Role of Postsynaptic Density Protein 95 (PSD95) and Neuronal Nitric Oxide Synthase (NNOS) Interaction in the Regulation of Conditioned Fear

Indiana University-Purdue University Indianapolis (IUPUI)Stimulation of N-­methyl-­D-­aspartic acid receptors (NMDARs) and the resulting activation of neuronal nitric oxide synthase (nNOS) are critical for fear memory formation. A variety of previously studied NMDAR antagonists and NOS inhibitors can disrupt fear memory, but they also affect many other CNS functions. Following NMDAR stimulation, efficient activation of nNOS requires linking nNOS to a scaffolding protein, the postsynaptic density protein 95 (PSD95). We hypothesized that PSD95-­nNOS interaction in critical limbic regions (such as amygdala and hippocampus) during fear conditioning is important in regulating fear memory formation, and disruption of this protein-­protein binding may cause impairments in conditioned fear memory. Utilizing co-­immunoprecipitation, electrophysiology and behavioral paradigms, we first showed that fear conditioning results in significant increases in PSD95-­nNOS binding within the basolateral amygdala (BLA) and the ventral hippocampus (vHP) in a time-­dependent manner, but not in the medial prefrontal cortex (mPFC). Secondly, by using ZL006, a small molecule disruptor of PSD95-­ nNOS interaction, it was found that systemic and intra-­BLA disruption of PSD95-­ nNOS interaction by ZL006 impaired the consolidation of cue-­induced fear. In contrast, disruption of PSD95-­nNOS interaction within the vHP did not affect the consolidation of cue-­induced fear, but significantly impaired the consolidation of context-­induced fear. At the cellular level, disruption of PSD95-­nNOS interaction with ZL006 was found to impair long-­term potentiation (LTP) in the BLA neurons. Finally, unlike NMDAR antagonist MK-­801, ZL006 is devoid of adverse effects on many other CNS functions, such as motor function, social activity, cognitive functions in tasks of object recognition memory and spatial memory. These findings collectively demonstrated that PSD95-­nNOS interaction within the conditioned fear network appears to be a key molecular step in regulating synaptic plasticity and the consolidation of conditioned fear. Disruption of PSD95-­nNOS interaction holds promise as a novel treatment strategy for fear-­ motivated disorders, such as post-­traumatic stress disorder and phobias

Upscaling and Inverse Modeling of Groundwater Flow and Mass Transport in Heterogeneous Aquifers

Dividimos el trabajo en tres bloques: En el primer bloque, se han revisado las técnicas de escalado que utilizan una media simple, el método laplaciano simple, el laplaciano con piel y el escalado con mallado no uniforme y se han evaluado en un ejercicio tridimensional de escalado de la conductividad hidráulica. El campo usado como referencia es una realización condicional a escala fina de la conductividad hidráulica del experimento de macrodispersión realizado en la base de la fuerza aérea estadounidense de Columbus en Misuri (MADE en su acrónimo inglés). El objetivo de esta sección es doble, primero, comparar la efectividad de diferentes técnicas de escalado para producir modelos capaces de reproducir el comportamiento observado del movimiento del penacho de tritio, y segundo, demostrar y analizar las condiciones bajo las cuales el escalado puede proporcionar un modelo a una escala gruesa en el que el flujo y el transporte puedan predecirse con al ecuación de advección-dispersión en condiciones aparentemente no fickianas. En otros casos, se observa que la discrepancia en la predicción del transporte entre las dos escalas persiste, y la ecuación de advección-dispersión no es suficiente para explicar el transporte en la escala gruesa. Por esta razón, se ha desarrollado una metodología para el escalado del transporte en formaciones muy heterogéneas en tres dimensiones. El método propuesto se basa en un escalado de la conductividad hidráulica por el método laplaciano con piel y centrado en los interbloques, seguido de un escalado de los parámetros de transporte que requiere la inclusión de un proceso de transporte con transferencia de masa multitasa para compensar la pérdida de heterogeneidad inherente al cambio de escala. El método propuesto no sólo reproduce el flujo y el transporte en la escala gruesa, sino que reproduce también la incertidumbre asociada con las predicciones según puede observarse analizando la variabilidad del conjunto de curvas de llegada.Li ., L. (2011). Upscaling and Inverse Modeling of Groundwater Flow and Mass Transport in Heterogeneous Aquifers [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/12268Palanci

Three-Dimensional Steady-state Groundwater Flow Modeling with Full Tensor Conductivities Using Finite Difference

A new three-dimensional steady-state groundwater-flow forward-simulator with full conductivity tensors using a nineteen-points block-centered finite-difference method is presented. Hydraulic conductivity tensors are defined at the block interfaces eliminating the need to average conductivity tensors at adjacent blocks to approximate their values at the interfaces. The capabilities of the code are demonstrated in three heterogeneous formulations, two of the examples are two-dimensional, and the third one is three-dimensional and uses a nonuniform discretization grid. A benchmark, in the context of conductivity upscaling, is carried out with the MODFLOW LVDA module, which uses hydraulic conductivity tensors at block centers and then approximates their values at the interfaces. The results show that the code developed outperforms the MODFLOW LVDA module when the block conductivity principal directions are not parallel to the Cartesian axis.Li ., L. (2009). Three-Dimensional Steady-state Groundwater Flow Modeling with Full Tensor Conductivities Using Finite Difference. http://hdl.handle.net/10251/14536Archivo delegad

Exploring the Benefits of Small Catchments on Rural Spatial Governance in Wuling Mountain Area, China

China is facing an important period of rural governance innovation and restructuring of territorial spatial patterns. This paper selects catchments as the most closely related spatial units for rural industrial development and rural settlement activities, profoundly revealing the characteristics of transformational development and spatial governance in mountainous areas. To date, extensive literature in this area has produced a broad multidisciplinary consensus on catchment water and soil conservation and rural industry development; however, the interactive mechanism of ecological, social, and economic networks, and the characteristics behind small catchments which benefit from spatial governance, have never been analyzed and are relatively new to the sphere of rural governance. Our research argues the relative importance of multi-scale catchment units compared with traditional administrative village units in enhancing the organizational benefits of rural revitalization in terms of workforce, resources, and capital, using the case study of a catchment in the Wuling mountainous area. Our study presents a framework to explore the multi-dimensional governance experience of a small catchment in the Wuling mountainous area and proposes to integrate the resource endowment advantages of small catchments into rural industries development and transform the economic and social benefits contained in the ecological environment into multi-scale spatial benefits among farmers, villages, and the regional rural area. However, not all cases provide positive evidence. The overall development of a catchment is confronted with complex constraints, which are mainly related to the development stage and local historical and geographical factors. Furthermore, affected by the top-down “project-system” in the “poverty era”, the logic of “betting on the strong” and the single-centered logic of resource allocation at the grassroots level exacerbated the fragmentation of the mountainous area. Generally speaking, the catchment perspective promotes regional linkage development and multi-center governance modes and triggers multidisciplinary theoretical thinking to some extent. The catchment’s overall development helps play to the comparative advantage of mountainous areas and promotes endogenous sustainable development to a certain degree. However, the promotion of catchment governance in poverty-stricken mountainous areas is faced with a lack of financial foundation and needs support in order to break through the national system and local social constraints

A Comparative Study of Three-Dimensional Hydraulic Conductivity Upscaling at the MAcro-Dispersion Experiment (MADE) site, Columbus Air Force Base, Mississippi (USA)

Simple averaging, simple-Laplacian, Laplacian-with-skin, and non-uniform coarsening are the techniques investigated in this comparative study of three-dimensional hydraulic conductivity upscaling. The reference is a fine scale conditional realization of the hydraulic conductivities at the MAcro-Dispersion Experiment site on Columbus Air Force Base in Mississippi (USA). This realization was generated using a hole-effect variogram model and it was shown that flow and transport modeling in this realization (at this scale) can reproduce the observed non-Fickian spreading of the tritium plume. The purpose of this work is twofold, first to compare the effectiveness of different upscaling techniques in yielding upscaled models able to reproduce the observed transport behavior, and second to demonstrate and analyze the conditions under which flow upscaling can provide a coarse model in which the standard advection-dispersion equation can be used to model transport in seemingly non-Fickian scenarios. Specifically, the use of Laplacian-based upscaling technique coupled with a non-uniform coarsening scheme yields the best results both in terms of flow and transport reproduction, for this case study in which the coarse blocks are smaller than the correlation ranges of the fine scale conductivities. © 2011 Elsevier B.V.The authors gratefully acknowledge the financial support by ENRESA (Project 0079000029). The second author also acknowledges the financial support from China Scholarship Council. The two anonymous reviewers are gratefully acknowledged for their comments which helped improving the final version of the manuscript.Li ., L.; Zhou ., H.; Gómez-Hernández, JJ. (2011). A Comparative Study of Three-Dimensional Hydraulic Conductivity Upscaling at the MAcro-Dispersion Experiment (MADE) site, Columbus Air Force Base, Mississippi (USA). Journal of Hydrology. 404(3-4):278-293. https://doi.org/10.1016/j.jhydrol.2011.05.001S2782934043-

Two-point or multiple-point statistics? A comparison between the ensemble Kalman filtering and the ensemble pattern matching inverse methods

The Ensemble Kalman Filter (EnKF) has been commonly used to assimilate real time dynamic data into geologic models over the past decade. Despite its various advantages such as computational efficiency and its capability to handle multiple sources of uncertainty, the EnKF may not be used to reliably update models that are characterized by curvilinear geometries such as fluvial deposits where the permeable channels play a crucial role in the prediction of solute transport. It is well-known that the EnKF performs optimally for updating multi-Gaussian distributed fields, basically because it uses two-point statistics (i.e., covariances) to represent the relationship between the model parameters and between the model parameters and the observed response, and this is the only statistic necessary to fully characterize a multiGaussian distribution. The Ensemble PATtern matching (EnPAT) is an alternative ensemble based method that shows significant potential to condition complex geology such as channelized aquifers to dynamic data. The EnPAT is an evolution of the EnKF, replacing, in the analysis step, two-point statistics with multiple-point statistics. The advantages of EnPAT reside in its capability to honor the complex spatial connectivity of geologic structures as well as the measured static and dynamic data. In this work, the performance of the classical EnKF and the EnPAT are compared for modeling a synthetic channelized aquifer. The results reveal that the EnPAT yields a better prediction of transport characteristics than the EnKF because it characterizes the conductivity heterogeneity better. Issues such as uncertainty of multiple variables and the effect of measurement errors on EnPAT results will be discussed. © 2015 Elsevier Ltd. All rights reserved.The first three authors gratefully acknowledge the financial support by the U.S. Department of Energy through project DE-FE0004962. The fourth author acknowledges the financial support by the Spanish Ministry of Economy and Competitiveness through project CGL2011-23295. We thank the guest editor Prof. Dr. Harrie-Jan Hendricks Franssen, as well as the reviewer Prof. Alberto Guadagnini and two anonymous reviewers for their comments, which substantially improved the manuscript.Li, L.; Srinivasan, S.; Zhou, H.; Gomez-Hernandez, JJ. (2015). Two-point or multiple-point statistics? A comparison between the ensemble Kalman filtering and the ensemble pattern matching inverse methods. Advances in Water Resources. 86:297-310. https://doi.org/10.1016/j.advwatres.2015.05.014S2973108

Characterizing curvilinear features using the localized normal-score ensemble Kalman filter

The localized normal-score ensemble Kalman filter is shown to work for the characterization of non-multi-Gaussian distributed hydraulic conductivities by assimilating state observation data. The influence of type of flow regime, number of observation piezometers, and the prior model structure are evaluated in a synthetic aquifer. Steady-state observation data are not sufficient to identify the conductivity channels. Transient-state data are necessary for a good characterization of the hydraulic conductivity curvilinear patterns. Such characterization is very good with a dense network of observation data, and it deteriorates as the number of observation piezometers decreases. It is also remarkable that, even when the prior model structure is wrong, the localized normal-score ensemble Kalman filter can produce acceptable results for a sufficiently dense observation network. Copyright © 2012 Haiyan Zhou et al.The authors gratefully acknowledge the financial support by the Spanish Ministry of Science and Innovation through project CGL2011-23295. The authors want to thank the reviewer for the comments which help improving the quality of the paper.Zhou ., H.; Li ., L.; Gómez-Hernández, JJ. (2012). Characterizing curvilinear features using the localized normal-score ensemble Kalman filter. Abstract and Applied Analysis. 2012:1-18. https://doi.org/10.1155/2012/805707S1182012Houtekamer, P. L., & Mitchell, H. L. (2001). A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation. Monthly Weather Review, 129(1), 123-137. doi:10.1175/1520-0493(2001)1292.0.co;2Naevdal, G., Johnsen, L. M., Aanonsen, S. I., & Vefring, E. H. (2005). Reservoir Monitoring and Continuous Model Updating Using Ensemble Kalman Filter. SPE Journal, 10(01), 66-74. doi:10.2118/84372-paChen, Y., & Zhang, D. (2006). Data assimilation for transient flow in geologic formations via ensemble Kalman filter. Advances in Water Resources, 29(8), 1107-1122. doi:10.1016/j.advwatres.2005.09.007Li, L., Zhou, H., Hendricks Franssen, H.-J., & Gómez-Hernández, J. J. (2012). Modeling transient groundwater flow by coupling ensemble Kalman filtering and upscaling. Water Resources Research, 48(1). doi:10.1029/2010wr010214Franssen, H. J. H., & Kinzelbach, W. (2009). Ensemble Kalman filtering versus sequential self-calibration for inverse modelling of dynamic groundwater flow systems. Journal of Hydrology, 365(3-4), 261-274. doi:10.1016/j.jhydrol.2008.11.033Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174-188. doi:10.1109/78.978374Evensen, G., & van Leeuwen, P. J. (2000). An Ensemble Kalman Smoother for Nonlinear Dynamics. Monthly Weather Review, 128(6), 1852-1867. doi:10.1175/1520-0493(2000)1282.0.co;2Zhou, H., Gómez-Hernández, J. J., Hendricks Franssen, H.-J., & Li, L. (2011). An approach to handling non-Gaussianity of parameters and state variables in ensemble Kalman filtering. Advances in Water Resources, 34(7), 844-864. doi:10.1016/j.advwatres.2011.04.014Li, L., Zhou, H., Hendricks Franssen, H. J., & Gómez-Hernández, J. J. (2011). Groundwater flow inverse modeling in non-MultiGaussian media: performance assessment of the normal-score Ensemble Kalman Filter. Hydrology and Earth System Sciences Discussions, 8(4), 6749-6788. doi:10.5194/hessd-8-6749-2011Zhou, H., Li, L., Hendricks Franssen, H.-J., & Gómez-Hernández, J. J. (2011). Pattern Recognition in a Bimodal Aquifer Using the Normal-Score Ensemble Kalman Filter. Mathematical Geosciences, 44(2), 169-185. doi:10.1007/s11004-011-9372-3Burgers, G., Jan van Leeuwen, P., & Evensen, G. (1998). Analysis Scheme in the Ensemble Kalman Filter. Monthly Weather Review, 126(6), 1719-1724. doi:10.1175/1520-0493(1998)1262.0.co;2Evensen, G. (2009). Data Assimilation. doi:10.1007/978-3-642-03711-5Chen, Y., & Oliver, D. S. (2009). Cross-covariances and localization for EnKF in multiphase flow data assimilation. Computational Geosciences, 14(4), 579-601. doi:10.1007/s10596-009-9174-6Gaspari, G., & Cohn, S. E. (1999). Construction of correlation functions in two and three dimensions. Quarterly Journal of the Royal Meteorological Society, 125(554), 723-757. doi:10.1002/qj.49712555417Hamill, T. M., Whitaker, J. S., & Snyder, C. (2001). Distance-Dependent Filtering of Background Error Covariance Estimates in an Ensemble Kalman Filter. Monthly Weather Review, 129(11), 2776-2790. doi:10.1175/1520-0493(2001)1292.0.co;2Carrera, J., & Neuman, S. P. (1986). Estimation of Aquifer Parameters Under Transient and Steady State Conditions: 2. Uniqueness, Stability, and Solution Algorithms. Water Resources Research, 22(2), 211-227. doi:10.1029/wr022i002p00211Delhomme, J. P. (1979). Spatial variability and uncertainty in groundwater flow parameters: A geostatistical approach. Water Resources Research, 15(2), 269-280. doi:10.1029/wr015i002p0026

c-Jun NH2-terminal kinase activation is essential for up-regulation of LC3 during ceramide-induced autophagy in human nasopharyngeal carcinoma cells

<p>Abstract</p> <p>Background</p> <p>Autophagy is a dynamic catabolic process characterized by the formation of double membrane vacuoles termed autophagosomes. LC3, a homologue of yeast Atg8, takes part in autophagosome formation, but the exact regulation mechanism of LC3 still needs to be elucidated.</p> <p>Methods</p> <p>Ceramide-induced autophagy was determined by detecting LC3 expression with Western blotting and confocal microscopy in human nasopharyngeal carcinoma cell lines CNE2 and SUNE1. The activation of JNK pathway was assessed by Western blotting for phospho-specific forms of JNK and c-Jun. The JNK activity specific inhibitor, SP600125, and siRNA directed against JNK were used to block JNK/c-Jun pathway. ChIP and luciferase reporter analysis were applied to determine whether c-Jun was involved in the regulation of LC3 transcription.</p> <p>Results</p> <p>Ceramide-treated cells exhibited the characteristics of autophagy and JNK pathway activation. Inhibition of JNK pathway could block the ceramide-induced autophagy and the up-regulation of LC3 expression. Transcription factor c-Jun was involved in LC3 transcription regulation in response to ceramide treatment.</p> <p>Conclusions</p> <p>Ceramide could induce autophagy in human nasopharyngeal carcinoma cells, and activation of JNK pathway was involved in ceramide-induced autophagy and LC3 expression.</p

Simultaneous estimation of both geologic and reservoir state variables within an ensemble-based multiple-point statistic framework

“The final publication is available at Springer via http://dx.doi.org/10.1007/s11004-013-9504-z"The first three authors gratefully acknowledge the financial support by US Department of Energy through project DE-FE0004962. The fourth author acknowledges the financial support by Spanish Ministry of Science and Innovation through project CGL2011-23295. The authors also wish to thank the guest editors, Philippe Renard and Gregoire Mariethoz, as well as three anonymous reviewers for their comments, which substantially helped improving the final version of the manuscript.Li, L.; Srinivasan, S.; Zhou, H.; Gómez-Hernández, JJ. (2014). Simultaneous estimation of both geologic and reservoir state variables within an ensemble-based multiple-point statistic framework. Mathematical Geosciences. 46(5):597-623. https://doi.org/10.1007/s11004-013-9504-zS597623465Aanonsen S, Nævdal G, Oliver D, Reynolds A, Valles B (2009) The ensemble Kalman filter in reservoir engineering—a review. SPE J 14(3):393–412Abdollahifard MJ, Faez K (2013) Stochastic simulation of patterns using Bayesian pattern modeling. Comput Geosci 17(1):99–116Alcolea A, Renard P (2010) Blocking moving window algorithm: conditioning multiple-point simulations to hydrogeological data. Water Resour Res 46:W08511Arpat GB (2005) Sequential simulation with patterns. PhD thesis, Stanford UniversityCaers J (2002) Geostatistical history matching under training-image based geological model constraints. In: SPE annual technical conference and exhibition, SPE 77429Caers J (2003) Efficient gradual deformation using a streamline-based proxy method. J Pet Sci Eng 39(1–2):57–83Chen Y, Zhang D (2006) Data assimilation for transient flow in geologic formations via ensemble Kalman filter. Adv Water Resour 29(8):1107–1122Evensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53(4):343–367Fu J, Gómez-Hernández JJ (2009) A blocking Markov chain Monte Carlo method for inverse stochastic hydrogeological modeling. Math Geosci 41(2):105–128Gómez-Hernández JJ, Sahuquillo A, Capilla JE (1997) Stochastic simulation of transmissivity fields conditional to both transmissivity and piezometric data. 1. Theory. J Hydrol 203(1–4):162–174Gu Y, Oliver D (2006) The ensemble Kalman filter for continuous updating of reservoir simulation models. J Energy Resour Technol 128(1):79–87Guardiano F, Srivastava R (1993) Multivariate geostatistics: beyond bivariate moments. In: Soares A (ed) Geostatistics-Troia. Kluwer Academic, Dordrecht, pp 133–144Hamill T, Whitaker J, Snyder C (2001) Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon Weather Rev 129:2776–2790Harbaugh AW, Banta ER, Hill MC, McDonald MG (2000) MODFLOW-2000, the US Geological Survey modular ground-water model. US Geological Survey, Branch of Information Services, Reston, VA, Denver, COHe J, Sarma P, Durlofsky LJ (2013) Reduced-order flow modeling and geological parameterization for ensemble-based data assimilation. Comput Geosci 55:54–69Hendricks Franssen H, Kinzelbach W (2008) Real-time groundwater flow modeling with the ensemble Kalman filter: joint estimation of states and parameters and the filter inbreeding problem. Water Resour Res 44(9):W09408Hoffman BT, Caers J (2005) Regional probability perturbations for history matching. J Pet Sci Eng 46(1–2):53–71Hu LY, Chugunova T (2008) Multiple-point geostatistics for modeling subsurface heterogeneity: a comprehensive review. Water Resour Res 44(11):W11413Hu LY, Zhao Y, Liu Y, Scheepens C, Bouchard A (2012) Updating multipoint simulatings using the ensemble Kalman filter. Comput Geosci 51:7–15Huang Y, Srinivasan S (2012) Efficient conditional simulation of spatial patterns using a pattern-growth algorithm. Geostatistics Oslo 2012:209–220Jafarpour B, Khodabakhshi M (2011) A probability conditioning method (PCM) for nonlinear flow data integration into multipoint statistical facies simulation. Math Geosci 43(2):133–164Kashib T, Srinivasan S (2006) A probabilistic approach to integrating dynamic data in reservoir models. J Pet Sci Eng 50(3):241–257Li L, Zhou H, Hendricks Franssen HJ, Gómez-Hernández JJ (2012) Modeling transient groundwater flow by coupling ensemble Kalman filtering and upscaling. Water Resour Res 48(1):W01537Li L, Srinivadan S, Zhou H, Gómez-Hernández J (2013) A pilot point guided pattern matching approach to integrate dynamic data into geological model. Adv Water Resour. doi: 10.1016/j.advwatres.2013.10.008Mariethoz G, Renard P, Caers J (2010a) Bayesian inverse problem and optimization with iterative spatial resampling. Water Resour Res 46(11):W11530Mariethoz G, Renard P, Straubhaar J (2010b) The direct sampling method to perform multiple-point geostatistical simulaitons. Water Resour Res 46:W11536Meerschman E, Pirot G, Mariethoz G, Straubhaar J, Meirvenne M, Renard P (2013) A practical guide to performing multiple-point statistical simulations with the direct sampling algorithm. Comput Geosci 52:307–324Ramarao BS, LaVenue de AM, Marsily G, Marietta MG (1995) Pilot point methodology for automated calibration of an ensemble of conditionally simulated transmissivity fields. 1. Theory and computational experiments. Water Resour Res 31(3):475–493Rezaee H, Mariethoz G, Koneshloo M, Asghari O (2013) Multiple-point geostatistical simulation using the bunch-pasting direct sampling method. Comput Geosci 54:293–308Straubhaar J, Renard P, Mariethoz G, Froidevaux R, Besson O (2011) An improved parallel multiple-point algorithm using a list approach. Math Geosci 43(3):305–328Strebelle S (2002) Conditional simulation of complex geological structures using multiple-point statistics. Math Geol 34(1):1–21Sun AY, Morris AP, Mohanty S (2009) Sequential updating of multimodal hydrogeologic parameter fields using localization and clustering techniques. Water Resour Res 45(7):W07424Tahmasebi P, Hezarkhani A, Sahimi M (2012a) Multiple-point geostatistical modeling based on the cross-correlation functions. Comput Geosci 16(3):779–797Tahmasebi P, Sahimi M, Mariethoz G, Hezarkhani A (2012b) Accelerating geostatistical simulations using graphics processing units (GPU). Comput Geosci 46:51–59Wen X, Chen W (2006) Real-time reservoir model updating using ensemble Kalman filter with confirming option. SPE J 11(4):431–442Zhang T, Switzer P, Journel A (2006) Filter-based classification of training image patterns for spatial simulation. Math Geol 38(1):63–80Zhou H, Gómez-Hernández J, Hendricks Franssen H, Li L (2011) An approach to handling non-Gaussianity of parameters and state variables in ensemble Kalman filtering. Adv Water Resour 34(7):844–864Zhou H, Gómez-Hernández J, Li L (2012) A pattern-search-based inverse method. Water Resour Res 48(3):W0350