Optimization of a Laboratory Rainfall Simulator to Be Representative of Natural Rainfall

[EN] The importance of understanding the effects of rainfall on different materials over time makes it essential to carry out controlled tests to reduce analysis time. Rainfall simulators have been in use for decades and have been implemented as technology and knowledge of the physical behavior of water advanced. There are two main types of rainfall simulators: gravity simulators and pressure simulators. In the former, the drop velocity is normally smaller than the terminal velocity reached by natural droplets; in the latter, the drop size is too small to be representative and has far more speed than the natural speed for those sizes. To solve this problem, a simulator has been developed where the terminal velocity of the raindrops is reached and the drop size can be varied by different nozzles of variable sizes, adapting it to the conditions of a given region. In this study, conditions similar to the rainfall conditions of the city of León have been achieved. This paper presents the design of a rainfall simulator that recreates different rainfall conditions and rainwater composition and its calibration process

Dyslexia Diagnosis by EEG Temporal and Spectral Descriptors: An Anomaly Detection Approach.

Diagnosis of learning difficulties is a challenging goal. There are huge number of factors involved in the evaluation procedure that present high variance among the population with the same difficulty. Diagnosis is usually performed by scoring subjects according to results obtained in different neuropsychological (performance-based) tests specifically designed to this end. One of the most frequent disorders is developmental dyslexia (DD), a specific difficulty in the acquisition of reading skills not related to mental age or inadequate schooling. Its prevalence is estimated between 5% and 12% of the population. Traditional tests for DD diagnosis aim to measure different behavioral variables involved in the reading process. In this paper, we propose a diagnostic method not based on behavioral variables but on involuntary neurophysiological responses to different auditory stimuli. The experiments performed use electroencephalography (EEG) signals to analyze the temporal behavior and the spectral content of the signal acquired from each electrode to extract relevant (temporal and spectral) features. Moreover, the relationship of the features extracted among electrodes allows to infer a connectivity-like model showing brain areas that process auditory stimuli in a synchronized way. Then an anomaly detection system based on the reconstruction residuals of an autoencoder using these features has been proposed. Hence, classification is performed by the proposed system based on the differences in the resulting connectivity models that have demonstrated to be a useful tool for differential diagnosis of DD as well as a method to step towards gaining a better knowledge of the brain processes involved in DD.This work was partly supported by the MINECO/FEDER under PGC2018-098813-B-C31, PGC2018-098813-B-C32 and PSI2015-65848-R projects. We gratefully acknowledge the support of NVIDIA Corporation with the donation of one of the GPUs used for this research. Work by F.J.M.M. was supported by the MICINN “Juan de la Cierva - Formaci´on” Fellowship. We also thank the Leeduca research group and Junta de Andaluc´ıa for the data supplied and the support

A Method of Pruning and Random Replacing of Known Values for Comparing Missing Data Imputation Models for Incomplete Air Quality Time Series

[EN] The data obtained from air quality monitoring stations, which are used to carry out studies using data mining techniques, present the problem of missing values. This paper describes a research work on missing data imputation. Among the most common methods, the method that best imputes values to the available data set is analysed. It uses an algorithm that randomly replaces all known values in a dataset once with imputed values and compares them with the actual known values, forming several subsets. Data from seven stations in the Silesian region (Poland) were analyzed for hourly concentrations of four pollutants: nitrogen dioxide (NO2), nitrogen oxides (NOx), particles of 10 μm or less (PM10) and sulphur dioxide (SO2) for five years. Imputations were performed using linear imputation (LI), predictive mean matching (PMM), random forest (RF), k-nearest neighbours (k-NN) and imputation by Kalman smoothing on structural time series (Kalman) methods and performance evaluations were performed. Once the comparison method was validated, it was determine that, in general, Kalman structural smoothing and the linear imputation methods best fitted the imputed values to the data pattern. It was observed that each imputation method behaves in an analogous way for the different stations The variables with the best results are NO2 and SO2. The UMI method is the worst imputer for missing values in the data sets.S

Comportamiento de patrones de cerezo en las condiciones edafoclimáticas de la Región de Murcia

En este trabajo se expone la influencia que ejercen los patrones Adara, Mariana 2624, Mayor, MaxMa 14, Santa Lucia GF 64, Gisela 5, Gisela 6, Piku 1, Piku 3 y Piku4 en el crecimiento vegetativo, producción y calidad del fruto de la variedad de cerezo NewStar. Este ensayo se ha realizado en un suelo pesado, calcáreo y con alto contenido en arcilla ubicado en el término municipal de Jumilla. Se han encontrado diferencias significativas para parámetros como el vigor, producción, tamaño del fruto, contenido en solidos solubles y firmeza. Las mayores producciones acumuladas fueron para patrones vigorosos como son Mariana 2624, Mayor y Adara. Los patrones de menor vigor como Gisela 5 y Piku 1, presentaron una tendencia excesiva al enanismo. Aquellos patrones peor adaptados a las condiciones edafoclimáticas del ensayo como Gisela 5, Gisela 6, SL 64, Mayor y Piku 1 presentaron mayor porcentaje de mortandad.A los componentes del Grupo Cerezo I+D. Este trabajo forma parte del proyecto INIA RTA:2006‐ 00057‐00‐00 y ha sido cofinanciado por el proyecto PO07‐027. Este trabajo ha sido realizado en el marco de la Acción Cost FA 1104

NKG2D-CAR-transduced natural killer cells efficiently target multiple myeloma

CAR-T-cell therapy against MM currently shows promising results, but usually with serious toxicities. CAR-NK cells may exert less toxicity when redirected against resistant myeloma cells. CARs can be designed through the use of receptors, such as NKG2D, which recognizes a wide range of ligands to provide broad target specificity. Here, we test this approach by analyzing the antitumor activity of activated and expanded NK cells (NKAE) and CD45RA− T cells from MM patients that were engineered to express an NKG2D-based CAR. NKAE cells were cultured with irradiated Clone9.mbIL21 cells. Then, cells were transduced with an NKG2D-4-1BB-CD3z-CAR. CAR-NKAE cells exhibited no evidence of genetic abnormalities. Although memory T cells were more stably transduced, CAR-NKAE cells exhibited greater in vitro cytotoxicity against MM cells, while showing minimal activity against healthy cells. In vivo, CAR-NKAE cells mediated highly efficient abrogation of MM growth, and 25% of the treated mice remained disease free. Overall, these results demonstrate that it is feasible to modify autologous NKAE cells from MM patients to safely express a NKG2D-CAR. Additionally, autologous CAR-NKAE cells display enhanced antimyeloma activity demonstrating that they could be an effective strategy against MM supporting the development of NKG2D-CAR-NK-cell therapy for MM.This study was supported by a grant from the Spanish Society for Hematology and Hemotherapy to Alejandra Leivas, the CRIS Foundation to Beat Cancer and the Instituto de Salud Carlos III (PI18/01519)

Deliverable D3: Global climatic features over the next million years and recommendation for specific situations to be considered. Work Package 2, Simulation of the future evolution of the biosphere system using the hierarchical strategy. Modelling Sequential Biosphere Systems under Climate Change for Radioactive Waste Disposal (BIOCLIM)

The BIOCLIM project aims at assessing the possible long-term impacts of climate change on the safety of waste repositories in deep formations using climate simulations of the long-term climate in various European areas. One of the objectives of the project is to develop two strategies for representing sequential climatic changes to the geosphere-biosphere system for different sites over Europe, addressing the time scale of one million years. The results of this work will be interpreted in terms of global or regional changes of climate and of vegetation. The first strategy (hierarchical strategy) will use the full hierarchy of existing climate models (a climate model is a numerical simplified representation of the climate system behaviour and evolution). Simple models (LLN 2-D NH and threshold models; see the description here after) will simulate the overall long-term evolution of the global climate. Their results will then be used as inputs to more complex models (LMD climate models possibly coupled with vegetation models, either SECHIBA or ORCHIDE) and finally climate and vegetation cover will be determined for specific sites at specific times. A second strategy (integrated strategy) will consist in building an integrated climate model, which represents most of the physical mechanisms for studying long-term climatic variations. The results will then be interpreted on a regional scale. This deliverable is the first step of the hierarchical strategy. The purpose of this deliverable is to identify and justify some specific climatic situations amongst different long-term simulations that are of interest for assessing the safety of radioactive waste repository sites and that will be further studied with GCMs (General Circulation Model)

Deliverable D2:Consolidation of needs of the european wasten management agencies and the regulator of the consortium: Work Package 1, Site-specific and palaeo environmental data. Modelling sequential biosphere systems under climate change for radioactive waste disposal. (BIOCLIM)

The nature of long-lived radioactive wastes is that they present a radiological hazard over a period of time that is extremely long compared with the timescale over which the engineered protection systems and institutional management of a disposal, or long-term storage, facility can be guaranteed. Safety assessments for potential deep repositories need to be able to provide indicators of safety performance over time periods of hundreds of thousands of years. On such timescales, it is generally assumed that radionuclides may be slowly released from the containment system, migrating via geosphere pathways until they reach the accessible environment. Hence, there is a need to study the evolution of the environment external to the disposal system and the ways in which this might impact on its long-term radiological safety performance, for example in terms of influences on the migration and accumulation of radionuclides

Deliverable D4/5: Global climatic characteristics, including vegetation and seasonal cycles over Europe, for snapshots over the next 200,000 years. Work Package 2, Simulation of the future evolution of the biosphere system using the hierarchical strategy. Modelling Sequential Biosphere Systems under Climate Change for Radioactive Waste Disposal (BIOCLIM)

The aim of the BIOCLIM project is to develop and present techniques that can be used to develop self-consistent patterns of possible future climate changes over the next million years (climate scenarios), and to demonstrate how these climate scenarios can be used in assessments of the long-term safety of nuclear waste repository sites. Within the project, two strategies are implemented to predict climate change. The first is the hierarchical strategy, in which a hierarchy of climate models is used to investigate the evolution of climate over the period of interest. These models vary from very simple 2-D and threshold models, which simulate interactions between only a few aspects of the earth system, through general circulation models (GCMs) and vegetation models, which simulate in great detail the dynamics and physics of the atmosphere, ocean, and biosphere, to regional models, which focus in particular on the European region and the specific areas of interest. The second strategy is the integrated strategy, in which intermediate complexity climate models are developed, and used to consecutively simulate the development of the earth system over many millennia. Although these models are relatively simple compared to a GCM, they are more advanced than 2D models, and do include physical descriptions of the biosphere, cryosphere, atmosphere and ocean. This deliverable, D4/5, focuses on the hierarchical strategy, and in particular the GCM and vegetation model simulation of possible future climates. Deliverable D3 documented the first step in this strategy. The Louvain-la-Neuve 2-D climate model (LLN-2D) was used to estimate (among other variables) annual mean temperatures and ice volume in the Northern Hemisphere over the next 1 million years. It was driven by the calculated evolution of orbital parameters, and plausible scenarios of CO2 concentration. From the results, 3 future time periods within the next 200,000 years were identified as being extreme, that is either significantly warmer or cooler than the present. The next stage in the hierarchical strategy was to use a GCM and biosphere model, to simulate in more detail these extreme time periods

Diagnosis of multiple sclerosis using multifocal ERG data feature fusion

The purpose of this paper is to implement a computer-aided diagnosis (CAD) system for multiple sclerosis (MS) based on analysing the outer retina as assessed by multifocal electroretinograms (mfERGs). MfERG recordings taken with the RETI?port/scan 21 (Roland Consult) device from 15 eyes of patients diagnosed with incipient relapsing-remitting MS and without prior optic neuritis, and from 6 eyes of control subjects, are selected. The mfERG recordings are grouped (whole macular visual field, five rings, and four quadrants). For each group, the correlation with a normative database of adaptively filtered signals, based on empirical model decomposition (EMD) and three features from the continuous wavelet transform (CWT) domain, are obtained. Of the initial 40 features, the 4 most relevant are selected in two stages: a) using a filter method and b) using a wrapper-feature selection method. The Support Vector Machine (SVM) is used as a classifier. With the optimal CAD configuration, a Matthews correlation coefficient value of 0.89 (accuracy = 0.95, specificity = 1.0 and sensitivity = 0.93) is obtained. This study identified an outer retina dysfunction in patients with recent MS by analysing the outer retina responses in the mfERG and employing an SVM as a classifier. In conclusion, a promising new electrophysiological-biomarker method based on feature fusion for MS diagnosis was identified.Agencia Estatal de InvestigaciónInstituto de Salud Carlos II

Deliverable D6a: Regional climatic characteristics for the European sites at specific times: the dynamical downscaling. Work Package 2, Simulation of the future evolution of the biosphere system using the hierarchical strategy. Modelling Sequential Biosphere Systems under Climate Change for Radioactive Waste Disposal (BIOCLIM)

The overall aim of BIOCLIM is to assess the possible long-term impacts due to climate change on the safety of radioactive waste repositories in deep formations. This aim is addressed through the following specific objectives: • Development of practical and innovative strategies for representing sequential climatic changes to the geosphere-biosphere system for existing sites over central Europe, addressing the timescale of one million years, which is relevant to the geological disposal of radioactive waste. • Exploration and evaluation of the potential effects of climate change on the nature of the biosphere systems used to assess the environmental impact. • Dissemination of information on the new methodologies and the results obtained from the project among the international waste management community for use in performance assessments of potential or planned radioactive waste repositories. The BIOCLIM project is designed to advance the state-of-the-art of biosphere modelling for use in Performance Assessments. Therefore, two strategies are developed for representing sequential climatic changes to geosphere-biosphere systems. The hierarchical strategy successively uses a hierarchy of climate models. These models vary from simple 2-D models, which simulate interactions between a few aspects of the Earth system at a rough surface resolution, through General Circulation Model (GCM) and vegetation model, which simulate in great detail the dynamics and physics of the atmosphere, ocean and biosphere, to regional models, which focus on the European regions and sites of interest. Moreover, rule-based and statistical downscaling procedures are also considered. Comparisons are provided in terms of climate and vegetation cover at the selected times and for the study regions. The integrated strategy consists of using integrated climate models, representing all the physical mechanisms important for long-term continuous climate variations, to simulate the climate evolution over many millennia. These results are then interpreted in terms of regional climatic changes using rule-based and statistical downscaling approaches. This deliverable, D6a, focuses on the hierarchical strategy, and in particular the MAR simulations. According to the hierarchical strategy developed in the BIOCLIM project to predict future climate, six BIOCLIM experiments were run with the MAR model. In addition to these experiments a baseline experiment, presenting the present-day climate simulated by MAR, was also undertaken. In the first step of the hierarchical strategy the LLN 2-D NH climate model simulated the gross features of the climate of the next 1 Myr [Ref.1]. Six snapshot experiments were selected from these results. In a second step a GCM and a biosphere model were used to simulate in more detail the climate of the selected time periods. These simulations were performed on a global scale [Ref.1]. The third step of the procedure is to derive the regional features of the climate at the same time periods. Therefore the results of the GCM are used as boundary conditions to force the regional climate model (MAR) for the six selected periods and the baseline simulation. The control simulation (baseline) corresponds to the regional climate simulated under present-day conditions, both insolation forcing and atmospheric CO2 concentration. All the BIOCLIM simulations are compared to that baseline simulation. In addition, other comparisons will also be presented. Tableau 1 summarises the characteristics of these BIOCLIM experiments already presented in [Ref.1] and [Ref.2]