El gobierno de Sancho : (estudio filosófico-crítico)

Precede al tít.: III centenario de la aparición del QuijoteCopia digital. Valladolid : Junta de Castilla y León. Consejería de Cultura y Turismo, 2009-201

Antithrombotic Therapy After Transcatheter Aortic Valve Implantation

The development of transcatheter aortic valve implantation has represented one of the greatest advances in the cardiology field in recent years and has changed clinical practice for patients with aortic stenosis. Despite the continuous improvement in operators' experience and techniques, and the development of new generation devices, thromboembolic and bleeding complications after transcatheter aortic valve implantation remain frequent, and are a major concern due to their negative impact on prognosis in this vulnerable population. In addition, the optimal antithrombotic regimen in this scenario is not known, and current recommendations are mostly empirical and not evidence based. The present review aims to provide an overview of the current status of knowledge, including relevant on-going randomised trials, on antithrombotic treatment strategies after transcatheter aortic valve implantation

Machine learning approaches over ion mobility spectra for the discrimination of ignitable liquids residues from interfering substrates

In arson fires, ignitable liquids (ILs) are frequently used to start combustion. For this reason, detecting IL residues (ILRs) at the fire scene is a key factor in fire investigation to determine whether a crime has been committed as well as to establish the modus operandi of the perpetrator. In the present study, the application of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) for the detection of ILRs in fire debris from complex matrices in combination with machine learning (ML) tools is proposed as an alternative to the traditional method, based on gas chromatography–mass spectrometry (GC-MS), described by the ASTM E1618 standard method. For this purpose, petroleum-derived substrates (vinyl, nylon, and polyester) and natural substrates (cotton, cork and linoleum) burned alone and with different ILs (gasoline, diesel, ethanol and charcoal starter with kerosene) were used. In addition, samples were taken at different times (0, 1, 6, 12, 24 and 48 h) after the fire was finished. The ion mobility sum spectrum (IMSS) of each sample was obtained and different ML algorithms were applied. The first derivative was performed at the IMSS, as well as a Savitzky-Golay filter. Hierarchical cluster analysis (HCA) revealed a clustering trend as a function of substrate and ILs used, where the studied sampling times did not affect the resulting clusters. The classification models for the detection of the presence of ILRs have high performance with an accuracy of 100% for support vector machines (SVM) and random forest model (RF), followed by linear discriminant analysis (LDA) with an accuracy of 86.67%. When discriminating the type of ILs used, the RF model obtained an accuracy of 100%, followed by the LDA with 97.22% and finally the SVM model with an accuracy of 93.06%. In addition, a simple web application has been developed where the trained models can be used, so any researcher can apply the method to detect ILRs in fire debris

Discrimination of Ignitable Liquid Residues in Burned Petroleum-Derived Substrates by Using HS-MS eNose and Chemometrics

Interpretation of data from fire debris is considered as one of the most challenging steps in fire investigation. Forensic analysts are tasked to identify the presence or absence of ignitable liquid residues (ILRs) which may indicate whether a fire was started deliberately. So far, data analysis is subjected to human interpretation following the American Society for Testing and Materials' guidelines (ASTM E1618) based on gas chromatography-mass spectrometry data. However, different factors such as interfering pyrolysis compounds may hinder the interpretation of data. Some substrates release compounds that are in the range of common ignitable liquids, which interferes with accurate determination of ILRs. The aim of the current research is to investigate whether headspace-mass spectroscopy electronic nose (HS-MS eNose) combined with pattern recognition can be used to classify different ILRs from fire debris samples that contain a complex matrix (petroleum-based substrates or synthetic fibers carpet) that can strongly interfere with their identification. Six different substrates-four petroleum-derived substrates (vinyl, linoleum, polyester, and polyamide carpet), as well as two different materials for comparison purposes (cotton and cork) were used to investigate background interferences. Gasoline, diesel, ethanol, and charcoal starter with kerosene were used as ignitable liquids. In addition, fire debris samples were taken after different elapsed times. A total of 360 fire debris samples were analyzed. The obtained total ion mass spectrum was combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) as well as supervised linear discriminant analysis (LDA). The results from HCA show a strong tendency to group the samples according to the ILs and substrate used, and LDA allowed for a full identification and discrimination of every ILR regardless of the substrate.Consejería de Economía, Conocimiento, Empresas y Universidad. Junta de Andalucía; 2014-2020 ERDF Operational Programm

Characterization of Biodegraded Ignitable Liquids by Headspace-Ion Mobility Spectrometry

The detection of ignitable liquids (ILs) can be crucial when it comes to determining arson cases. Such identification of ILs is a challenging task that may be affected by a number of factors. Microbial degradation is considered one of three major processes that can alter the composition of IL residues. Since biodegradation is a time related phenomenon, it should be studied at different stages of development. This article presents a method based on ion mobility spectroscopy (IMS) which has been used as an electronic nose. In particular, ion mobility sum spectrum (IMSS) in combination with chemometric techniques (hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA)) has been applied for the characterization of different biodegraded ILs. This method intends to use IMSS to identify a range of ILs regardless of their degree of biodegradation. Three ILs (diesel, gasoline and kerosene) from three different commercial brands were evaluated after remaining in a soil substrate for several lengths of time (0, 2, 5, 13 and 38 days). The HCA results showed the samples' trend to fall into categories characterized by ILs type and biodegradation time. The LDAs allowed a 99% successful classification of the samples according to the IL type. This is the first time that an HS-IMS technique has been used to detect ILs that have undergone biodegradation processes. The results show that IMS may be a promising alternative to the current standard method based on gas-chromatography for the analysis of biodegraded ILs. Furthermore, no pretreatment of the samples nor the use of a solvent is required

Immunohistological study of the unexplored vomeronasal organ of an endangered mammal, the dama gazelle (Nanger dama)

Dama gazelle is a threatened and rarely studied species found primarily in northern Africa. Human pressure has depleted the dama gazelle population from tens of thousands to a few hundred individuals. Since 1970, a founder population consisting of the last 17 surviving individuals in Western Sahara has been maintained in captivity, reproducing naturally. In preparation for the future implementation of assisted reproductive technology, certain aspects of dama gazelle reproductive biology have been established. However, the role played by semiochemical-mediated communications in the sexual behavior of dama gazelle remains unknown due partially to a lack of a neuroanatomical or morphofunctional characterization of the dama gazelle vomeronasal organ (VNO), which is the sensory organ responsible for pheromone processing. The present study characterized the dama gazelle VNO, which appears fully equipped to perform neurosensory functions, contributing to current understanding of interspecies VNO variability among ruminants. By employing histological, lectin-histochemical, and immunohistochemical techniques, we conducted a detailed morphofunctional evaluation of the dama gazelle VNO along its entire longitudinal axis. Our findings of significant structural and neurochemical transformation along the entire VNO suggest that future studies of the VNO should take a similar approach. The present study contributes to current understanding of dama gazelle VNO, providing a basis for future studies of semiochemical-mediated communications and reproductive management in this speciesThis work was partially supported by a grant from “Consello Social Universidade de Santiago de Compostela” 2022-PU004S

Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data

Fruit juice production is one of the most important sectors in the beverage industry, and its adulteration by adding cheaper juices is very common. This study presents a methodology based on the combination of machine learning models and near-infrared spectroscopy for the detection and quantification of juice-to-juice adulteration. We evaluated 100% squeezed apple, pineapple, and orange juices, which were adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The spectroscopic data have been combined with different machine learning tools to develop predictive models for the control of the juice quality. The use of non-supervised techniques, specifically model-based clustering, revealed a grouping trend of the samples depending on the type of juice. The use of supervised techniques such as random forest and linear discriminant analysis models has allowed for the detection of the adulterated samples with an accuracy of 98% in the test set. In addition, a Boruta algorithm was applied which selected 89 variables as significant for adulterant quantification, and support vector regression achieved a regression coefficient of 0.989 and a root mean squared error of 1.683 in the test set. These results show the suitability of the machine learning tools combined with spectroscopic data as a screening method for the quality control of fruit juices. In addition, a prototype application has been developed to share the models with other users and facilitate the detection and quantification of adulteration in juices

Application of a Combined Adsorption−Ozonation Process for Phenolic Wastewater Treatment in a Continuous Fixed-Bed Reactor

This work studied the removal of phenol from industrial effluents through catalytic ozonation in the presence of granular activated carbon in a continuous fixed-bed reactor. Phenol was chosen as model pollutant because of its environmental impact and high toxicity. Based on the evolution of total organic carbon (TOC) and phenol concentration, a kinetic model was proposed to study the effect of the operational variables on the combined adsorption–oxidation (Ad/Ox) process. The proposed three-phase model expressed the oxidation phenomena in the liquid and the adsorption and oxidation on the surface of the granular activated carbon in the form of two kinetic constants, k1 and k2 respectively. The interpretation of the constants allow to study the benefits and behaviour of the use of activated carbon during the ozonisation process under different conditions affecting adsorption, oxidation, and mass transfer. Additionally, the calculated kinetic parameters helped to explain the observed changes in treatment efficiency. The results showed that phenol would be completely removed at an effective contact time of 3.71 min, operating at an alkaline pH of 11.0 and an ozone gas concentration of 19.0 mg L−1. Under these conditions, a 97.0% decrease in the initial total organic carbon was observed.The authors are grateful to the University of the Basque Country for their financial support of this study through the GIU20/56 project and C. Ferreiro’s predoctoral PIF grant (PIF16/367)

Detection of adulterations in fruit juices using machine learning methods over FT-IR spectroscopic data

Fruit juices are one of the most adulterated beverages, usually because of the addition of water, sugars, or less expensive fruit juices. This study presents a method based on Fourier transform infrared spectroscopy (FT-IR), in combination with machine learning methods, for the correct identification and quantification of adulterants in juices. Thus, three types of 100% squeezed juices (pineapple, orange, and apple) were evaluated and adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The results of the exploratory data analysis revealed a clear clustering trend of the samples according to the type of juice analyzed. The supervised learning analysis, based on the development of models for the detection of adulteration, obtained significant results for all tested methods (i.e., support-vector machines or SVM), random forest or RF, and linear discriminant analysis or LDA) with an accuracy above 97% on the test set. Regarding quantification, the best results are obtained with the support vector regression and with partial least square regression showing an R2 greater than 0.99 and a root mean square error (RMSE) less than 1.4 for the test setPeer ReviewedPostprint (published version

Colour Changes during the Carbamazepine Oxidation by Photo-Fenton

The oxidation of aqueous solutions of carbamazepine is conducted using the Fenton reagent, combined with the photolytic action of a 150 W medium pressure UV lamp, operating at T = 40 °C. The effect of acidity is analysed at an interval pH = 2.0–5.0, verifying that operating at pH = 5.0 promotes colour formation (Colour = 0.15 AU). The effect of iron is studied, finding that the colour of the water increases in a linear way, Colour = 0.05 + 0.0075 [Fe]0. The oxidising action of hydrogen peroxide is tested, confirming that when operating with [H2O2]0 = 2.0 mM, the maximum colour is generated (Colourmax = 0.381 AU). The tint would be generated by the degradation of by-products of carbamazepine, which have chromophoric groups in their internal structure, such as oxo and dioxocarbazepines, which would produce tint along the first minutes of oxidation, while the formation of acridones would slowly induce colour in the water.Authors are grateful to the University of the Basque Country UPV/EHU the financial support to carry out this research study through the scholarship Student Movility for Traineeships in the Erasmus + Programme between the Anadolu University in Eskisehir (Turkey) and the Faculty of Engineering Vitoria-Gasteiz (Spain), and the research Project PPGA20/33