1,147 research outputs found

    T-cell allorecognition of donor glutathione S-transferase T1 in plasma cell-rich rejection

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    AIM: To investigate the role of glutathione S-transferase T1 donor-specific T lymphocytes in plasma cell-rich rejection of liver allografts. METHODS: The study group included 22 liver transplant patients. Among them, 18 patients were mismatched for the glutathione S-transferase T1 (GSTT1) alleles (don+/rec-), and 4 were matched (don+/rec+). Seven of the mismatched patients produced anti-GSTT1 antibodies and developed plasma cell-rich rejection (former de novo immune hepatitis). For the detection of specific T lymphocytes, peripheral blood mononuclear cells were collected and stored in liquid nitrogen. The memory T cell response was studied by adding to the cell cultures to a mix of 39 custom-made, 15-mer overlapping peptides, which covered the entire GSTT1 amino acid sequence. The specific cellular response to peptides was analyzed by flow cytometry using the markers CD8, CD4, IL-4 and IFNγ. RESULTS: Activation of CD8+ T cells with different peptides was observed exclusively in the group of patients with plasma-cell rich rejection (3 out of 7), with production of IL-4 and/or IFNγ at a rate of 1%-4.92% depending on the peptides. The CD4+ response was most common and not exclusive for patients with the disease, where 5 out of 7 showed percentages of activated cells from 1.24% to 31.34%. Additionally, two patients without the disease but with the mismatch had cells that became stimulated with some peptides (1.45%-5.18%). Highly unexpected was the finding of a double positive CD4+CD8low T cell population that showed the highest degree of activation with some of the peptides in 7 patients with the mismatch, in 4 patients with plasma cell-rich rejection and in 3 patients without the disease. Unfortunately, CD4+CD8low cells represent 1% of the total number of lymphocytes, and stimulation could not be analyzed in 9 patients due to the low number of gated cells. Cells from the 4 patients included as controls did not show activation with any of the peptides. CONCLUSION: Patients with GSTT1 mismatch can develop a specific T-cell response, but the potential role of this response in the pathogenesis of plasma cell-rich rejection is unknown

    Knigth's Move in the Periodic Table, From Copper to Platinum, Novel Antitumor Mixed Chelate Copper Compounds, Casiopeinas, Evaluated by an in Vitro Human and Murine Cancer Cell Line Panel

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    We synthesized a novel anticancer agents based on mixed chelate copper (II) complexes, named Casiopeínas® has of general formula [Cu(N-N)(N-O)H2O]NO3 (where, N-N = diimines as 1,10- phenanthroline, 2,2-bipyridine, or substituted and N-O=aminoeidate or [Cu(N-N)(O-O)H2O]NO3 (where NN= diimines as 10-phenanthroline, 2,2-bipyridine or substituted Casiopeínas I, II, IV, V, VI, VII VIII and O-O=acetylacetonate, salicylaldehidate Casiopínas III). We evaluated the in vitro antitumor activity using a human cancer cell panel and some nurine cancer cells. Eleven Casiopeinas are evaluated in order to acquire some structure-activity correlations and some monodentated Casiopeinäs analogues; cisplatinum was used as control drug. The 50% growth inhibition observed is, in all cases reach with concentrations of Casiopeina's 10 or 100 times lower than cisplatinum. In a previous work we reported the induction of apoptosis by Casiopeina II. The results indicate that Casiopeinass are a promising new anticancer drug candidates to be developed further toward clinical trials

    Characterization of non-Gaussian conductivities and porosities with hydraulic heads, solute concentrations, and water temperatures

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    Reliable characterization of hydraulic parameters is important for the understanding of groundwater flow and solute transport. The normal-score ensemble Kalman filter (NS-EnKF) has proven to be an effective inverse method for the characterization of non-Gaussian hydraulic conductivities by assimilating transient piezometric head data, or solute concentration data. Groundwater temperature, an easily captured state variable, has not drawn much attention as an additional state variable useful for the characterization of aquifer parameters. In this work, we jointly estimate non-Gaussian aquifer parameters (hydraulic conductivities and porosities) by assimilating three kinds of state variables (piezometric head, solute concentration, and groundwater temperature) using the NS-EnKF. A synthetic example including seven tests is designed, and used to evaluate the ability to characterize hydraulic conductivity and porosity in a non-Gaussian setting by assimilating different numbers and types of state variables. The results show that characterization of aquifer parameters can be improved by assimilating groundwater temperature data and that the main patters of the non-Gaussian reference fields can be retrieved with more accuracy and higher precision if multiple state variables are assimilated.Financial support to carry out this work was provided by the Spanish Ministry of Economy and Competitiveness through project CGL2014-59841-P. All data used in this analysis are available from the authors.Xu, T.; Gómez-Hernández, JJ. (2016). Characterization of non-Gaussian conductivities and porosities with hydraulic heads, solute concentrations, and water temperatures. Water Resources Research. 52(8):6111-6136. https://doi.org/10.1002/2016WR019011S61116136528Alcolea, 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.2005.12.009Anderson, M. P. (2005). Heat as a Ground Water Tracer. Ground Water, 43(6), 951-968. doi:10.1111/j.1745-6584.2005.00052.xBravo, H. R., Jiang, F., & Hunt, R. J. (2002). Using groundwater temperature data to constrain parameter estimation in a groundwater flow model of a wetland system. Water Resources Research, 38(8), 28-1-28-14. doi:10.1029/2000wr000172Capilla, 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.2009.03.015Chang, H., Zhang, D., & Lu, Z. (2010). History matching of facies distribution with the EnKF and level set parameterization. Journal of Computational Physics, 229(20), 8011-8030. doi:10.1016/j.jcp.2010.07.005Chen , Y. D. S. Oliver 2010Chen, Y., Oliver, D. S., & Zhang, D. (2009). Data assimilation for nonlinear problems by ensemble Kalman filter with reparameterization. Journal of Petroleum Science and Engineering, 66(1-2), 1-14. doi:10.1016/j.petrol.2008.12.002Doussan, C., Toma, A., Paris, B., Poitevin, G., Ledoux, E., & Detay, M. (1994). Coupled use of thermal and hydraulic head data to characterize river-groundwater exchanges. Journal of Hydrology, 153(1-4), 215-229. doi:10.1016/0022-1694(94)90192-9Dovera, L., & Della Rossa, E. (2010). Multimodal ensemble Kalman filtering using Gaussian mixture models. Computational Geosciences, 15(2), 307-323. doi:10.1007/s10596-010-9205-3Evensen, G. (2003). The Ensemble Kalman Filter: theoretical formulation and practical implementation. Ocean Dynamics, 53(4), 343-367. doi:10.1007/s10236-003-0036-9Franssen, 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)00191-4Fu, J., & Jaime Gómez-Hernández, J. (2009). Uncertainty assessment and data worth in groundwater flow and mass transport modeling using a blocking Markov chain Monte Carlo method. Journal of Hydrology, 364(3-4), 328-341. doi:10.1016/j.jhydrol.2008.11.014Gómez-Hernández, J. J., & Journel, A. G. (1993). Joint Sequential Simulation of MultiGaussian Fields. Geostatistics Tróia ’92, 85-94. doi:10.1007/978-94-011-1739-5_8Gómez-Hernández, J. J., & Wen, X.-H. (1994). Probabilistic assessment of travel times in groundwater modeling. Stochastic Hydrology and Hydraulics, 8(1), 19-55. doi:10.1007/bf01581389G�mez-Hern�ndez, J. J., Franssen, H.-J. W. M. H., & Sahuquillo, A. (2003). Stochastic conditional inverse modeling of subsurface mass transport: A brief review and the self-calibrating method. Stochastic Environmental Research and Risk Assessment (SERRA), 17(5), 319-328. doi:10.1007/s00477-003-0153-5Gordon , N. D. Salmond A. Smith 1993 Novel approach to nonlinear/non-Gaussian Bayesian state estimation Proc. Inst. Electr. Eng. 140 107 113Gu, Y., & Oliver, D. S. (2005). The Ensemble Kalman Filter for Continuous Updating of Reservoir Simulation Models. Journal of Energy Resources Technology, 128(1), 79-87. doi:10.1115/1.2134735Gu, Y., & Oliver, D. S. (2007). An Iterative Ensemble Kalman Filter for Multiphase Fluid Flow Data Assimilation. SPE Journal, 12(04), 438-446. doi:10.2118/108438-paHu, L. Y. (2000). Mathematical Geology, 32(1), 87-108. doi:10.1023/a:1007506918588Kalman, R. E. (1960). A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1), 35-45. doi:10.1115/1.3662552Kurtz, W., Hendricks Franssen, H.-J., Kaiser, H.-P., & Vereecken, H. (2014). Joint assimilation of piezometric heads and groundwater temperatures for improved modeling of river-aquifer interactions. Water Resources Research, 50(2), 1665-1688. doi:10.1002/2013wr014823Li, L., Zhou, H., Gómez-Hernández, J. J., & Hendricks Franssen, H.-J. (2012). Jointly mapping hydraulic conductivity and porosity by assimilating concentration data via ensemble Kalman filter. Journal of Hydrology, 428-429, 152-169. doi:10.1016/j.jhydrol.2012.01.037Li, 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-8-6749-2011Liu , N. D. Oliver 2005 Critical evaluation of the ensemble Kalman filter on history matching of geologic facies SPE Reservoir Eval. Eng. 8 6 470 477Losa, S. N., Kivman, G. A., Schröter, J., & Wenzel, M. (2003). Sequential weak constraint parameter estimation in an ecosystem model. Journal of Marine Systems, 43(1-2), 31-49. doi:10.1016/j.jmarsys.2003.06.001Ma , R. C. Zheng 2010 Effects of density and viscosity in modeling heat as a groundwater tracer, Groundwater 48 3 380 389Ma, R., Zheng, C., Zachara, J. M., & Tonkin, M. (2012). Utility of bromide and heat tracers for aquifer characterization affected by highly transient flow conditions. Water Resources Research, 48(8). doi:10.1029/2011wr011281McDonald , M. A. Harbaugh 1988Oliver, 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:10.1029/94wr02258Reich, S. (2011). A Gaussian-mixture ensemble transform filter. Quarterly Journal of the Royal Meteorological Society, 138(662), 222-233. doi:10.1002/qj.898Simon, E., & Bertino, L. (2009). Application of the Gaussian anamorphosis to assimilation in a 3-D coupled physical-ecosystem model of the North Atlantic with the EnKF: a twin experiment. Ocean Science, 5(4), 495-510. doi:10.5194/os-5-495-2009Strebelle, S. (2002). Mathematical Geology, 34(1), 1-21. doi:10.1023/a:1014009426274Sun, A. Y., Morris, A. P., & Mohanty, S. (2009). Sequential updating of multimodal hydrogeologic parameter fields using localization and clustering techniques. Water Resources Research, 45(7). doi:10.1029/2008wr007443Van Leeuwen, P. J. (2009). Particle Filtering in Geophysical Systems. Monthly Weather Review, 137(12), 4089-4114. doi:10.1175/2009mwr2835.1Wang, Y., Li, G., & Reynolds, A. C. (2010). Estimation of Depths of Fluid Contacts by History Matching Using Iterative Ensemble-Kalman Smoothers. SPE Journal, 15(02), 509-525. doi:10.2118/119056-paWen, X.-H., & Chen, W. H. (2006). Real-Time Reservoir Model Updating Using Ensemble Kalman Filter With Confirming Option. SPE Journal, 11(04), 431-442. doi:10.2118/92991-paWen, X.-H., Deutsch, C. V., & Cullick, A. S. (2002). Construction of geostatistical aquifer models integrating dynamic flow and tracer data using inverse technique. Journal of Hydrology, 255(1-4), 151-168. doi:10.1016/s0022-1694(01)00512-1Wen, 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)00126-5Xu, T., & Gómez‐Hernández, J. J. (2015). Inverse sequential simulation: A new approach for the characterization of hydraulic conductivities demonstrated on a non‐ G aussian field. Water Resources Research, 51(4), 2227-2242. doi:10.1002/2014wr016320Xu, T., & Gómez-Hernández, J. J. (2015). Inverse sequential simulation: Performance and implementation details. Advances in Water Resources, 86, 311-326. doi:10.1016/j.advwatres.2015.04.015Xu, T., Jaime Gómez-Hernández, J., Zhou, H., & Li, L. (2013). The power of transient piezometric head data in inverse modeling: An application of the localized normal-score EnKF with covariance inflation in a heterogenous bimodal hydraulic conductivity field. Advances in Water Resources, 54, 100-118. doi:10.1016/j.advwatres.2013.01.006Zheng , C. 2010Zhou, 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/s11004-011-9372-3Zhou, H., Gómez-Hernández, J. J., & Li, L. (2014). Inverse methods in hydrogeology: Evolution and recent trends. Advances in Water Resources, 63, 22-37. doi:10.1016/j.advwatres.2013.10.01

    Specificity determinants for Cry insecticidal proteins: insights from their mode of action

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    Insecticidal proteins from the bacterium Bacillus thuringiensis (Bt) are used as active components of biopesticides and as plant incorporated protectants in transgenic crops. One of the most relevant attributes of these Bt protein-based insecticidal technologies is their high specificity, which assures lack of detrimental effects on non-target insects, vertebrates and the environment. The identification of specificity determinants in Bt insecticidal proteins could guide risk assessment for novel insecticidal proteins currently considered for commercialization. In this work we review the available data on specificity determinants of crystal (Cry) insecticidal proteins as the Bt toxins most well characterized and used in transgenic crops. The multi step mode of action of the Cry insecticidal proteins allows various factors to potentially affect specificity determination and here we define seven levels that could influence specificity. The relative relevance of each of these determinants on efficacy of transgenic crops producing Cry insecticidal proteins is also discussed

    Drivers of joint cropland management strategies in agri-food cooperatives

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    [EN] In several Spanish regions, collective action through production and marketing cooperatives has traditionally concentrated the food supply of small and medium-sized farms. However, many cooperatives are threatened by the risk of abandonment of members' cropland, which reduces their sourcing capacity. In this context, joint cropland management initiatives have become a useful form of social and organizational innovation. This research's contribution is twofold: it examines the relevance of some drivers of this organizational innovation, and it determines the cooperative characteristics or combinations of characteristics that can sufficiently explain the adoption of a joint cropland management strategy. Some cooperatives' features have been a priori identified as related to the achievement of joint cropland initiatives: economic size, social innovation, innovative behavior, and collaborative orientation. The study is mainly based on data from a cooperatives survey, and fuzzy set Qualitative Comparative Analysis (fsQCA) methodology has been used. The analysis has been completed by surveying cooperatives' managers about their opinions on a joint cropland management strategy's main advantages and drivers. Results indicate that social and economic innovation, size, and propensity to cooperate with other cooperatives are key factors that help create a cooperative profile capable of tackling the challenge of land abandonment and the consequent loss of production.Ministry of Science and Innovation, Spain, European Regional Development Fund, European Commission. Project "Strengthening innovation policy in the agri-food sector" (RTI2018-093791-B-C22).Piñeiro, V.; Martinez Gomez, VD.; Melia-Marti, E.; García Alvarez-Coque, JM. (2021). Drivers of joint cropland management strategies in agri-food cooperatives. Journal of Rural Studies. 84:162-173. https://doi.org/10.1016/j.jrurstud.2021.04.003S1621738

    Open Science principles for accelerating trait-based science across the Tree of Life

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    Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. Here, we outline how the adoption of key Open Science principles-open data, open source and open methods-is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges
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