Improving prediction intervals using measured solar power with a multi-objective approach

Prediction Intervals are pairs of lower and upper bounds on point forecasts and are useful to take into account the uncertainty on predictions. This article studies the influence of using measured solar power, available at prediction time, on the quality of prediction intervals. While previous studies have suggested that using measured variables can improve point forecasts, not much research has been done on the usefulness of that additional information, so that prediction intervals with less uncertainty can be obtained. With this aim, a multi-objective particle swarm optimization method was used to train neural networks whose outputs are the interval bounds. The inputs to the network used measured solar power in addition to hourly meteorological forecasts. This study was carried out on data from three different locations and for five forecast horizons, from 1 to 5 h. The results were compared with two benchmark methods (quantile regression and quantile regression forests). The Wilcoxon test was used to assess statistical significance. The results show that using measured power reduces the uncertainty associated to the prediction intervals, but mainly for the short forecasting horizonsThis work was funded by the Spanish Ministry of Science under contract ENE2014-56126-C2-2-R (AOPRIN-SOL project)

Site-Specific mRNA Cleavage for Selective and Quantitative Profiling of Alternative Splicing with Label-Free Optical Biosensors

Alternative splicing of mRNA precursors is a key process in gene regulation, contributing to the diversity of proteomes by the alternative selection of exonic sequences. Alterations in this mechanism are associated with most cancers, enhancing their proliferation and survival, and can be employed as cancer biomarkers. Label-free optical biosensors are ideal tools for the highly sensitive and label-free analysis of nucleic acids. However, their application for alternative splicing analysis has been hampered due to the formation of complex and intricate long-range base-pairing interactions which make the direct detection in mRNA isoforms difficult. To solve this bottleneck, we introduce a methodology for the generation of length-controlled RNA fragments from purified total RNA, which can be easily detected by the biosensor. The methodology seizes RNase H enzyme activity to degrade the upstream and downstream RNA segments flanking the target sequence upon hybridization to specific DNA oligos. It allows the fast and direct monitoring of Fas gene alternative splicing in real time, employing a surface plasmon resonance biosensor. We demonstrate the selective and specific detection of mRNA fragments in the pM-nM concentration range, reducing quantification errors and showing 81% accuracy when compared to RT-qPCR. The site-specific cleavage outperformed random RNA hydrolysis by increasing the detection accuracy by 20%, making this methodology particularly appropriate for label-free quantification of alternative splicing events in complex samples

Transcriptional and biochemical changes in mouse liver following exposure to a metal/drug cocktail. Attenuating effect of a selenium-enriched diet

Real-life pollution usually involves simultaneous co-exposure to different chemicals. Metals and drugs are frequently and abundantly released into the environment, where they interact and bioaccumulate. Few studies analyze potential interactions between metals and pharmaceuticals in these mixtures, although their joint effects cannot be inferred from their individual properties. We have previously demonstrated that the mixture (PC) of the metals Cd and Hg, the metalloid As and the pharmaceuticals diclofenac (DCF) and flumequine (FLQ) impairs hepatic proteostasis. To gain a deeper vision of how PC affects mouse liver homeostasis, we evaluated here the effects of PC exposure upon some biochemical and morphometric parameters, and on the transcriptional profiles of selected group of genes. We found that exposure to PC caused oxidative damage that exceeded the antioxidant capacity of cells. The excessive oxidative stress response resulted in an overabundance of reducing equivalents, which hindered the metabolism and transport of metabolites, including cholesterol and bile acids, between organs. These processes have been linked to metabolic and inflammatory disorders, cancer, and neurodegenerative diseases. Therefore, our findings suggest that unintended exposure to mixtures of environmental pollutants may underlie the etiology of many human diseases. Fortunately, we also found that a diet enriched with selenium mitigated the harmful effects of this combination of toxicants

Robust Federated Learning for execution time-based device model identification under label-flipping attack

The computing device deployment explosion experienced in recent years, motivated by the advances of technologies such as Internet-of-Things (IoT) and 5G, has led to a global scenario with increasing cybersecurity risks and threats. Among them, device spoofing and impersonation cyberattacks stand out due to their impact and, usually, low complexity required to be launched. To solve this issue, several solutions have emerged to identify device models and types based on the combination of behavioral fingerprinting and Machine/Deep Learning (ML/DL) techniques. However, these solutions are not appropriate for scenarios where data privacy and protection are a must, as they require data centralization for processing. In this context, newer approaches such as Federated Learning (FL) have not been fully explored yet, especially when malicious clients are present in the scenario setup. The present work analyzes and compares the device model identification performance of a centralized DL model with an FL one while using execution time-based events. For experimental purposes, a dataset containing execution-time features of 55 Raspberry Pis belonging to four different models has been collected and published. Using this dataset, the proposed solution achieved 0.9999 accuracy in both setups, centralized and federated, showing no performance decrease while preserving data privacy. Later, the impact of a label-flipping attack during the federated model training is evaluated using several aggregation mechanisms as countermeasures. Zeno and coordinate-wise median aggregation show the best performance, although their performance greatly degrades when the percentage of fully malicious clients (all training samples poisoned) grows over 50%

Sequential extraction of almond hull biomass with pulsed electric fields (PEF) and supercritical CO2 for the recovery of lipids, carbohydrates and antioxidants

This work reports the first example of combined sequential extraction by pulsed electric fields (PEF) (3 kV/cm, 100 kJ/kg, 2 Hz, 100 ms) and supercritical (SC) fluid extraction (SFE) (15 MPa, 25 mL/min, 50ºC, 60 min) with CO2 (SC-CO2) for the valorisation of almond hull (AH) biomass. PEF+SFE boosted the efficiency of the protocol up to 77% for total antioxidant capacity and 20% in terms of polyphenols recovery compared to the traditional soaking. Triple-TOF-LC-MS-MS analysis provided the phenolic profiles for the PEF and SC-CO2 extracts, observing significant differences in the polyphenol profile according to the technology applied. Additionally, NMR analysis detected the presence of the carbohydrate soluble (mainly glucose, fructose and sucrose) and lipidic fractions, both selectively extracted by PEF or SC-CO2, respectively. Finally, the post-extraction residual solid biomass was characterized by several techniques such as TGA, FT-IR and SEM. For the latter, the formation of surface pores after PEF and a high fibre compaction after SFE was observed. On the other hand, DTG curves allowed to firmly propose concurrent valorisation routes for this solid, in agreement with a zero-waste approach

Valorization of wastewater from table olives: NMR identification of antioxidant phenolic fraction and microwave single-phase reaction of sugary fraction

The table olive industry is producing a huge amount of wastewater, which is a post-processing cost and an environmental concern. The present study aims to valorize this processing by-product to obtain a value-added product, thereby enhancing resource efficiency and contributing to achieving sustainable development goals (SDGs). In this sense, a chemical reaction-based platform was developed to obtain valuable components, such as levulinic acid (LA) and 5-hydromethylfurfural (HMF). The products were then analyzed using NMR identification of the antioxidant phenolic fraction and microwave single-phase reaction of the sugary fraction. According to the results, the highest concentration of phenolic compounds does not correspond to the sample directly obtained from NaOH treatment (S1), indicating that water washing steps (S2-S5) are fundamental to recover phenolic substances. Moreover, glucose was presented in the sugary fraction that can be transformed into levulinic acid by a single-phase reaction under microwave irradiation. The information provided in this manuscript suggests that the wastewater from the olive processing industry can be valorized to obtain valuable products

Biodegradación de cianuro por Pseudomonas pseudoalcaligenes CECT5344. Análisis proteómico

El cianuro es un compuesto muy tóxico para los seres vivos ya que forma complejos muy estables con metales de transición, esenciales para la función de numerosas proteínas. A pesar de su toxicidad, se han descrito microorganismos que metabolizan cianuro debido a la posesión de estas características: una ruta para su degradación, un sistema de suministro de hierro (sideróforos) y una cadena de transporte de electrones insensible a cianuro. A partir de lodos procedentes del río Guadalquivir se ha aislado una bacteria que degrada cianuro en condiciones alcalinas, se ha identificado como Pseudomonas pseudoalcaligenes y se ha depositado en la colección española de cultivos tipo como CECT5344. El estudio del proteoma de P. pseudoalcaligenes ha permitido la identificación de proteínas relacionadas con resistencia a cianuro, biosíntesis de sideróforos y respuesta a estrés general. Otros estudios permitirán evaluar el uso potencial de la estirpe CECT5344 en la descontaminación de residuos y de zonas contaminadas con cianur

Table olive wastewater as a potential source of biophenols for valorization : a mini review

The table olive industry generates high amounts of wastewater annually during the alkaline treatment, fermentation, and washing steps of olives. High conductivity and salt content, as well as the high organic and biophenol contents of these waters, is a worldwide problem, especially in the Mediterranean region, which is the major table olive producing area. There is a wide variety of bioactives found in wastewater derived from table olive processing. The main compounds of table olive wastewater, such as those derived from phenolic, hydrocarbon, and sugar fractions, can be recovered and reused. In this review, the table olive manufacturing processes and the volumes and composition of wastewater generated from the different methods of table olive processing are discussed. In addition, biophenols of table olive water and their biological activities are also introduced. The high concentrations of valuable biophenols, such as tyrosol and hydroxytyrosol, show promising potential for valorizing table olive wastewater; however, more research is needed in this area