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Abstract Harvesting renewable mechanical energy is envisioned as a promising and sustainable way for power generation. Many recent mechanical energy harvesters are able to produce instantaneous (pulsed) electricity with a high peak voltage of over 100 V. However, directly storing such irregular high‐voltage pulse electricity remains a great challenge. The use of extra power management components can boost storage efficiency but increase system complexity. Here utilizing the conducting polymer PEDOT:PSS, high‐rate metal‐free micro‐supercapacitor (MSC) arrays are successfully fabricated for direct high‐efficiency storage of high‐voltage pulse electricity. Within an area of 2.4 × 3.4 cm2 on various paper substrates, large‐scale MSC arrays (comprising up to 100 cells) can be printed to deliver a working voltage window of 160 V at an ultrahigh scan rate up to 30 V s−1. The ultrahigh rate capability enables the MSC arrays to quickly capture and efficiently store the high‐voltage (≈150 V) pulse electricity produced by a droplet‐based electricity generator at a high efficiency of 62%, significantly higher than that (

The development of specific antiviral therapies targeting SARS-CoV-2 remains fundamental because of the continued high incidence of COVID-19 and limited accessibility to antivirals in some countries. In this context, dark chemical matter (DCM), a set of drug-like compounds with outstanding selectivity profiles that have never shown bioactivity despite being extensively assayed, appears to be an excellent starting point for drug development. Accordingly, in this study, we performed a high-throughput screening to identify inhibitors of the SARS-CoV-2 main protease (Mpro) using DCM compounds as ligands. Multiple receptors and two different docking scoring functions were employed to identify the best molecular docking poses. The selected structures were subjected to extensive conventional and Gaussian accelerated molecular dynamics. From the results, four compounds with the best molecular behavior and binding energy were selected for experimental testing, one of which presented inhibitory activity with a Ki value of 48 ± 5 μM. Through virtual screening, we identified a significant starting point for drug development, shedding new light on DCM compounds.

Abstract Intensive in-store use of smartphones has driven ethically questionable behaviors with significant economic repercussions for the survival of brick-and-mortar retailers. The mobile-assisted showroomer’s dilemma refers to the dilemma such shoppers experience at the moment of decision in a brick-and-mortar store, when they are holding an item in their hands, check their phone, and hesitate between buying it (a) at the physical store (loyal behavior, LB) or (b) through a cheaper online retailer (competitive behavior, CB). Using the theoretical framework of the Composite MES, this research proves that the dilemma exists: in a sample of 648 mobile-assisted showroomers, 44.91% would engage in CB vs. 55.09% in LB. Furthermore, 50.6% of the CB is explained by two dimensions of ethical judgment: relativism and egoism. To prevent sales leakage at brick-and-mortar stores, ethical judgment must be considered. The greater the weight of the relativism dimension, the less predisposed customers are toward CB. The egoism dimension is positively associated with engaging in CB. LB is a major opportunity for independent brick-and-mortar retailers.

Over 750 million cases of COVID-19, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), have been reported since the onset of the global outbreak. The need for effective treatments has spurred intensive research for therapeutic agents based on pharmaceutical repositioning or natural products. In light of prior studies asserting the bioactivity of natural compounds of the autochthonous Peruvian flora, the present study focuses on the identification SARS-CoV-2 Mpro main protease dimer inhibitors. To this end, a target-based virtual screening was performed over a representative set of Peruvian flora-derived natural compounds. The best poses obtained from the ensemble molecular docking process were selected. These structures were subjected to extensive molecular dynamics steps for the computation of binding free energies along the trajectory and evaluation of the stability of the complexes. The compounds exhibiting the best free energy behaviors were selected for in vitro testing, confirming the inhibitory activity of Hyperoside against Mpro, with a Ki value lower than 20 µM, presumably through allosteric modulation.

The development of specific antiviral therapies targeting SARS-CoV-2 remains fundamental because of the continued high incidence of COVID-19 and limited accessibility to antivirals in some countries. In this context, dark chemical matter (DCM), a set of drug-like compounds with outstanding selectivity profiles that have never shown bioactivity despite being extensively assayed, appears to be an excellent starting point for drug development. Accordingly, in this study, we performed a high-throughput screening to identify inhibitors of the SARS-CoV-2 main protease (Mpro) using DCM compounds as ligands. Multiple receptors and two different docking scoring functions were employed to identify the best molecular docking poses. The selected structures were subjected to extensive conventional and Gaussian accelerated molecular dynamics. From the results, four compounds with the best molecular behavior and binding energy were selected for experimental testing, one of which presented inhibitory activity with a Ki value of 48 ± 5 μM. Through virtual screening, we identified a significant starting point for drug development, shedding new light on DCM compounds. [ABSTRACT FROM AUTHOR]

Este trabajo ha obtenido un accésit del Premio Estudios Financieros 2021 en la modalidad de Marketing y Publicidad. Desde la perspectiva tecnológica, la crisis covid-19 ha supuesto para el retail: (a) la aceleración de la transformación digital del comercio físico con la incorporación de tecnologías interactivas, como la realidad aumentada móvil (RAM), y (b) la transformación del shopping journey de sus clientes, en particular, los mobile-assisted showroomers (MAS). Con el objetivo de examinar de manera conjunta la tecnología del cliente MAS (smartphone) y la tecnología de la tienda (RAM), se propone y contrasta un modelo teórico cognitivo-afectivo-normativo sobre una muestra de 805 consumidores MAS que explique su intención de uso de la RAM in-store, considerando el papel moderador del género. Los resultados muestran que la «expectativa de rendimiento» se revela como el mayor antecedente para ambos grupos, aunque existe un efecto moderador del género, dada la mayor capacidad explicativa y predictiva del modelo de hombres MAS (R2 = 56,1 %) frente al modelo de mujeres MAS (R2 = 51,3 %). Sorprendentemente, el «esfuerzo esperado» es determinante en la valoración de los hombres MAS, cuando en la literatura ha sido relacionado con las mujeres. La «influencia social» se revela como el segundo factor en los antecedentes del segmento femenino. Estos resultados tienen implicaciones teóricas en el ámbito de la modelización e implicaciones prácticas para reactivar el sector del retail físico.

Cybersecurity is one of the great challenges of today’s world. Rapid technological development has allowed society to prosper and improve the quality of life and the world is more dependent on new technologies. Managing security risks quickly and effectively, preventing, identifying, or mitigating them is a great challenge. The appearance of new attacks, and with more frequency, requires a constant update of threat detection methods. Traditional signature-based techniques are effective for known attacks, but they are not able to detect a new attack. For this reason, intrusion detection systems (IDS) that apply machine learning (ML) techniques represent an alternative that is gaining importance today. In this work, we have analyzed different machine learning techniques to determine which ones permit to obtain the best traffic classification results based on classification performance measurements and execution times, which is decisive for further real-time deployments. The CICIDS2017 dataset was selected in this work since it contains bidirectional traffic flows (derived from traffic captures) that include benign traffic and different types of up-to-date attacks. Each traffic flow is characterized by a set of connection-related attributes that can be used to model the traffic and distinguish between attacks and normal flows. The CICIDS2017 also contains the raw network traffic captures collected during the dataset creation in a packet-based format, thus permitting to extract the traffic flows from them. Various classification techniques have been evaluated using the Weka software: naive Bayes, logistic, multilayer perceptron, sequential minimal optimization, k-nearest neighbors, adaptive boosting, OneR, J48, PART, and random forest. As a general result, methods based on decision trees (PART, J48, and random forest) have turned out to be the most efficient with F1 values above 0.999 (average obtained in the complete dataset). Moreover, multiclass classification (distinguishing between different types of attack) and binary classification (distinguishing only between normal traffic and attack) have been compared, and the effect of reducing the number of attributes using the correlation-based feature selection (CFS) technique has been evaluated. By reducing the complexity in binary classification, better results can be obtained, and by selecting a reduced set of the most relevant attributes, less time is required (above 30% of decrease in the time required to test the model) at the cost of a small performance loss. The tree-based techniques with CFS attribute selection (six attributes selected) reached F1 values above 0.990 in the complete dataset. Finally, a conventional tool like Zeek has been used to process the raw traffic captures to identify the traffic flows and to obtain a reduced set of attributes from these flows. The classification results obtained using tree-based techniques (with 14 Zeek-based attributes) were also very high, with F1 above 0.997 (average obtained in the complete dataset) and low execution times (allowing several hundred thousand flows/s to be processed). These classification results obtained on the CICIDS2017 dataset allow us to affirm that the tree-based machine learning techniques may be appropriate in the flow-based intrusion detection problem and that algorithms, such as PART or J48, may offer a faster alternative solution to the RF technique.

El objetivo de este trabajo es dar a conocer el origen y la trayectoria del equipo docente estructurado IKAsasun (edIKDit-18-2, equipo docente para la Enseñanza Multidisciplinar Biosanitaria). El equipo está compuesto por personal docente investigador adscrito a los Campus de Leioa (Facultad de Medicina y Enfermería) y de Vitoria-Gasteiz (Facultad de Farmacia) de la Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU). La docencia que se imparte comprende a distintas disciplinas de Grados mayoritariamente relacionados con las Ciencias de la Salud (Enfermería, Farmacia, Fisioterapia, Medicina, Nutrición Humana y Dietética, Odontología, Ciencia y Tecnología de los Alimentos y Ciencias Ambientales). Desde el inicio de la andadura como equipo docente en el año 2015, el grupo ha participado en dos Proyectos de Innovación Educativa (PIE) subvencionados por la UPV/EHU (PIE nº 40 y nº 96). Estos proyectos están dirigidos a ayudar al alumnado a desarrollar, de una manera eficaz, las competencias específicas y transversales propias de cada Grado y a conseguir que se involucren en su propio aprendizaje. En ambos proyectos se ha incidido en la importancia de trabajar en equipos multidisciplinares, apostando por la utilización de metodologías de aprendizaje activo. En el primero de ellos (PIE nº 40; 2015-2017), se planteó la participación activa del alumnado en la resolución de casos clínicos complejos que requieren un abordaje multidisciplinar para garantizar la asistencia integral al paciente. En el segundo proyecto (PIE nº 96; 2018-2020), se llevó a cabo un abordaje multidisciplinar de temas relacionados con la salud biomédica y científica desde distintos Grados, y posterior divulgación enfocada a diferentes sectores de la sociedad. Los resultados obtenidos tras el desarrollo de ambos proyectos han sido muy alentadores, y alumnado, profesorado y personas externas colaboradoras han valorado muy positivamente la experiencia, que ha quedado reflejada en varias publicaciones y comunicaciones a congresos.

Este trabajo se llevó a cabo durante el período del año 2016, de forma conjunta entre investigadores de la Universidad Católica de Salta, a través del Consejo de Investigaciones y el Ente Regulador del Juego de Azar de la Provincia de Salta, dentro del área del Programa de Prevención de la Adicción al Juego. La misma tiene como marco teórico de referencia el psicoanalítico de orientación lacaniana. Ésta describe, a partir de los datos recabados, asentados en un sistema informático especialmente diseñado para el 0800 del programa arriba citado, de 702 entrevistas telefónicas semidirigidas, con la finalidad de detectar las particularidades de las personas que tienen una adicción al juego, en relación a la edad promedio en que un sujeto inicia una adicción al juego y los motivos por los cuales supone lo llevaron a jugar. Al mismo tiempo, se realizó estudios de casos, tomados al azar.

Establishing the interactome of the cancer-associated stress protein Nuclear Protein 1 (NUPR1), we found that it binds to several hundreds of proteins, including proteins involved in nuclear translocation, DNA repair, and key factors of the SUMO pathway. We demonstrated that the NUPR1 inhibitor ZZW-115, an organic synthetic molecule, competes with importins for the binding to the NLS region of NUPR1, thereby inhibiting its nuclear translocation. We hypothesized, and then proved, that inhibition of NUPR1 by ZZW-115 sensitizes cancer cells to DNA damage induced by several genotoxic agents. Strikingly, we found that treatment with ZZW-115 reduced SUMOylation of several proteins involved in DNA damage response (DDR). We further report that the presence of recombinant NUPR1 improved the SUMOylation in a cell-free system, indicating that NUPR1 directly stimulates the SUMOylation machinery. We propose that ZZW-115 sensitizes cancer cells to genotoxic agents by inhibiting the nuclear translocation of NUPR1 and thereby decreasing the SUMOylation-dependent functions of key proteins involved in the DDR.

Psoriasis is a chronic skin disease that affects 125 million people worldwide and, particularly, 2% of the Spanish population, characterized by the appearance of skin lesions due to a growth of the epidermis that is seven times larger than usual. Its diagnosis and monitoring are based on the use of methodologies for measuring the severity and extent of these spots, and this includes a large subjective component. For this reason, this paper presents an automatic method for characterizing psoriasis images that is divided into four parts: image preparation or pre-processing, feature extraction, classification of the lesions, and the obtaining of parameters. The methodology proposed in this work covers different digital-image processing techniques, namely, marker-based image delimitation, hair removal, nipple detection, lesion contour detection, areal-measurement-based lesion classification, as well as lesion characterization by means of red and white intensity. The results obtained were also endorsed by a professional dermatologist. This methodology provides professionals with a common software tool for monitoring the different existing typologies, which proved satisfactory in the cases analyzed for a set of 20 images corresponding to different types of lesions.

The COVID-19 pandemic outbreak prompts an urgent need for efficient therapeutics, and repurposing of known drugs has been extensively used in an attempt to get to anti-SARS-CoV-2 agents in the shortest possible time. The glycoside rutin shows manifold pharmacological activities and, despite its use being limited by its poor solubility in water, it is the active principle of many pharmaceutical preparations. We herein report our in silico and experimental investigations of rutin as a SARS-CoV-2 Mpro inhibitor and of its water solubility improvement obtained by mixing it with l-arginine. Tests of the rutin/l-arginine mixture in a cellular model of SARS-CoV-2 infection highlighted that the mixture still suffers from unfavorable pharmacokinetic properties, but nonetheless, the results of this study suggest that rutin might be a good starting point for hit optimization.

Inhibiting the main protease 3CLpro is the most common strategy in the search for antiviral drugs to fight the infection from SARS-CoV-2. We report that the natural compound eugenol is able to hamper in vitro the enzymatic activity of 3CLpro, the SARS-CoV-2 main protease, with an inhibition constant in the sub-micromolar range (Ki = 0.81 μM). Two phenylpropene analogs were also tested: the same effect was observed for estragole with a lower potency (Ki = 4.1 μM), whereas anethole was less active. The binding efficiency index of these compounds is remarkably favorable due also to their small molecular mass (MW < 165 Da). We envision that nanomolar inhibition of 3CLpro is widely accessible within the chemical space of simple natural compounds.

The widespread adoption of smartphones and the new-generation wireless networks have changed the way that people interact among themselves and with their environment. The use of messaging platforms, such as WhatsApp, has become deeply ingrained in peoples’ lives, and many digital services have started to be delivered using these communication channels. In this work, we propose a new OAuth grant type to be used when the interaction between the resource owner and the client takes place through a messaging platform. This new grant type firstly allows the authorization server to be sure that no Man-in-the-Middle risk exists between the resource owner and the client before issuing an access token. Secondly, it allows the authorization server to interact with the resource owner through the same user-agent already being used to interact with the client, i.e., the messaging platform, which is expected to improve the overall user experience of the authorization process. To verify this assumption, we conducted a usability study in which subjects were required to perform the full authorization process using both the standard authorization code grant type (through a web-browser) and the new grant type defined in this work. They have also been required to fill in a small questionnaire including some demographic information and their impressions about both authorization flows. The results suggest that the proposed grant type eases the authorization process in most cases.

The development of new antiviral drugs against SARS-CoV-2 is a valuable long-term strategy to protect the global population from the COVID-19 pandemic complementary to the vaccination. Considering this, the viral main protease (Mpro) is among the most promising molecular targets in light of its importance during the viral replication cycle. The natural flavonoid quercetin 1 has been recently reported to be a potent Mpro inhibitor in vitro, and we explored the effect produced by the introduction of organoselenium functionalities in this scaffold. In particular, we report here a new synthetic method to prepare previously inaccessible C-8 seleno-quercetin derivatives. By screening a small library of flavonols and flavone derivatives, we observed that some compounds inhibit the protease activity in vitro. For the first time, we demonstrate that quercetin (1) and 8-(p-tolylselenyl)quercetin (2d) block SARS-CoV-2 replication in infected cells at non-toxic concentrations, with an IC50 of 192 μM and 8 μM, respectively. Based on docking experiments driven by experimental evidence, we propose a non-covalent mechanism for Mpro inhibition in which a hydrogen bond between the selenium atom and Gln189 residue in the catalytic pocket could explain the higher Mpro activity of 2d and, as a result, its better antiviral profile.