ALBERT

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.

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.

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.

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.

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.

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.

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.

Description: The increasing demand for high-resolution climate information has attracted growing attention to statistical downscaling (SDS) methods, due in part to their relative advantages and merits as compared to dynamical approaches (based on regional climate model simulations), such as their much lower computational cost and their fitness for purpose for many local-scale applications. As a result, a plethora of SDS methods is nowadays available to climate scientists, which has motivated recent efforts for their comprehensive evaluation, like the VALUE initiative (http://www.value-cost.eu, last access: 29 March 2020). The systematic intercomparison of a large number of SDS techniques undertaken in VALUE, many of them independently developed by different authors and modeling centers in a variety of languages/environments, has shown a compelling need for new tools allowing for their application within an integrated framework. In this regard, downscaleR is an R package for statistical downscaling of climate information which covers the most popular approaches (model output statistics – including the so-called “bias correction” methods – and perfect prognosis) and state-of-the-art techniques. It has been conceived to work primarily with daily data and can be used in the framework of both seasonal forecasting and climate change studies. Its full integration within the climate4R framework (Iturbide et al., 2019) makes possible the development of end-to-end downscaling applications, from data retrieval to model building, validation, and prediction, bringing to climate scientists and practitioners a unique comprehensive framework for SDS model development. In this article the main features of downscaleR are showcased through the replication of some of the results obtained in VALUE, placing an emphasis on the most technically complex stages of perfect-prognosis model calibration (predictor screening, cross-validation, and model selection) that are accomplished through simple commands allowing for extremely flexible model tuning, tailored to the needs of users requiring an easy interface for different levels of experimental complexity. As part of the open-source climate4R framework, downscaleR is freely available and the necessary data and R scripts to fully replicate the experiments included in this paper are also provided as a companion notebook.