:10.1007/s11269-017-1868-2Galelli, S., Goedbloed, A., Schwanenberg, D., & van Overloop, P.-J. (2014). Optimal Real-Time Operation of Multipurpose Urban Reservoirs: Case Study in Singapore. Journal of Water Resources Planning and Management, 140(4), 511-523. doi:10.1061/(asce)wr.1943-5452.0000342Giuliani, M., Castelletti, A., Pianosi, F., Mason, E., & Reed, P. M. (2016). Curses, Tradeoffs, and Scalable Management: Advancing Evolutionary Multiobjective Direct Policy Search to Improve Water Reservoir Operations. Journal of Water Resources Planning and Management, 142(2), 04015050. doi:10.1061/(asce)wr.1943-5452.0000570Giuliani, M., Herman, J. D., Castelletti, A., & Reed, P. (2014). Many-objective reservoir policy identification and refinement to reduce policy inertia and myopia in water management. Water Resources Research, 50(4), 3355-3377. doi:10.1002/2013wr014700Giuliani, M., Li, Y., Castelletti, A., & Gandolfi, C. (2016). A coupled human-natural systems analysis of irrigated agriculture under changing climate. Water Resources Research, 52(9), 6928-6947. doi:10.1002/2016wr019363Giuliani, M., Quinn, J. D., Herman, J. D., Castelletti, A., & Reed, P. M. (2018). Scalable Multiobjective Control for Large-Scale Water Resources Systems Under Uncertainty. IEEE Transactions on Control Systems Technology, 26(4), 1492-1499. doi:10.1109/tcst.2017.2705162Grüne, L., & Semmler, W. (2004). Using dynamic programming with adaptive grid scheme for optimal control problems in economics. Journal of Economic Dynamics and Control, 28(12), 2427-2456. doi:10.1016/j.jedc.2003.11.002Guariso, G., Rinaldi, S., & Soncini-Sessa, R. (1986). The Management of Lake Como: A Multiobjective Analysis. Water Resources Research, 22(2), 109-120. doi:10.1029/wr022i002p00109Gundelach, J., & ReVelle, C. (1975). Linear decision rule in reservoir management and design: 5. A general algorithm. Water Resources Research, 11(2), 204-207. doi:10.1029/wr011i002p00204Guo, X., Hu, T., Zeng, X., & Li, X. (2013). Extension of Parametric Rule with the Hedging Rule for Managing Multireservoir System during Droughts. Journal of Water Resources Planning and Management, 139(2), 139-148. doi:10.1061/(asce)wr.1943-5452.0000241Haddad, O. B., Afshar, A., & Mariño, M. A. (2006). Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization. Water Resources Management, 20(5), 661-680. doi:10.1007/s11269-005-9001-3Hadka, D., Herman, J., Reed, P., & Keller, K. (2015). An open source framework for many-objective robust decision making. Environmental Modelling & Software, 74, 114-129. doi:10.1016/j.envsoft.2015.07.014Haguma, D., & Leconte, R. (2018). Long-Term Planning of Water Systems in the Context of Climate Non-Stationarity with Deterministic and Stochastic Optimization. Water Resources Management, 32(5), 1725-1739. doi:10.1007/s11269-017-1900-6Haguma, D., Leconte, R., & Côté, P. (2018). Evaluating Transition Probabilities for a Stochastic Dynamic Programming Model Used in Water System Optimization. Journal of Water Resources Planning and Management, 144(2), 04017090. doi:10.1061/(asce)wr.1943-5452.0000883Houck, M. H. (1979). A Chance Constrained Optimization Model for reservoir design and operation. Water Resources Research, 15(5), 1011-1016. doi:10.1029/wr015i005p01011Ji, C., Zhou, T., & Huang, H. (2014). Operating Rules Derivation of Jinsha Reservoirs System with Parameter Calibrated Support Vector Regression. Water Resources Management, 28(9), 2435-2451. doi:10.1007/s11269-014-0610-6Karamouz, M., & Houck, M. H. (1982). Annual and monthly reservoir operating rules generated by deterministic optimization. Water Resources Research, 18(5), 1337-1344. doi:10.1029/wr018i005p01337Karamouz, M., & Houck, M. H. (1987). COMPARISON OF STOCHASTIC AND DETERMINISTIC DYNAMIC PROGRAMMING FOR RESERVOIR OPERATING RULE GENERATION. Journal of the American Water Resources Association, 23(1), 1-9. doi:10.1111/j.1752-1688.1987.tb00778.xKaramouz, M., & Vasiliadis, H. V. (1992). Bayesian stochastic optimization of reservoir operation using uncertain forecasts. Water Resources Research, 28(5), 1221-1232. doi:10.1029/92wr00103Kasprzyk, J. R., Nataraj, S., Reed, P. M., & Lempert, R. J. (2013). Many objective robust decision making for complex environmental systems undergoing change. Environmental Modelling & Software, 42, 55-71. doi:10.1016/j.envsoft.2012.12.007Kelman, J., Stedinger, J. R., Cooper, L. A., Hsu, E., & Yuan, S.-Q. (1990). Sampling stochastic dynamic programming applied to reservoir operation. Water Resources Research, 26(3), 447-454. doi:10.1029/wr026i003p00447Keshtkar, A. R., Salajegheh, A., Sadoddin, A., & Allan, M. G. (2013). Application of Bayesian networks for sustainability assessment in catchment modeling and management (Case study: The Hablehrood river catchment). Ecological Modelling, 268, 48-54. doi:10.1016/j.ecolmodel.2013.08.003Kim, T., Heo, J.-H., Bae, D.-H., & Kim, J.-H. (2008). Single-reservoir operating rules for a year using multiobjective genetic algorithm. Journal of Hydroinformatics, 10(2), 163-179. doi:10.2166/hydro.2008.019Koutsoyiannis, D., & Economou, A. (2003). Evaluation of the parameterization-simulation-optimization approach for the control of reservoir systems. Water Resources Research, 39(6). doi:10.1029/2003wr002148Kumar, D. N., & Reddy, M. J. (2006). Ant Colony Optimization for Multi-Purpose Reservoir Operation. Water Resources Management, 20(6), 879-898. doi:10.1007/s11269-005-9012-0Nagesh Kumar, D., & Janga Reddy, M. (2007). Multipurpose Reservoir Operation Using Particle Swarm Optimization. Journal of Water Resources Planning and Management, 133(3), 192-201. doi:10.1061/(asce)0733-9496(2007)133:3(192)Kumar, K., & Kasthurirengan, S. (2018). Generalized Linear Two-Point Hedging Rule for Water Supply Reservoir Operation. Journal of Water Resources Planning and Management, 144(9), 04018051. doi:10.1061/(asce)wr.1943-5452.0000964Kwakkel, J. H., Haasnoot, M., & Walker, W. E. (2016). Comparing Robust Decision-Making and Dynamic Adaptive Policy Pathways for model-based decision support under deep uncertainty. Environmental Modelling & Software, 86, 168-183. doi:10.1016/j.envsoft.2016.09.017Labadie, J. W. (2004). Optimal Operation of Multireservoir Systems: State-of-the-Art Review. Journal of Water Resources Planning and Management, 130(2), 93-111. doi:10.1061/(asce)0733-9496(2004)130:2(93)Labadie J. W. Baldo M. &Larson R.(2000).MODSIM: Decision support system for river basin management. Documentation and user manual.Lee, J.-H., & Labadie, J. W. (2007). Stochastic optimization of multireservoir systems via reinforcement learning. Water Resources Research, 43(11). doi:10.1029/2006wr005627Lei, X., Tan, Q., Wang, X., Wang, H., Wen, X., Wang, C., & Zhang, J. (2018). Stochastic optimal operation of reservoirs based on copula functions. Journal of Hydrology, 557, 265-275. doi:10.1016/j.jhydrol.2017.12.038Lerma, N., Paredes-Arquiola, J., Andreu, J., & Solera, A. (2013). Development of operating rules for a complex multi-reservoir system by coupling genetic algorithms and network optimization. Hydrological Sciences Journal, 58(4), 797-812. doi:10.1080/02626667.2013.779777Lerma, N., Paredes-Arquiola, J., Andreu, J., Solera, A., & Sechi, G. M. (2015). Assessment of evolutionary algorithms for optimal operating rules design in real Water Resource Systems. Environmental Modelling & Software, 69, 425-436. doi:10.1016/j.envsoft.2014.09.024Li, Y., Giuliani, M., & Castelletti, A. (2017). A coupled human–natural system to assess the operational value of weather and climate services for agriculture. Hydrology and Earth System Sciences, 21(9), 4693-4709. doi:10.5194/hess-21-4693-2017Lin, N. M., & Rutten, M. (2016). Optimal Operation of a Network of Multi-purpose Reservoir: A Review. Procedia Engineering, 154, 1376-1384. doi:10.1016/j.proeng.2016.07.504Liu, P., Cai, X., & Guo, S. (2011). Deriving multiple near-optimal solutions to deterministic reservoir operation problems. Water Resources Research, 47(8). doi:10.1029/2011wr010998Loucks, D. P. (1970). Some Comments on Linear Decision Rules and Chance Constraints. Water Resources Research, 6(2), 668-671. doi:10.1029/wr006i002p00668Loucks

    Dissecting Domain-Specific Evolutionary Pressure Profiles of Transient Receptor Potential Vanilloid Subfamily Members 1 to 4

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    The transient receptor potential vanilloid family includes four ion channels-TRPV1, TRPV2, TRPV3 and TRPV4-that are represented within the vertebrate subphylum and involved in several sensory and physiological processes. These channels are related to adaptation to the environment, and probably under strong evolutionary pressure. Using multiple sequence alignments as source for evolutionary, bioinformatics and statistical analysis, we have analyzed the evolutionary profiles for TRPV1, TRPV2, TRPV3 and TRPV4. The evolutionary pressure exerted over vertebrate TRPV2 sequences compared to the other channels argues for a positive selection profile for TRPV2 compared to TRPV1, TRPV3 and TRPV4. We have analyzed the selective pressure on specific protein domains, observing a common selective pressure trend for the common TRPV scaffold, consisting of the ankyrin repeat domain, the membrane proximal domain, the transmembrane domain, and the TRP domain. Through a more detailed analysis we have identified evolutionary constraints involved in the subunit contact at the transmembrane domain level. Performing evolutionary comparison, we have translated specific channel structural information such as the transmembrane topology, and the interaction between the membrane proximal domain and the TRP box. We have also identified potential common regulatory domains among all TRPV1-4 members, such as protein-protein, lipid-protein and vesicle trafficking domains

    Integrating Historical Operating Decisions and Expert Criteria into a DSS for the Management of a multireservoir System

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    [EN] This paper presents a collaborative framework to couple historical records with expert knowledge and criteria in order to define a Decision Support System (DSS) to support the seasonal operation of the reservoirs of the Jucar river system. The framework relies on the co-development of a DSS tool that is able to explicitly reproduce the decision-making processes and criteria considered by the system operators. Fuzzy logic is used to derive the implicit operating rules followed by the managers based on historical decisions and expert knowledge obtained in the co-development process, combining both sources of information. Fuzzy regression is used to forecast future inflows based on the meteorological and hydrological variables considered by the system operators in their decisions on reservoir operation. The DSS was validated against historical records. The developed framework and tools offer the system operators a way to predefine a set of feasible ex ante management decisions, as well as to explore the consequences associated with any single choice. In contrast with other approaches, the fuzzy-based method used is able to embed inflow uncertainty and its effects in the definition of the decisions on the system operation. Furthermore, the method is flexible enough to be applied to other water resource systems.The authors wish to acknowledge the Jucar River Basin Management Authority (Confederacion Hidrografica del Joecar, CHJ), especially its Operation Office's (Oficina de Explotacion) system operators Jose Maria Benlliure Moreno and Juan Fullana Montoro, for their contribution to the whole process, valuable suggestions, and provision of the necessary data to carry out the study. The study has been partially supported by the IMPADAPT project (CGL2013-48424-C2-1-R) with Spanish MINECO (Ministerio de Economia y Competitividad) and FEDER funds. It has also received funding from the European Union's Horizon 2020 research and innovation programme under the IMPREX project (GA 641.811).Macian-Sorribes, H.; Pulido-Velazquez, M. (2017). Integrating Historical Operating Decisions and Expert Criteria into a DSS for the Management of a multireservoir System. Journal of Water Resources Planning and Management. 143(1). https://doi.org/10.1061/(ASCE)WR.1943-5452.0000712S143

    NFAT-mediated defects in erythropoiesis cause anemia in Il2-/- mice.

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    The role of NFAT family transcription factors in erythropoiesis is so far unknown, although their involvement has been suggested previously. We have shown recently that Il2-/- mice develop severe anemia due to defects in KLF1 activity during BM erythropoiesis. Although, KLF1 activity is indispensable for erythropoiesis, the molecular details of Klf1 expression have not yet been elucidated. Here we show that an enhanced NFATc1 activity induced by increased integrin-cAMP signaling plays a critical role in the dysregulation of Klf1 expression and thereby cause anemia in Il2-/- mice. Interestingly, enhanced NFATc1 activity augmented apoptosis of immature erythrocytes in Il2-/- mice. On the other hand, ablation of NFATc1 activity enhanced differentiation of Ter119+ cells in BM. Restoring IL-2 signaling in Il2-/- mice reversed the increase in cAMP-NFAT signaling and facilitated normal erythropoiesis. Altogether, our study identified an NFAT-mediated negative signaling axis, manipulation of which could facilitate erythropoiesis and prevent anemia development

    Effects of receptor tyrosine kinase inhibitors on VEGF165a- and VEGF165b-stimulated gene transcription in HEK-293 cells expressing human VEGFR2

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    BACKGROUND AND PURPOSE: Receptor tyrosine kinase inhibitors (RTKIs) targeted at VEGF receptor 2 (VEGFR2) have proved to be attractive approaches to cancer therapy based on their ability to reduce angiogenesis. Here we have undertaken a quantitative analysis of the interaction of RTKIs and two VEGF splice variants, VEGF165a and VEGF165b, with VEGFR2 by studying nuclear factor of activated T-cells (NFAT) reporter gene activity in live HEK-293 cells. EXPERIMENTAL APPROACH: HEK-293 cells expressing the human VEGFR2 and a firefly luciferase reporter gene regulated by an NFAT response element were used for quantitative analysis of the effect of RTKIs on VEGF165a- and VEGF165b-stimulated luciferase gene expression. KEY RESULTS: VEGF165a produced a concentration-dependent activation of the NFAT-luciferase reporter gene in living cells that was inhibited in a non-competitive fashion by four different RTKIs (cediranib, pazopanib, sorafenib and vandetanib). The potency obtained for each RTKI from this analysis was similar to those obtained in binding studies using purified VEGFR2 kinase domains. VEGF165b was a lower-efficacy agonist of the NFAT-luciferase response when compared with VEGF165a. Analysis of the concentration–response data using the operational model of agonism indicated that both VEGF165 isoforms had similar affinity for VEGFR2. CONCLUSIONS AND IMPLICATIONS: Quantitative pharmacological analysis of the interaction of VEGF165 isoforms and RTKIs with VEGFR2 in intact living cells has provided important insights into the relative affinity and efficacy of VEGF165a and VEGF165b for activation of the calcineurinNFAT signalling pathway by this tyrosine kinase receptor
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