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  • 1
    Publication Date: 2012-10-12
    Description: Diabetes is the major cause of end stage renal disease, and tubular alterations are now considered to participate in the development and progression of diabetic nephropathy (DN). Here, we report for the first time that expression of the insulin receptor (IR) in human kidney is altered during diabetes. We detected a strong expression in proximal and distal tubules from human renal cortex, and a significant reduction in type 2 diabetic patients. Moreover, isolated proximal tubules from type 1 diabetic rat kidney showed a similar response, supporting its use as an excellent model for in vitro study of human DN. IR protein down-regulation was paralleled in proximal and distal tubules from diabetic rats, but prominent in proximal tubules from diabetic patients. A target of renal insulin signaling, the gluconeogenic enzyme phosphoenolpyruvate carboxykinase (PEPCK), showed increased expression and activity, and localization in compartments near the apical membrane of proximal tubules, which was correlated with activation of the GSK3β kinase in this specific renal structure in the diabetic condition. Thus, expression of IR protein in proximal tubules from type 1 and type 2 diabetic kidney indicates that this is a common regulatory mechanism which is altered in DN, triggering enhanced gluconeogenesis regardless the etiology of the disease. J. Cell. Biochem. © 2012 Wiley Periodicals, Inc.
    Electronic ISSN: 0091-7419
    Topics: Biology , Chemistry and Pharmacology , Medicine
    Published by Wiley
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  • 2
    Publication Date: 2012-07-26
    Description: Glioblastoma multiforme (GBM) cells are characterised by their extreme chemoresistance. The activity of multiple-drug resistance (MDR) transporters that extrude antitumour drugs from cells plays the most important role in this phenomenon. To date, the mechanism controlling the expression and activity of MDR transporters is poorly understood. Activity of the enzyme ecto-5'-nucleotidase (CD73) in tumour cells, which hydrolyses AMP to adenosine, has been linked to immunosuppression and prometastatic effects in breast cancer and to the proliferation of glioma cells. In this study, we identify a high expression of CD73 in surgically resected samples of human GBM. In primary cultures of GBM, inhibition of CD73 activity or knocking down its expression by siRNA reversed the MDR phenotype and cell viability was decreased up to 60% on exposure to the antitumoural drug vincristine. This GBM chemosensitization was caused by a decrease in the expression and activity of the multiple drug associated protein 1 (Mrp1), the most important transporter conferring multiple drug resistance in these cells. Using pharmacological modulators we have recognised the adenosine A 3 receptor subtype in mediation of the chemoresistant phenotype in these cells. In conclusion, we have determined that the activity of CD73 to trigger adenosine signalling sustains chemoresistant phenotype in GBM cells. J. Cell. Physiol. © 2012 Wiley Periodicals, Inc.
    Electronic ISSN: 1097-4652
    Topics: Biology , Medicine
    Published by Wiley
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  • 3
    Publication Date: 2012-06-16
    Description: The catalytic activity of two CNC palladium pincer complexes is evaluated in two fundamental C C bond-forming reactions: Mizoroki Heck and Sonogashira cross-couplings. After several optimization attempts and a brief comparison with a PCN pincer catalyst, a number of arylated alkenes and diarylethynes are synthesized by procedures based on the catalytic use of the above mentioned CNC Pd pincers in H 2 O and DMF.
    Print ISSN: 0018-019X
    Electronic ISSN: 1522-2675
    Topics: Chemistry and Pharmacology
    Published by Wiley
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  • 4
    Publication Date: 2017-08-19
    Description: ABSTRACT Temporal variability is an important feature of climate, comprising systematic variations such as the annual cycle, as well as residual temporal variations such as short-term variations, spells and variability from interannual to long-term trends. The EU-COST Action VALUE developed a comprehensive framework to evaluate downscaling methods. Here we present the evaluation of the perfect predictor experiment for temporal variability. Overall, the behaviour of the different approaches turned out to be as expected from their structure and implementation. The chosen regional climate model adds value to reanalysis data for most considered aspects, for all seasons and for both temperature and precipitation. Bias correction methods do not directly modify temporal variability apart from the annual cycle. However, wet day corrections substantially improve transition probabilities and spell length distributions, whereas interannual variability is in some cases deteriorated by quantile mapping. The performance of perfect prognosis (PP) statistical downscaling methods varies strongly from aspect to aspect and method to method, and depends strongly on the predictor choice. Unconditional weather generators tend to perform well for the aspects they have been calibrated for, but underrepresent long spells and interannual variability. Long-term temperature trends of the driving model are essentially unchanged by bias correction methods. If precipitation trends are not well simulated by the driving model, bias correction further deteriorates these trends. The performance of PP methods to simulate trends depends strongly on the chosen predictors. This paper presents the results of the comprehensive VALUE perfect predictor experiment, a downscaling exercise for Europe with predictors and boundary conditions from reanalysis data. The performance of a wide range of statistical downscaling, bias correction and weather generator methods to represent temporal aspects of local weather is evaluated and compared with reanalysis data as well as regional climate model simulations.
    Print ISSN: 0899-8418
    Electronic ISSN: 1097-0088
    Topics: Geosciences , Physics
    Published by Wiley
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  • 5
    Publication Date: 2019
    Description: This article presents the results of the comprehensive VALUE perfect predictor experiment, a downscaling exercise for Europe with predictors and boundary conditions from reanalysis data. The performance of a wide range of statistical downscaling, bias correction and weather generator methods to represent temporal aspects of local weather is evaluated and compared with reanalysis data as well as regional climate model simulations. Temporal variability is an important feature of climate, comprising systematic variations such as the annual cycle, as well as residual temporal variations such as short‐term variations, spells and variability from interannual to long‐term trends. The EU‐COST Action VALUE developed a comprehensive framework to evaluate downscaling methods. Here we present the evaluation of the perfect predictor experiment for temporal variability. Overall, the behaviour of the different approaches turned out to be as expected from their structure and implementation. The chosen regional climate model adds value to reanalysis data for most considered aspects, for all seasons and for both temperature and precipitation. Bias correction methods do not directly modify temporal variability apart from the annual cycle. However, wet day corrections substantially improve transition probabilities and spell length distributions, whereas interannual variability is in some cases deteriorated by quantile mapping. The performance of perfect prognosis (PP) statistical downscaling methods varies strongly from aspect to aspect and method to method, and depends strongly on the predictor choice. Unconditional weather generators tend to perform well for the aspects they have been calibrated for, but underrepresent long spells and interannual variability. Long‐term temperature trends of the driving model are essentially unchanged by bias correction methods. If precipitation trends are not well simulated by the driving model, bias correction further deteriorates these trends. The performance of PP methods to simulate trends depends strongly on the chosen predictors.
    Print ISSN: 0899-8418
    Electronic ISSN: 1097-0088
    Topics: Geosciences , Physics
    Published by Wiley
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  • 6
    Publication Date: 2019
    Description: The largest and most comprehensive to date intercomparison of statistical downscaling methods is presented, with a total of over 50 downscaling methods representative of the most common approaches and techniques. Overall, most of the downscaling methods greatly improve raw model biases and no approach is superior in general, due to the large method‐to‐method variability. The main factors influencing the results are the seasonal calibration of the methods and their stochastic nature, for biases in the mean and variance. VALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, process‐based, etc.). Here we describe the participating methods and first results from the first experiment, using “perfect” reanalysis (and reanalysis‐driven regional climate model (RCM)) predictors to assess the intrinsic performance of the methods for downscaling precipitation and temperatures over a set of 86 stations representative of the main climatic regions in Europe. This study constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods, covering the three common downscaling approaches (perfect prognosis, model output statistics—including bias correction—and weather generators) with a total of over 50 downscaling methods representative of the most common techniques. Overall, most of the downscaling methods greatly improve (reanalysis or RCM) raw model biases and no approach or technique seems to be superior in general, because there is a large method‐to‐method variability. The main factors most influencing the results are the seasonal calibration of the methods (e.g., using a moving window) and their stochastic nature. The particular predictors used also play an important role in cases where the comparison was possible, both for the validation results and for the strength of the predictor–predictand link, indicating the local variability explained. However, the present study cannot give a conclusive assessment of the skill of the methods to simulate regional future climates, and further experiments will be soon performed in the framework of the EURO‐CORDEX initiative (where VALUE activities have merged and follow on). Finally, research transparency and reproducibility has been a major concern and substantive steps have been taken. In particular, the necessary data to run the experiments are provided at http://www.value‐cost.eu/data and data and validation results are available from the VALUE validation portal for further investigation: http://www.value‐cost.eu/validationportal.
    Print ISSN: 0899-8418
    Electronic ISSN: 1097-0088
    Topics: Geosciences , Physics
    Published by Wiley
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  • 7
    Publication Date: 2019
    Description: The VALUE perfect predictor experiment SDMs are examined following the so‐called “regime‐oriented” technique, focused on relevant atmospheric circulation features and processes, from large to local scales. Overall, SDMs show a reasonable performance representing the large spectra of atmospheric phenomena analysed, although the PP methods reveal a more differentiated behaviour than MOS. As expected, MOS methods are unable to generate process sensitivity when it is not simulated by the predictors (ERA‐Interim). Although PP methods are conditioned on predictors typically representing the large‐scale circulation, many PP methods frequently fail in capturing the process sensitivity. In the figure, winter NAO conditioned biases for precipitation for the raw model outputs and the different MOS and PP methods. In (c, d) boxes span the 25–75% range, the whiskers the maximum value within 1.5 times the interquartile range, values outside that range are plotted individually; average results over the different PRUDENCE regions are indicated by a coloured horizontal bar (see the colours in the bottom legend). Statistical downscaling methods (SDMs) are techniques used to downscale and/or bias‐correct climate model results to regional or local scales. The European network VALUE developed a framework to evaluate and inter‐compare SDMs. One of VALUE's experiments is the perfect predictor experiment that uses reanalysis predictors to isolate downscaling skill. Most evaluation papers for SDMs employ simple statistical diagnostics and do not follow a process‐based rationale. Thus, in this paper, a process‐based evaluation has been conducted for the more than 40 participating model output statistics (MOS, mostly bias correction) and perfect prognosis (PP) methods, for temperature and precipitation at 86 weather stations across Europe. The SDMs are analysed following the so‐called “regime‐oriented” technique, focussing on relevant features of the atmospheric circulation at large to local scales. These features comprise the North Atlantic Oscillation, blocking and selected Lamb weather types and at local scales the bora wind and the western Iberian coastal‐low level jet. The representation of the local weather response to the selected features depends strongly on the method class. As expected, MOS is unable to generate process sensitivity when it is not simulated by the predictors (ERA‐Interim). Moreover, MOS often suffers from an inflation effect when a predictor is used for more than one station. The PP performance is very diverse and depends strongly on the implementation. Although conditioned on predictors that typically describe the large‐scale circulation, PP often fails in capturing the process sensitivity correctly. Stochastic generalized linear models supported by well‐chosen predictors show improved skill to represent the sensitivities.
    Print ISSN: 0899-8418
    Electronic ISSN: 1097-0088
    Topics: Geosciences , Physics
    Published by Wiley
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  • 8
  • 9
    Publication Date: 2019-01-24
    Description: Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming 〉2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming 〈2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade.
    Print ISSN: 1354-1013
    Electronic ISSN: 1365-2486
    Topics: Biology , Energy, Environment Protection, Nuclear Power Engineering , Geography
    Published by Wiley
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  • 10
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