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Genomic full-length characterization of the HLA-DQB1*03:25:01 allele. This article is protected by copyright. All rights reserved.

The extended genomic sequence of HLA-DQB1*04:59N allele.

Characterization of two novel HLA class I null alleles.

Photovoltaic solar energy is becoming very important globally due the benefits of their use. Climate change is resulting in frequent climatic variations that have a direct effect on the energy production in photovoltaic installations, so their good management is essential. This can be a big problem, for example, in photovoltaic pumping systems where irrigated crops can be affected due to lack of water. In this work, a PREPOSOL (PREdiction of POwer in SOLar installations) model was developed in MATLAB® software, which allowed to predict the power generated in the photovoltaic installations up to 3 h in advance using Artificial Neural Networks (ANNs) in a Bayesian framework with Genetic Algorithms. Despite that the PREPOSOL model can be implemented for other activities with photovoltaic solar energy, in this case, it was applied to photovoltaic pumping systems. The results showed that the model estimated the generated power with a relative error (RE) and R2 of 8.10 and 0.9157, respectively. Moreover, a representative example concerning irrigation programming is presented, which allowed adequate management. The methodology was calibrated and validated in a high-power and complex photovoltaic pumping system in Albacete, Spain.

Six novel HLA alleles were characterized in the Spanish population.

In countries characterized by arid and semi-arid climates, a precise determination of soil moisture conditions on the field scale is critically important, especially in the first crop growth stages, to schedule irrigation and to avoid wasting water. The objective of this study was to apply the operative methodology that allowed surface soil moisture (SSM) content in a semi-arid environment to be estimated. SSM retrieval was carried out by combining two scattering models (IEM and WCM), supplied by backscattering coefficients at the VV polarization obtained from the C-band Synthetic Aperture Radar (SAR), a vegetation descriptor NDVI obtained from the optical sensor, among other essential parameters. The inversion of these models was performed by Neural Networks (NN). The combined models were calibrated by the Sentinel 1 and Sentinel 2 data collected on bare soil, and in cereal, pea and onion crop fields. To retrieve SSM, these scattering models need accurate measurements of the roughness surface parameters, standard deviation of the surface height (hrms) and correlation length (L). This work used a photogrammetric acquisition system carried on Unmanned Aerial Vehicles (UAV) to reconstruct digital surface models (DSM), which allowed these soil roughness parameters to be acquired in a large portion of the studied fields. The obtained results showed that the applied improved methodology effectively estimated SSM on bare and cultivated soils in the principal early growth stages. The bare soil experimentation yielded an R2 = 0.74 between the estimated and observed SSMs. For the cereal field, the relation between the estimated and measured SSMs yielded R2 = 0.71. In the experimental pea fields, the relation between the estimated and measured SSMs revealed R2 = 0.72 and 0.78, respectively, for peas 1 and peas 2. For the onion experimentation, the highest R2 equaled 0.5 in the principal growth stage (leaf development), but the crop R2 drastically decreased to 0.08 in the completed growth phase. The acquired results showed that the applied improved methodology proves to be an effective tool for estimating the SSM on bare and cultivated soils in the principal early growth stages.

Five new HLA class I alleles were characterized by next-generation sequencing.

Nowadays, irrigation is becoming an activity of precision, whereby it is necessary to combine information collected from various sources to manage resources in an optimal way. New management strategies, such as big data techniques, sensors, artificial intelligence, unmanned aerial vehicles (UAV) and new technologies in general, are becoming more relevant every day. Related to this, modeling techniques, both at the water distribution network and at farm level, will be essential to gather information from various sources and offer useful recommendations for decision-making processes. In this Special Issue, ten high-quality papers were selected that cover a wide range of issues that are relevant to the different aspects related to irrigation management: water source and distribution network, plot irrigation systems and crop water management.

In this study, a new procedure for calibrating on-demand irrigation network models was developed. This procedure used a new objective function called maximum data with a reasonable error (MDRE) for calibrating the network. It was compared with the two more commonly used objective functions in calibration procedures that are the simple least squares (SLS) and the maximum likelihood estimator for the heteroscedastic error case (HMLE). In order to carry out the calibration, a quasi-Newton optimization method was used having as variable the Hazen-Williams head losses coefficient (C). This procedure was applied to an on-demand irrigation network located in Tarazona de La Mancha (Albacete, Spain) where flow and pressure at hydrant level was measured. The calibration procedure using the MDRE objective function was applied considering all the pressure control points simultaneously and the obtained results were compared with the results of considering the pressure control points independently. Therefore, the effect of the location of the pressure control point was studied. Results showed that, when the proposed objective function was used, the root mean squared error (RMSE) comparing the measured and simulated data after calibration was lower than when the SLS or HMLE objective functions were used. The location of the pressure control points throughout the irrigation network could affect the results; therefore, it was more accurate to use all the control points simultaneously than independently in the calibration process.

Because of the increasing importance of on-demand irrigation systems, a support system for general use has been developed to aid in selecting and regulating pumping stations. This innovation will improve the balance between total costs (project and energy) and operation quality. The procedure first determines the maximum and minimum system head curves, followed by the evolution of demand curves to obtain the maximum discharge needed. Once this discharge is determined, it is possible to carry out the dimensioning and regulation of the pumping station. An easy tool to select the number of variable and fixed speed pumps has also been developed Excel and Visual Basic can be used. The results demonstrate the importance of selecting pumps that are best adapted to the system head curve. The minimum total cost solution has been obtained by using one variable-speed pump in conjunction with another operating at fixed speed.