ALBERT

Description: Tetracyclines, sulfonamides and amphenicols are broad spectrum antimicrobial drugs that are widely used in poultry farming. However, a high proportion of these drugs can be excreted at high concentrations in droppings, even after the end of a therapy course. This work intended to assess and compare concentrations of florfenicol (FF), florfenicol amine (FFa), chlortetracycline (CTC), 4-epi-chlortetracycline (4-epi-CTC), and sulfachloropyridazine (SCP) in broiler chicken droppings. To this end, 70 chickens were housed under controlled environmental conditions, and assigned to experimental groups that were treated with therapeutic doses of either 10% FF, 20% CTC, or 10% SCP. Consequently, we implemented and designed an in-house validation for three analytical methodologies, which allowed us to quantify the concentrations of these three antimicrobial drugs using liquid chromatography coupled to mass spectrometry (LC-MS/MS). Our results showed that FF and FFa concentrations were detected in chicken droppings up to day 10 after ceasing treatment, while CTC and 4-epi-CTC were detected up to day 25. As for SCP residues, these were detected up to day 21. Noticeably, CTC showed the longest excretion period, as well as the highest concentrations detected after the end of its administration using therapeutic doses.

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 necessity of transport electrification is already undeniable due to, among other facts, global Greenhouse Gas (GHG) emissions and fossil-fuel dependency. In this context, electric vehicles (EVs) play a fundamental role. Such vehicles are usually seen by the network as simple loads whose needs have to be supplied. However, they can contribute to the correct operation of the network or a microgrid and the provision of ancillary services and delay the need to reinforce the power lines. These concepts are referred to as Vehicle-to-Grid (V2G), Vehicle-to-Building (V2B) and Vehicle-to-Home (V2H). In paper, a deep classification and analysis of published charging strategies is provided. In addition, optimal charging strategies must minimise the degradation of the batteries to increase their lifetime, since it is considered that the life of a battery ends when its capacity is reduced by 20% with respect to its nominal capacity. Therefore, an optimal integration of EVs must consider both grid and batteries impact. Finally, some guidelines are proposed for further research considering the current limitations of electric vehicle technology. Thus, these proposed guidelines are focused on V2G optimal management, enabling new business models while keeping economic viability for all parts involved.

Description: Electric vehicles (EVs) are a promising technology to reduce emissions, but its development enormously depends on the technology used in batteries. Nowadays, batteries based on lithium-ion (Li-Ion) seems to be the most suitable for traction, especially nickel-manganese-cobalt (NMC) and nickel-cobalt-aluminum (NCA). An appropriate model of these batteries is fundamental for the simulation of several processes inside an EV, such as the state of charge (SoC) estimation, capacity and power fade analysis, lifetime calculus, or for developing control and optimization strategies. There are different models in the current literature, among which the electric equivalent circuits stand out, being the most appropriate model when performing real-time simulations. However, impedance models for battery diagnosis are considered very attractive. In this context, this paper compares and contrasts the different electrical equivalent circuit models, impedance models, and runtime models for battery-based EV applications, addressing their characteristics, advantages, disadvantages, and usual applications in the field of electromobility. In this sense, this paper serves as a reference for the scientific community focused on the development of control and optimization strategies in the field of electric vehicles, since it facilitates the choice of the model that best suits the needs required.

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: A gravitational field model based on two symmetric tensors, g μ ν and g ˜ μ ν , is studied, using a Markov Chain Monte Carlo (MCMC) analysis with the most updated catalog of SN-Ia. In this model, new matter fields are added to the original matter fields, motivated by an additional symmetry ( δ ˜ symmetry). We call them δ ˜ matter fields. This theory predicts an accelerating Universe without the need to introduce a cosmological constant Λ by hand in the equations. We obtained a very good fit to the SN-Ia Data, and with this, we found the two free parameters of the theory called C and L 2 . With these values, we have fixed all the degrees of freedom in the model. The last H 0 local value measurement is in tension with the CMB Data from Planck. Based on an absolute magnitude M V = − 19.23 for the SN, Delta Gravity finds H 0 to be 74.47 ± 1.63 km/(s Mpc). This value is in concordance with the last measurement of the H 0 local value, 73.83 ± 1.48 km/(s Mpc).

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.