Electrochemical devices for cholesterol detection

Cholesterol can be considered as a biomarker of illnesses such as heart and coronary artery diseases or arteriosclerosis. Therefore, the fast determination of its concentration in blood is interesting as a means of achieving an early diagnosis of these unhealthy conditions. Electrochemical sensors and biosensors have become a potential tool for selective and sensitive detection of this biomolecule, combining the analytical advantages of electrochemical techniques with the selective recognition features of modified electrodes. This review covers the different approaches carried out in the development of electrochemical sensors for cholesterol, differentiating between enzymatic biosensors and non-enzymatic systems, highlighting lab-on-a-chip devices. A description of the different modification procedures of the working electrode has been included and the role of the different functional materials used has been discussed

Relation between hair cortisol concentration and meat quality

Oral session 1[EN] Currently, consumers’ concerns about their own health and animal welfare are in crescendo. For this reason, it is necessary to direct the cattle fattening period towards systems that seek the highest level of animal welfare and the highest quality of meat possible. This work aims to evaluate the relationship between the cortisol in hair (used as a welfare indicator) and the fatty acid content (used as an indicator for meat quality) depending on the forage provided during the fattening period

Evaluation of statistical downscaling methods for climate change projections over Spain: future conditions with pseudo reality (transferability experiment)

The Spanish Meteorological Agency (AEMET) is responsible for the elaboration of downscaled climate projections over Spain to feed the Second National Plan of Adaptation to Climate Change (PNACC-2) and this is the last of three papers aimed to evaluate and intercompare five empirical/statistical downscaling (ESD) methods developed at AEMET: (a) Analog, (b) Regression, (c) Artificial Neural Networks, (d) Support Vector Machines and (e) Kernel Ridge Regression, in order to decide which methods and under what configurations are more suitable for that purpose. Following the framework established by the EU COST Action VALUE, in this experiment we test the transferability of these methods to future climate conditions with the use of regional climate models (RCMs) as pseudo observations. We evaluate the marginal aspects of the distributions of daily maximum/minimum temperatures and daily accumulated precipitation, over mainland Spain and the Balearic Islands, analysed by season. For maximum/minimum temperatures all methods display certain transferability issues, being remarkable for Support Vector Machines and Kernel Ridge Regression. For precipitation all methods appear to suffer from transferability difficulties as well, although conclusions are not as clear as for temperature, probably due to the fact that precipitation does not present such a marked signal of change. This study has revealed how an analysis over a historical period is not enough to fully evaluate ESD methods, so we propose that some type of analysis of transferability should be added in a standard procedure of a complete evaluation.Marta Domínguez has received funding from the MEDSCOPE project co-funded by the European Commission as part of ERA4CS, an ERA-NET initiated by JPI Climate, grant agreement 690462. MEDSCOPE, Grant/Award Number: 69046

A critical view on the suitability of machine learning techniques to downscale climate change projections : illustration for temperature with a toy experiment

Machine learning is a growing field of research with many applications. It provides a series of techniques able to solve complex nonlinear problems, and that has promoted their application for statistical downscaling. Intercomparison exercises with other classical methods have so far shown promising results. Nevertheless, many evaluation studies of statistical downscaling methods neglect the analysis of their extrapolation capability. In this study, we aim to make a wakeup call to the community about the potential risks of using machine learning for statistical downscaling of climate change projections. We present a set of three toy experiments, applying three commonly used machine learning algorithms, two different implementations of artificial neural networks and a support vector machine, to downscale daily maximum temperature, and comparing them with the classical multiple linear regression. We have tested the four methods in and out of their calibration range, and have found how the three machine learning techniques can perform poorly under extrapolation. Additionally, we have analysed the impact of this extrapolation issue depending on the degree of overlapping between the training and testing datasets, and we have found very different sensitivities for each method and specific implementation

Basic income and women. Incentives and disincentives. Its effects on equality and on social roles

El objeto de este análisis es intentar comprobar cómo la Renta Básica puede ser un instrumento eficaz para propulsar y alcanzar la Igualdad de género y cómo se correlaciona con el salario y el empleo de la mujer. Para ello, las autoras delimitarán los conceptos implicados, el alcance en el marco del ordenamiento jurídico español e intentarán demostrar cómo el reconocimiento de la Renta Básica ayuda a aumentar la libertad de decisión de la mujer y, por ende, su mejor posición jurídica en el mercado laboral. Desde luego, con el objetivo de alcanzar una mayor Igualdad al enfrentarse a la promoción profesional, a unas más iguales retribuciones a igualdad de mérito y capacidad y a una entrada en el empleo que no suponga superar los obstáculos de género que, en la actualidad, existen con los estereotipos tradicionales que el Mercado Laboral mantiene. Así, será esencial que se analicen los posibles incentivos que el salario u otras condiciones de trabajo pueden suponer.The purpose of this analysis is to try to prove how the Basic Income can be an effective instrument to propel and achieve gender equality and how it correlates with wages and employment. For this, the authors will delimit the concepts involved, the scope within the Spanish legal framework and will try to demonstrate how the recognition of Basic Income helps to increase the freedom of decision of women and, therefore, their better legal position in the working market. Of course, with the aim of achieving greater equality when faced with the promotion of professionals, to more equal remuneration for equality of merit and ability and to entry into employment that does not involve overcoming gender barriers that currently exist, with the traditional stereotypes that the Labor Market maintains. Thus, it will be essential to analyze the possible incentives that salary or other working conditions may entail

Determination of aluminium using different techniques based on the Al(III)-morin complex

Three different methods for the determination of Al(III) in aqueous samples were compared. The different described procedures were based on the formation of the Al(III)-morin complex. UV–Vis spectrophotometry, spectrofluorimetry and differential pulse adsorptive stripping voltammetry (DPAdSV) techniques were compared under optimized experimental conditions. The DPAdSV method showed a better performance for the analysis of Al(III) in terms of capability of detection (70 nM) in comparison with the value obtained for UV–Vis spectrophotometric (300 nM) and spectrofluorimetic (110 nM) techniques. Thus, DPAdSV method was selected for the analysis of aluminium in river, tap and bottled water samples under the following optimized experimental conditions: pH = 4.4, deposition potential = +243 mV, deposition time = 210 s, giving satisfactory results

Statistical Downscaling in the Tropics and Midlatitudes: A Comparative Assessment over Two Representative Regions

Statistical downscaling (SD) of climate change projections is a key piece for impact and adaptation studies due to its low computational expense compared to dynamical downscaling, which allows exploration of uncertainties through the generation of large ensembles. SD has been extensively evaluated and applied in the extratropics, but few examples exist in tropical regions. In this study, several state-of-the-art methods belonging to different families have been evaluated for maximum/minimum daily temperature and daily accumulated precipitation (both from the ERA5 at 0.258) in two regions with very different climates: Spain (midlatitudes) and Central America (tropics). Some key assumptions of SD have been tested: the strength of the predictor–predictand links, the skill of different approaches, and the extrapolation capability of each method. It has been found that relevant predictors are different in both regions, as is the behavior of statistical methods. For temperature, most methods perform significantly better in Spain than in Central America, where transfer function (TF) methods present important extrapolation problems, probably due to the low variability of the training sample (present climate). In both regions, model output statistics (MOS) methods have achieved the best results for temperature. In Central America, TF methods have achieved better results than MOS methods in the evaluation in the present climate, but they do not preserve trends in the future. For precipitation, MOS methods and the extreme gradient boost machine learning method have achieved the best results in both regions. In addition, it has been found that, although the use of humidity indices as predictors improves results for the downscaling of precipitation, future trends given by statistical methods are very sensitive to the use of one or another index. Three indices have been compared: relative humidity, specific humidity, and dewpoint depression. The use of the specific humidity has been found to lead to trends given by the downscaled projections that deviate seriously from those given by raw global climate models in both regions

Development of an empirical model for seasonal forecasting over the Mediterranean

Número monográfico dedicado al "18th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2018"In the frame of MEDSCOPE project, which mainly aims at improving predictability on seasonal timescales over the Mediterranean area, a seasonal forecast empirical model making use of new predictors based on a collection of targeted sensitivity experiments is being developed. Here, a first version of the model is presented. This version is based on multiple linear regression, using global climate indices (mainly global teleconnection patterns and indices based on sea surface temperatures, as well as sea-ice and snow cover) as predictors. The model is implemented in a way that allows easy modifications to include new information from other predictors that will come as result of the ongoing sensitivity experiments within the project. Given the big extension of the region under study, its high complexity (both in terms of orography and landsea distribution) and its location, different sub regions are affected by different drivers at different times. The empirical model makes use of different sets of predictors for every season and every sub region. Starting from a collection of 25 global climate indices, a few predictors are selected for every season and every sub region, checking linear correlation between predictands (temperature and precipitation) and global indices up to one year in advance and using moving averages from two to six months. Special attention has also been payed to the selection of predictors in order to guaranty smooth transitions between neighbor sub regions and consecutive seasons. The model runs a three-month forecast every month with a one-month lead time.This research has been supported by MEDSCOPE project, cofunded by the European Comission as part of ERA4CS, an ERANET initiated by JPI Climate (grant agreement 690462.5)

Modelización regional de eventos extremos de cambio climático sobre la Península Ibérica: periodos secos y duración de las estaciones

Ponencia presentada en: XXXII Jornadas Científicas de la AME y el XIII Encuentro Hispano Luso de Meteorología celebrado en Alcobendas (Madrid), del 28 al 30 de mayo de 2012.Este trabajo forma parte del proyecto regional CLIMANCHA (2010-2013, POII100255-8836, Junta de Comunidades de Castilla–La Mancha). Los datos de los RCMs pertenecen a los proyectos europeos PRUDENCE (FP5, contrato EVK2-2000-00132) y ENSEMBLES (2004-2009, 6th EU Framework Programme, GOCE-CT-2003-505539) y la base de datos observacionales Spain02 al proyecto nacional ESCENA (2008-2011, Secretaria de Estado de Cambio Climático, Ministerio de Agricultura, Alimentación y Medio Ambiente, ref.200800050084365)