127 research outputs found

    The Non-Canonical Wnt/PKC Pathway Regulates Mitochondrial Dynamics through Degradation of the Arm-Like Domain-Containing Protein Alex3

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    The regulation of mitochondrial dynamics is vital in complex cell types, such as neurons, that transport and localize mitochondria in high energy-demanding cell domains. The Armcx3 gene encodes a mitochondrial-targeted protein (Alex3) that contains several arm-like domains. In a previous study we showed that Alex3 protein regulates mitochondrial aggregation and trafficking. Here we studied the contribution of Wnt proteins to the mitochondrial aggregation and dynamics regulated by Alex3. Overexpression of Alex3 in HEK293 cells caused a marked aggregation of mitochondria, which was attenuated by treatment with several Wnts. We also found that this decrease was caused by Alex3 degradation induced by Wnts. While the Wnt canonical pathway did not alter the pattern of mitochondrial aggregation induced by Alex3, we observed that the Wnt/PKC non-canonical pathway regulated both mitochondrial aggregation and Alex3 protein levels, thereby rendering a mitochondrial phenotype and distribution similar to control patterns. Our data suggest that the Wnt pathway regulates mitochondrial distribution and dynamics through Alex3 protein degradation

    Thin healthy women have a similar low bone mass to women with anorexia nervosa.

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    An association between anorexia nerviosa (AN) and low bone mass has been demonstrated. Bone loss associated with AN involves hormonal and nutritional impairments, though their exact contribution is not clearly established. We compared bone mass in AN patients with women of similar weight with no criteria for AN, and a third group of healthy, normal-weight, age-matched women. The study included forty-eight patients with AN, twenty-two healthy eumenorrhoeic women with low weight (LW group; BMI 18.5 kg/m2 (control group), all of similar age. We measured lean body mass, percentage fat mass, total bone mineral content (BMC) and bone mineral density in lumbar spine (BMD LS) and in total (tBMD). We measured anthropometric parameters, leptin and growth hormone. The control group had greater tBMD and BMD LS than the other groups, with no differences between the AN and LW groups. No differences were found in tBMD, BMD LS and total BMC between the restrictive (n 25) and binge-purge type (n 23) in AN patients. In AN, minimum weight (P = 0.002) and percentage fat mass (P = 0.02) explained BMD LS variation (r2 0.48) and minimum weight (r2 0.42; P = 0.002) for tBMD in stepwise regression analyses. In the LW group, BMI explained BMD LS (r2 0.72; P = 0.01) and tBMD (r2 0.57; P = 0.04). We concluded that patients with AN had similar BMD to healthy thin women. Anthropometric parameters could contribute more significantly than oestrogen deficiency in the achievement of peak bone mass in AN patients

    Calcisponges have a ParaHox gene and dynamic expression of dispersed NK homeobox genes

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    This study was funded by the Sars Centre core budget to M. Adamska. Sequencing was performed at the Norwegian High Throughput Sequencing Centre funded by the Norwegian Research Council. O.M.R. and D.E.K.F. acknowledge support from the BBSRC and the School of Biology, University of St Andrews.Sponges are simple animals with few cell types, but their genomes paradoxically contain a wide variety of developmental transcription factors1,2,3,4, including homeobox genes belonging to the Antennapedia (ANTP) class5,6, which in bilaterians encompass Hox, ParaHox and NK genes. In the genome of the demosponge Amphimedon queenslandica, no Hox or ParaHox genes are present, but NK genes are linked in a tight cluster similar to the NK clusters of bilaterians5. It has been proposed that Hox and ParaHox genes originated from NK cluster genes after divergence of sponges from the lineage leading to cnidarians and bilaterians5,7. On the other hand, synteny analysis lends support to the notion that the absence of Hox and ParaHox genes in Amphimedon is a result of secondary loss (the ghost locus hypothesis)8. Here we analysed complete suites of ANTP-class homeoboxes in two calcareous sponges, Sycon ciliatum and Leucosolenia complicata. Our phylogenetic analyses demonstrate that these calcisponges possess orthologues of bilaterian NK genes (Hex, Hmx and Msx), a varying number of additional NK genes and one ParaHox gene, Cdx. Despite the generation of scaffolds spanning multiple genes, we find no evidence of clustering of Sycon NK genes. All Sycon ANTP-class genes are developmentally expressed, with patterns suggesting their involvement in cell type specification in embryos and adults, metamorphosis and body plan patterning. These results demonstrate that ParaHox genes predate the origin of sponges, thus confirming the ghost locus hypothesis8, and highlight the need to analyse the genomes of multiple sponge lineages to obtain a complete picture of the ancestral composition of the first animal genome.PostprintPeer reviewe

    N-Methyl-D-aspartic Acid (NMDA) in the nervous system of the amphioxus Branchiostoma lanceolatum

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    <p>Abstract</p> <p>Background</p> <p>NMDA (<it>N</it>-methyl-D-aspartic acid) is a widely known agonist for a class of glutamate receptors, the NMDA type. Synthetic NMDA elicits very strong activity for the induction of hypothalamic factors and hypophyseal hormones in mammals. Moreover, endogenous NMDA has been found in rat, where it has a role in the induction of GnRH (Gonadotropin Releasing Hormone) in the hypothalamus, and of LH (Luteinizing Hormone) and PRL (Prolactin) in the pituitary gland.</p> <p>Results</p> <p>In this study we show evidence for the occurrence of endogenous NMDA in the amphioxus <it>Branchiostoma lanceolatum</it>. A relatively high concentration of NMDA occurs in the nervous system of this species (3.08 ± 0.37 nmol/g tissue in the nerve cord and 10.52 ± 1.41 nmol/g tissue in the cephalic vesicle). As in rat, in amphioxus NMDA is also biosynthesized from D-aspartic acid (D-Asp) by a NMDA synthase (also called D-aspartate methyl transferase).</p> <p>Conclusion</p> <p>Given the simplicity of the amphioxus nervous and endocrine systems compared to mammalian, the discovery of NMDA in this protochordate is important to gain insights into the role of endogenous NMDA in the nervous and endocrine systems of metazoans and particularly in the chordate lineage.</p

    Pattern Recognition in a Bimodal Aquifer Using the Normal-Score Ensemble Kalman Filter

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    The ensemble Kalman filter (EnKF) is now widely used in diverse disciplines to estimate model parameters and update model states by integrating observed data. The EnKF is known to perform optimally only for multi-Gaussian distributed states and parameters. A new approach, the normal-score EnKF (NS-EnKF), has been recently proposed to handle complex aquifers with non-Gaussian distributed parameters. In this work, we aim at investigating the capacity of the NS-EnKF to identify patterns in the spatial distribution of the model parameters (hydraulic conductivities) by assimilating dynamic observations in the absence of direct measurements of the parameters themselves. In some situations, hydraulic conductivity measurements (hard data) may not be available, which requires the estimation of conductivities from indirect observations, such as piezometric heads. We show how the NS-EnKF is capable of retrieving the bimodal nature of a synthetic aquifer solely from piezometric head data. By comparison with a more standard implementation of the EnKF, the NS-EnKF gives better results with regard to histogram preservation, uncertainty assessment, and transport predictions. © 2011 International Association for Mathematical Geosciences.The authors gratefully acknowledge the financial support by the Spanish Ministry of Science and Innovation through project CGL2011-23295. The first author appreciates the financial aid from China Scholarship Council (CSC No. [2007]3020).Zhou, H.; Li, L.; Hendricks Franssen, H.; Gómez-Hernández, JJ. (2012). Pattern Recognition in a Bimodal Aquifer Using the Normal-Score Ensemble Kalman Filter. Mathematical Geosciences. 44(2):169-185. https://doi.org/10.1007/s11004-011-9372-3S169185442Arulampalam MS, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. 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    Stochastic upscaling of hydrodynamic dispersion and retardation factor in a physically and chemically heterogeneous tropical soil

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    [EN] Stochastic upscaling of flow and reactive solute transport in a tropical soil is performed using real data collected in the laboratory. Upscaling of hydraulic conductivity, longitudinal hydrodynamic dispersion, and retardation factor were done using three different approaches of varying complexity. How uncertainty propagates after upscaling was also studied. The results show that upscaling must be taken into account if a good reproduction of the flow and transport behavior of a given soil is to be attained when modeled at larger than laboratory scales. The results also show that arrival time uncertainty was well reproduced after solute transport upscaling. This work represents a first demonstration of flow and reactive transport upscaling in a soil based on laboratory data. It also shows how simple upscaling methods can be incorporated into daily modeling practice using commercial flow and transport codes.The authors thank the financial support by the Brazilian National Council for Scientific and Technological Development (CNPq) (Project 401441/2014-8). The doctoral fellowship award to the first author by the Coordination of Improvement of Higher Level Personnel (CAPES) is acknowledged. The first author also thanks the international mobility grant awarded by CNPq, through the Sciences Without Borders program (Grant Number: 200597/2015-9). The international mobility grant awarded by Santander Mobility in cooperation with the University of Sao Paulo is also acknowledged. DHI-WASI is gratefully thanked for providing a FEFLOW license.Almeida De-Godoy, V.; Zuquette, L.; Gómez-Hernández, JJ. (2019). Stochastic upscaling of hydrodynamic dispersion and retardation factor in a physically and chemically heterogeneous tropical soil. 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Appl Math Model 37:8533–8542. https://doi.org/10.1016/j.apm.2013.03.070Zech A, Attinger S, Cvetkovic V, Dagan G, Dietrich P, Fiori A, Rubin Y, Teutsch G (2015) Is unique scaling of aquifer macrodispersivity supported by field data? Water Resour Res 51:7662–7679. https://doi.org/10.1002/2015WR017220Zhou H, Li L, Gómez-Hernández JJ (2010) Three-dimensional hydraulic conductivity upscaling in groundwater modeling. Comput Geosci 36:1224–1235. https://doi.org/10.1016/j.cageo.2010.03.008Zhou H, Li L, Hendricks Franssen H-J, Gómez-Hernández JJ (2012) Pattern recognition in a bimodal aquifer using the normal-score ensemble Kalman filter. Math Geosci 44:169–185. https://doi.org/10.1007/s11004-011-9372-

    A pattern-search-based inverse method

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    Uncertainty in model predictions is caused to a large extent by the uncertainty in model parameters, while the identification of model parameters is demanding because of the inherent heterogeneity of the aquifer. A variety of inverse methods has been proposed for parameter identification. In this paper we present a novel inverse method to constrain the model parameters (hydraulic conductivities) to the observed state data (hydraulic heads). In the method proposed we build a conditioning pattern consisting of simulated model parameters and observed flow data. The unknown parameter values are simulated by pattern searching through an ensemble of realizations rather than optimizing an objective function. The model parameters do not necessarily follow a multi-Gaussian distribution, and the nonlinear relationship between the parameter and the response is captured by the multipoint pattern matching. The algorithm is evaluated in two synthetic bimodal aquifers. The proposed method is able to reproduce the main structure of the reference fields, and the performance of the updated model in predicting flow and transport is improved compared with that of the prior model.The authors gratefully acknowledge the financial support from the Ministry of Science and Innovation, project CGL2011-23295. The first author also acknowledges the scholarship provided by the China Scholarship Council (CSC [2007] 3020). The authors would like to thank Gregoire Mariethoz (University of New South Wales) and Philippe Renard (University of Neuchatel) for their enthusiastic help in answering questions about the direct sampling algorithm. Gregoire Mariethoz and two anonymous reviewers are also thanked for their comments during the reviewing process, which helped improving the final paper.Zhou ., H.; Gómez-Hernández, JJ.; Li ., L. (2012). A pattern-search-based inverse method. Water Resources Research. 48(3):1-17. https://doi.org/10.1029/2011WR011195S117483Alcolea, A., & Renard, P. (2010). Blocking Moving Window algorithm: Conditioning multiple-point simulations to hydrogeological data. Water Resources Research, 46(8). doi:10.1029/2009wr007943Alcolea, A., Carrera, J., & Medina, A. (2006). Pilot points method incorporating prior information for solving the groundwater flow inverse problem. Advances in Water Resources, 29(11), 1678-1689. doi:10.1016/j.advwatres.2005.12.009Arpat, G. B., & Caers, J. (2007). Conditional Simulation with Patterns. Mathematical Geology, 39(2), 177-203. doi:10.1007/s11004-006-9075-3Caers , J. 2002 Geostatistical history matching under training-image based geological model constraintsCaers, J. (2003). Efficient gradual deformation using a streamline-based proxy method. Journal of Petroleum Science and Engineering, 39(1-2), 57-83. doi:10.1016/s0920-4105(03)00040-8Caers, J., & Hoffman, T. (2006). The Probability Perturbation Method: A New Look at Bayesian Inverse Modeling. Mathematical Geology, 38(1), 81-100. doi:10.1007/s11004-005-9005-9Capilla, J. E., & Llopis-Albert, C. (2009). Gradual conditioning of non-Gaussian transmissivity fields to flow and mass transport data: 1. Theory. Journal of Hydrology, 371(1-4), 66-74. doi:10.1016/j.jhydrol.2009.03.015Carrera, J., & Neuman, S. P. (1986). Estimation of Aquifer Parameters Under Transient and Steady State Conditions: 1. Maximum Likelihood Method Incorporating Prior Information. Water Resources Research, 22(2), 199-210. doi:10.1029/wr022i002p00199Chen, 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.2005.09.007Christiansen, L., Binning, P. J., Rosbjerg, D., Andersen, O. B., & Bauer-Gottwein, P. (2011). Using time-lapse gravity for groundwater model calibration: An application to alluvial aquifer storage. Water Resources Research, 47(6). doi:10.1029/2010wr009859De Marsily, G., Delay, F., Gonçalvès, J., Renard, P., Teles, V., & Violette, S. (2005). Dealing with spatial heterogeneity. Hydrogeology Journal, 13(1), 161-183. doi:10.1007/s10040-004-0432-3Deutsch, C. V., & Tran, T. T. (2002). FLUVSIM: a program for object-based stochastic modeling of fluvial depositional systems. Computers & Geosciences, 28(4), 525-535. doi:10.1016/s0098-3004(01)00075-9Dubuisson, M.-P., & Jain, A. K. (s. f.). A modified Hausdorff distance for object matching. Proceedings of 12th International Conference on Pattern Recognition. doi:10.1109/icpr.1994.576361Emsellem, Y., & De Marsily, G. (1971). An Automatic Solution for the Inverse Problem. Water Resources Research, 7(5), 1264-1283. doi:10.1029/wr007i005p01264Evensen, G. (2003). The Ensemble Kalman Filter: theoretical formulation and practical implementation. 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A Blocking Markov Chain Monte Carlo Method for Inverse Stochastic Hydrogeological Modeling. Mathematical Geosciences, 41(2), 105-128. doi:10.1007/s11004-008-9206-0Jaime Gómez-Hernánez, J., Sahuquillo, A., & Capilla, J. (1997). Stochastic simulation of transmissivity fields conditional to both transmissivity and piezometric data—I. Theory. Journal of Hydrology, 203(1-4), 162-174. doi:10.1016/s0022-1694(97)00098-xGuardiano, F. B., & Srivastava, R. M. (1993). Multivariate Geostatistics: Beyond Bivariate Moments. Geostatistics Tróia ’92, 133-144. doi:10.1007/978-94-011-1739-5_12Harbaugh , A. W. E. R. Banta M. C. Hill M. G. McDonald 2000 MODFLOW-2000, the U.S. Geological Survey modular ground-water model-User guide to modularization concepts and the ground-water flow process Reston, Va.Hendricks Franssen, H. J., & 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 Resources Research, 44(9). doi:10.1029/2007wr006505Franssen, H.-J. H., Gómez-Hernández, J., & Sahuquillo, A. (2003). Coupled inverse modelling of groundwater flow and mass transport and the worth of concentration data. Journal of Hydrology, 281(4), 281-295. doi:10.1016/s0022-1694(03)00191-4Henrion, V., Caumon, G., & Cherpeau, N. (2010). ODSIM: An Object-Distance Simulation Method for Conditioning Complex Natural Structures. Mathematical Geosciences, 42(8), 911-924. doi:10.1007/s11004-010-9299-0Hoeksema, R. J., & Kitanidis, P. K. (1984). An Application of the Geostatistical Approach to the Inverse Problem in Two-Dimensional Groundwater Modeling. Water Resources Research, 20(7), 1003-1020. doi:10.1029/wr020i007p01003Hoeksema, R. J., & Kitanidis, P. K. (1985). Analysis of the Spatial Structure of Properties of Selected Aquifers. Water Resources Research, 21(4), 563-572. doi:10.1029/wr021i004p00563Hu, L. Y. (2000). Mathematical Geology, 32(1), 87-108. doi:10.1023/a:1007506918588Hu, L. Y., & Chugunova, T. (2008). Multiple-point geostatistics for modeling subsurface heterogeneity: A comprehensive review. Water Resources Research, 44(11). doi:10.1029/2008wr006993Huysmans, M., & Dassargues, A. (2009). Application of multiple-point geostatistics on modelling groundwater flow and transport in a cross-bedded aquifer (Belgium). Hydrogeology Journal, 17(8), 1901-1911. doi:10.1007/s10040-009-0495-2Jafarpour, B., & Khodabakhshi, M. (2011). A Probability Conditioning Method (PCM) for Nonlinear Flow Data Integration into Multipoint Statistical Facies Simulation. Mathematical Geosciences, 43(2), 133-164. doi:10.1007/s11004-011-9316-yJournel, A., & Zhang, T. (2006). The Necessity of a Multiple-Point Prior Model. Mathematical Geology, 38(5), 591-610. doi:10.1007/s11004-006-9031-2Kerrou, J., Renard, P., Hendricks Franssen, H.-J., & Lunati, I. (2008). Issues in characterizing heterogeneity and connectivity in non-multiGaussian media. Advances in Water Resources, 31(1), 147-159. doi:10.1016/j.advwatres.2007.07.002Kwa, Franz, M. O., & Scholkopf, B. (2005). Iterative kernel principal component analysis for image modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(9), 1351-1366. doi:10.1109/tpami.2005.181Kitanidis, P. K. (2007). On stochastic inverse modeling. Geophysical Monograph Series, 19-30. doi:10.1029/171gm04Kitanidis, P. K., & Vomvoris, E. G. (1983). A geostatistical approach to the inverse problem in groundwater modeling (steady state) and one-dimensional simulations. Water Resources Research, 19(3), 677-690. doi:10.1029/wr019i003p00677Li, 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-8-6749-2011Li, L., Zhou, H., & Gómez-Hernández, J. J. (2011). Transport upscaling using multi-rate mass transfer in three-dimensional highly heterogeneous porous media. Advances in Water Resources, 34(4), 478-489. doi:10.1016/j.advwatres.2011.01.001Li, L., Zhou, H., & Gómez-Hernández, J. J. (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. doi:10.1016/j.jhydrol.2011.05.001Mariethoz, G., Renard, P., & Straubhaar, J. (2010). The Direct Sampling method to perform multiple-point geostatistical simulations. Water Resources Research, 46(11). doi:10.1029/2008wr007621Mariethoz, G., Renard, P., & Caers, J. (2010). Bayesian inverse problem and optimization with iterative spatial resampling. Water Resources Research, 46(11). doi:10.1029/2010wr009274Neuman, S. P. (1973). Calibration of distributed parameter groundwater flow models viewed as a multiple-objective decision process under uncertainty. Water Resources Research, 9(4), 1006-1021. doi:10.1029/wr009i004p01006Oliver, D. S., Cunha, L. B., & Reynolds, A. C. (1997). Markov chain Monte Carlo methods for conditioning a permeability field to pressure data. Mathematical Geology, 29(1), 61-91. doi:10.1007/bf02769620RamaRao, B. S., LaVenue, A. M., De Marsily, G., & Marietta, M. G. (1995). Pilot Point Methodology for Automated Calibration of an Ensemble of conditionally Simulated Transmissivity Fields: 1. Theory and Computational Experiments. Water Resources Research, 31(3), 475-493. doi:10.1029/94wr02258Rubin, Y., Chen, X., Murakami, H., & Hahn, M. (2010). A Bayesian approach for inverse modeling, data assimilation, and conditional simulation of spatial random fields. 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    Evolution through segmental duplications and losses : A Super-Reconciliation approach

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    The classical gene and species tree reconciliation, used to infer the history of gene gain and loss explaining the evolution of gene families, assumes an independent evolution for each family. While this assumption is reasonable for genes that are far apart in the genome, it is not appropriate for genes grouped into syntenic blocks, which are more plausibly the result of a concerted evolution. Here, we introduce the Super-Reconciliation problem which consists in inferring a history of segmental duplication and loss events (involving a set of neighboring genes) leading to a set of present-day syntenies from a single ancestral one. In other words, we extend the traditional Duplication-Loss reconciliation problem of a single gene tree, to a set of trees, accounting for segmental duplications and losses. Existency of a Super-Reconciliation depends on individual gene tree consistency. In addition, ignoring rearrangements implies that existency also depends on gene order consistency. We first show that the problem of reconstructing a most parsimonious Super-Reconciliation, if any, is NP-hard and give an exact exponential-time algorithm to solve it. Alternatively, we show that accounting for rearrangements in the evolutionary model, but still only minimizing segmental duplication and loss events, leads to an exact polynomial-time algorithm. We finally assess time efficiency of the former exponential time algorithm for the Duplication-Loss model on simulated datasets, and give a proof of concept on the opioid receptor genes

    Algebraic Distribution of Segmental Duplication Lengths in Whole-Genome Sequence Self-Alignments

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    Distributions of duplicated sequences from genome self-alignment are characterized, including forward and backward alignments in bacteria and eukaryotes. A Markovian process without auto-correlation should generate an exponential distribution expected from local effects of point mutation and selection on localised function; however, the observed distributions show substantial deviation from exponential form – they are roughly algebraic instead – suggesting a novel kind of long-distance correlation that must be non-local in origin

    Inverse sequential simulation: Performance and implementation details

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    For good groundwater flow and solute transport numerical modeling, it is important to characterize the formation properties. In this paper, we analyze the performance and important implementation details of a new approach for stochastic inverse modeling called inverse sequential simulation (iSS). This approach is capable of characterizing conductivity fields with heterogeneity patterns difficult to capture by standard multiGaussian-based inverse approaches. The method is based on the multivariate sequential simulation principle, but the covariances and cross-covariances used to compute the local conditional probability distributions are computed by simple co-kriging which are derived from an ensemble of conductivity and piezometric head fields, in a similar manner as the experimental covariances are computed in an ensemble Kalman filtering. A sensitivity analysis is performed on a synthetic aquifer regarding the number of members of the ensemble of realizations, the number of conditioning data, the number of piezometers at which piezometric heads are observed, and the number of nodes retained within the search neighborhood at the moment of computing the local conditional probabilities. The results show the importance of having a sufficiently large number of all of the mentioned parameters for the algorithm to characterize properly hydraulic conductivity fields with clear non-multiGaussian features. © 2015 Elsevier Ltd. All rights reserved.The first author acknowledgs the financial support from the China Scholarship Council (CSC [2010]3010). Financial support to carry out this work was also received from the Spanish Ministry of Economy and Competitiveness through Project CGL2014-59841-P. We thank the three reviewers for their thorough review and their insightful comments, which have helped to improve the final manuscript.Xu, T.; Gómez-Hernández, JJ. (2015). Inverse sequential simulation: Performance and implementation details. Advances in Water Resources. 86B:311-326. https://doi.org/10.1016/j.advwatres.2015.04.015S31132686
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