A pattern-search-based inverse method

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. Ocean Dynamics, 53(4), 343-367. doi:10.1007/s10236-003-0036-9Falivene, O., Cabello, P., Arbués, P., Muñoz, J. A., & Cabrera, L. (2009). A geostatistical algorithm to reproduce lateral gradual facies transitions: Description and implementation. Computers & Geosciences, 35(8), 1642-1651. doi:10.1016/j.cageo.2008.12.003Fernàndez-Garcia, D., Illangasekare, T. H., & Rajaram, H. (2005). Differences in the scale dependence of dispersivity and retardation factors estimated from forced-gradient and uniform flow tracer tests in three-dimensional physically and chemically heterogeneous porous media. Water Resources Research, 41(3). doi:10.1029/2004wr003125Feyen, L., & Caers, J. (2006). Quantifying geological uncertainty for flow and transport modeling in multi-modal heterogeneous formations. Advances in Water Resources, 29(6), 912-929. doi:10.1016/j.advwatres.2005.08.002Fu, J., & Gómez-Hernández, J. J. (2008). 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. Water Resources Research, 46(10). doi:10.1029/2009wr008799Strebelle, S. (2002). Mathematical Geology, 34(1), 1-21. doi:10.1023/a:1014009426274Suzuki, S., & Caers, J. (2008). A Distance-based Prior Model Parameterization for Constraining Solutions of Spatial Inverse Problems. Mathematical Geosciences, 40(4), 445-469. doi:10.1007/s11004-008-9154-8Wen, X. H., Capilla, J. E., Deutsch, C. V., Gómez-Hernández, J. J., & Cullick, A. S. (1999). A program to create permeability fields that honor single-phase flow rate and pressure data. Computers & Geosciences, 25(3), 217-230. doi:10.1016/s0098-3004(98)00126-5Zhang, T., Switzer, P., & Journel, A. (2006). Filter-Based Classification of Training Image Patterns for Spatial Simulation. Mathematical Geology, 38(1), 63-80. doi:10.1007/s11004-005-9004-xZhou, 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.2011.04.014Zhou, 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-

An Interactive Bayesian Geostatistical Inverse Protocol for Hydraulic Tomography

Hydraulic tomography is a powerful technique for characterizing heterogeneous hydrogeologic parameters. An explicit trade-off between characterization based on measurement misfit and subjective characterization using prior information is presented. We apply a Bayesian geostatistical inverse approach that is well suited to accommodate a flexible model with the level of complexity driven by the data and explicitly considering uncertainty. Prior information is incorporated through the selection of a parameter covariance model characterizing continuity and providing stability. Often, discontinuities in the parameter field, typically caused by geologic contacts between contrasting lithologic units, necessitate subdivision into zones across which there is no correlation among hydraulic parameters. We propose an interactive protocol in which zonation candidates are implied from the data and are evaluated using cross validation and expert knowledge. Uncertainty introduced by limited knowledge of dynamic regional conditions is mitigated by using drawdown rather than native head values. An adjoint state formulation of MODFLOW-2000 is used to calculate sensitivities which are used both for the solution to the inverse problem and to guide protocol decisions. The protocol is tested using synthetic two-dimensional steady state examples in which the wells are located at the edge of the region of interest

Galectin-3C Inhibits Tumor Growth and Increases the Anticancer Activity of Bortezomib in a Murine Model of Human Multiple Myeloma

Galectin-3 is a human lectin involved in many cellular processes including differentiation, apoptosis, angiogenesis, neoplastic transformation, and metastasis. We evaluated galectin-3C, an N-terminally truncated form of galectin-3 that is thought to act as a dominant negative inhibitor, as a potential treatment for multiple myeloma (MM). Galectin-3 was expressed at varying levels by all 9 human MM cell lines tested. In vitro galectin-3C exhibited modest anti-proliferative effects on MM cells and inhibited chemotaxis and invasion of U266 MM cells induced by stromal cell-derived factor (SDF)-1α. Galectin-3C facilitated the anticancer activity of bortezomib, a proteasome inhibitor approved by the FDA for MM treatment. Galectin-3C and bortezomib also synergistically inhibited MM-induced angiogenesis activity in vitro. Delivery of galectin-3C intravenously via an osmotic pump in a subcutaneous U266 cell NOD/SCID mouse model of MM significantly inhibited tumor growth. The average tumor volume of bortezomib-treated animals was 19.6% and of galectin-3C treated animals was 13.5% of the average volume of the untreated controls at day 35. The maximal effect was obtained with the combination of galectin-3C with bortezomib that afforded a reduction of 94% in the mean tumor volume compared to the untreated controls at day 35. In conclusion, this is the first study to show that inhibition of galectin-3 is efficacious in a murine model of human MM. Our results demonstrated that galectin-3C alone was efficacious in a xenograft mouse model of human MM, and that it enhanced the anti-tumor activity of bortezomib in vitro and in vivo. These data provide the rationale for continued testing of galectin-3C towards initiation of clinical trials for treatment of MM

Inverse Methods in Hydrogeology: Evolution and Recent Trends

[EN] Parameter identification is an essential step in constructing a groundwater model. The process of recognizing model parameter values by conditioning on observed data of the state variable is referred to as the inverse problem. A series of inverse methods has been proposed to solve the inverse problem, ranging from trial-and-error manual calibration to the current complex automatic data assimilation algorithms. This paper does not attempt to be another overview paper on inverse models, but rather to analyze and track the evolution of the inverse methods over the last decades, mostly within the realm of hydrogeology, revealing their transformation, motivation and recent trends. Issues confronted by the inverse problem, such as dealing with multiGaussianity and whether or not to preserve the prior statistics are discussed. (C) 2013 Elsevier Ltd. All rights reserved.The authors gratefully acknowledge the financial support by the Spanish Ministry of Science and Innovation through project CGL2011-23295. We would like to thank Dr. Alberto Guadagnini (Politecnico di Milano, Italy) for his comments during the reviewing process, which helped improving the final paper.Zhou, H.; Gómez-Hernández, JJ.; Li, L. (2014). Inverse Methods in Hydrogeology: Evolution and Recent Trends. Advances in Water Resources. 63:22-37. https://doi.org/10.1016/j.advwatres.2013.10.014S22376

Transcriptomic analysis of crustacean neuropeptide signaling during the moult cycle in the green shore crab, Carcinus maenas

Abstract Background Ecdysis is an innate behaviour programme by which all arthropods moult their exoskeletons. The complex suite of interacting neuropeptides that orchestrate ecdysis is well studied in insects, but details of the crustacean ecdysis cassette are fragmented and our understanding of this process is comparatively crude, preventing a meaningful evolutionary comparison. To begin to address this issue we identified transcripts coding for neuropeptides and their putative receptors in the central nervous system (CNS) and Y-organs (YO) within the crab, Carcinus maenas, and mapped their expression profiles across accurately defined stages of the moult cycle using RNA-sequencing. We also studied gene expression within the epidermally-derived YO, the only defined role for which is the synthesis of ecdysteroid moulting hormones, to elucidate peptides and G protein-coupled receptors (GPCRs) that might have a function in ecdysis. Results Transcriptome mining of the CNS transcriptome yielded neuropeptide transcripts representing 47 neuropeptide families and 66 putative GPCRs. Neuropeptide transcripts that were differentially expressed across the moult cycle included carcikinin, crustacean hyperglycemic hormone-2, and crustacean cardioactive peptide, whilst a single putative neuropeptide receptor, proctolin R1, was differentially expressed. Carcikinin mRNA in particular exhibited dramatic increases in expression pre-moult, suggesting a role in ecdysis regulation. Crustacean hyperglycemic hormone-2 mRNA expression was elevated post- and pre-moult whilst that for crustacean cardioactive peptide, which regulates insect ecdysis and plays a role in stereotyped motor activity during crustacean ecdysis, was elevated in pre-moult. In the YO, several putative neuropeptide receptor transcripts were differentially expressed across the moult cycle, as was the mRNA for the neuropeptide, neuroparsin-1. Whilst differential gene expression of putative neuropeptide receptors was expected, the discovery and differential expression of neuropeptide transcripts was surprising. Analysis of GPCR transcript expression between YO and epidermis revealed 11 to be upregulated in the YO and thus are now candidates for peptide control of ecdysis. Conclusions The data presented represent a comprehensive survey of the deduced C. maenas neuropeptidome and putative GPCRs. Importantly, we have described the differential expression profiles of these transcripts across accurately staged moult cycles in tissues key to the ecdysis programme. This study provides important avenues for the future exploration of functionality of receptor-ligand pairs in crustaceans

Unravelling the evolution of the Allatostatin-Type A, KISS and Galanin Peptide-Receptor gene families in Bilaterians: insights from Anopheles Mosquitoes

Allatostatin type A receptors (AST-ARs) are a group of G-protein coupled receptors activated by members of the FGL-amide (AST-A) peptide family that inhibit food intake and development in arthropods. Despite their physiological importance the evolution of the AST-A system is poorly described and relatively few receptors have been isolated and functionally characterised in insects. The present study provides a comprehensive analysis of the origin and comparative evolution of the AST-A system. To determine how evolution and feeding modified the function of AST-AR the duplicate receptors in Anopheles mosquitoes, were characterised. Phylogeny and gene synteny suggested that invertebrate AST-A receptors and peptide genes shared a common evolutionary origin with KISS/GAL receptors and ligands. AST-ARs and KISSR emerged from a common gene ancestor after the divergence of GALRs in the bilaterian genome. In arthropods, the AST-A system evolved through lineage-specific events and the maintenance of two receptors in the flies and mosquitoes (Diptera) was the result of a gene duplication event. Speciation of Anophelesmosquitoes affected receptor gene organisation and characterisation of AST-AR duplicates (GPRALS1 and 2) revealed that in common with other insects, the mosquito receptors were activated by insect AST-A peptides and the iCa(2+)-signalling pathway was stimulated. GPRALS1 and 2 were expressed mainly in mosquito midgut and ovaries and transcript abundance of both receptors was modified by feeding. A blood meal strongly up-regulated expression of both GPRALS in the midgut (p < 0.05) compared to glucose fed females. Based on the results we hypothesise that the AST-A system in insects shared a common origin with the vertebrate KISS system and may also share a common function as an integrator of metabolism and reproduction. Highlights: AST-A and KISS/GAL receptors and ligands shared common ancestry prior to the protostome-deuterostome divergence. Phylogeny and gene synteny revealed that AST-AR and KISSR emerged after GALR gene divergence. AST-AR genes were present in the hemichordates but were lost from the chordates. In protostomes, AST-ARs persisted and evolved through lineage-specific events and duplicated in the arthropod radiation. Diptera acquired and maintained functionally divergent duplicate AST-AR genes.Foundation for Science and Technology, Portugal (FCT) [PTDC/BIA-BCM/114395/2009]; European Regional Development Fund (ERDF) COMPETE - Operational Competitiveness Programme; Portuguese funds through FCT Foundation for Science and Technology [PEst-C/MAR/LA0015/2013, UID/Multi/04326/2013, PEst-OE/SAU/LA0018/2013]; FCT [SFRH/BPD/89811/2012, SFRH/BPD/80447/2011, SFRH/BPD/66742/2009]; auxiliary research contract FCT Pluriannual funds [PEst-C/MAR/LA0015/2013, UID/Multi/04326/2013]info:eu-repo/semantics/publishedVersio

The Hemopoietic Stem Cell Niche Versus the Microenvironment of the Multiple Myeloma-Tumor Initiating Cell

Multiple myeloma cells are reminiscent of hemopoietic stem cells in their strict dependence upon the bone marrow microenvironment. However, from all other points of view, multiple myeloma cells differ markedly from stem cells. The cells possess a mature phenotype and secrete antibodies, and have thus made the whole journey to maturity, while maintaining a tumor phenotype. Not much credence was given to the possibility that the bulk of plasma-like multiple myeloma tumor cells is generated from tumor-initiating cells. Although interleukin-6 is a major contributor to the formation of the tumor’s microenvironment in multiple myeloma, it is not a major factor within hemopoietic stem cell niches. The bone marrow niche for myeloma cells includes the activity of inflammatory cytokines released through osteoclastogenesis. These permit maintenance of myeloma cells within the bone marrow. In contrast, osteoclastogenesis constitutes a signal that drives hemopoietic stem cells away from their bone marrow niches. The properties of the bone marrow microenvironment, which supports myeloma cell maintenance and proliferation, is therefore markedly different from the characteristics of the hemopoietic stem cell niche. Thus, multiple myeloma presents an example of a hemopoietic tumor microenvironment that does not resemble the corresponding stem cell renewal niche

Cokriging for multivariate Hilbert space valued random fields: application to multi-fidelity computer code emulation

In this paper we propose Universal trace co-kriging, a novel methodology for interpolation of multivariate Hilbert space valued functional data. Such data commonly arises in multi-fidelity numerical modeling of the subsurface and it is a part of many modern uncertainty quantification studies. Besides theoretical developments we also present methodological evaluation and comparisons with the recently published projection based approach by Bohorquez et al. (Stoch Environ Res Risk Assess 31(1):53–70, 2016. https://doi.org/10.1007/s00477-016-1266-y). Our evaluations and analyses were performed on synthetic (oil reservoir) and real field (uranium contamination) subsurface uncertainty quantification case studies. Monte Carlo analyses were conducted to draw important conclusions and to provide practical guidelines for all future practitioners