Environmental Sustainability in Information Technologies Governance

[Abstract] In the present day, many risk factors affect the continuity of a business. However, this situation produces a conducive atmosphere to approach alternatives that relieve this situation for organizations. Within these alternatives, environmental sustainability (ES) and information technologies governance (IT governance or ITG) stand out. Both alternatives allow organizations to address intrinsically common issues such as strategic alignment, generation of value, mechanisms for performance improvement, risk management and resource management. This article focuses on the fusion of both alternatives, determining to what extent current ITG models consider ES issues. With this purpose, the strategy followed was firstly to identify the relevant factors of ES present in the main approaches of the domain (ISO14001, GRI G4, EMAS, SGE21 and ISO26000). As a result, we identified 27 activities and 103 sub-activities of ES. Next, as the second main objective, we determined which of those factors are present in the main current ITG approaches (COBIT5, ISO38500 and WEILL & ROSS). Finally, we concluded through a quantitative study that COBIT5 is the most sustainable (i.e., the one that incorporates more ES issues) ITG approach

A metrology-based approach for measuring the social dimension of cognitive trust in collaborative networks

This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10111-018-0483-1[Abstract]: This paper addresses the measurement of the social dimension of cognitive trust in collaborative networks. Trust indicators are typically measured and combined in literature in order to calculate partners’ trustworthiness. When expressing the result of a measurement, some quantitative indication of the quality of the result—the uncertainty of measurement—should be given. However, currently this is not taken into account for the measurement of the social dimension of cognitive trust in collaborative networks. In view of this, an innovative metrology-based approach for the measurement of social cognitive trust indicators in collaborative networks is presented. Thus, a measurement result is always accompanied by its uncertainty of measurement, as well as by information traditionally used to properly interpret the results: the sample size, and the standard deviation of the sample

PanDrugs2: prioritizing cancer therapies using integrated individual multi-omics data

Genomics studies routinely confront researchers with long lists of tumor alterations detected in patients. Such lists are difficult to interpret since only a minority of the alterations are relevant biomarkers for diagnosis and for designing therapeutic strategies. PanDrugs is a methodology that facilitates the interpretation of tumor molecular alterations and guides the selection of personalized treatments. To do so, PanDrugs scores gene actionability and drug feasibility to provide a prioritized evidence-based list of drugs. Here, we introduce PanDrugs2, a major upgrade of PanDrugs that, in addition to somatic variant analysis, supports a new integrated multi-omics analysis which simultaneously combines somatic and germline variants, copy number variation and gene expression data. Moreover, PanDrugs2 now considers cancer genetic dependencies to extend tumor vulnerabilities providing therapeutic options for untargetable genes. Importantly, a novel intuitive report to support clinical decision-making is generated. PanDrugs database has been updated, integrating 23 primary sources that support >74K drug–gene associations obtained from 4642 genes and 14 659 unique compounds. The database has also been reimplemented to allow semi-automatic updates to facilitate maintenance and release of future versions. PanDrugs2 does not require login and is freely available at https://www.pandrugs.org/.Instituto de Salud Carlos III | Ref. IMP/00019Agencia Estatal de Investigacion | Ref. PID2021-124188NB-I00Xunta de Galicia | Ref. | Ref. ED431C2018/55Xunta de Galicia | Ref. | Ref. ED431C2022/0

Guía para la evaluación de repositorios institucionales de investigación

Esta es la tercera versión de la Guía para la evaluación de repositorios institucionales de investigación, cuya primera edición fue publicada por RECOLECTA en Diciembre de 2010.Guia realitzada amb el suport de la Fundación Española para la Ciencia y la Tecnología i Crue Universidades EspañolasEsta nueva versión de la Guía para la evaluación de repositorios institucionales de investigación tiene como finalidad principal el seguir siendo instrumento de auditoría interna para mejorar la calidad de los repositorios. Disponer de esta herramienta de autoevaluación supone una importante ayuda a la hora de detectar puntos fuertes y áreas de mejora, lo cual contribuye a incrementar el alcance y la visibilidad de los repositorios entre la comunidad científica. Además, para los repositorios que se encuentran en fase de planificación, este documento ofrece las pautas necesarias a tener en cuenta para crear una infraestructura robusta y visible. Esta guía está dirigida primordialmente a los profesionales implicados en la gestión de repositorios científicos: responsables, administradores, gestores, técnicos o personal de apoyo. Para elaborarla se ha constituido un grupo de trabajo, en el marco de la colaboración FECYT-REBIUN

Implementación de la metodología flipped classroom en los laboratorios de Química Analítica

Adaptar el sistema tradicional de aprendizaje a las necesidades actuales del alumnado empleando la metodología flipped classroom en el laboratorio de Química Analítica I, con el objetivo de fomentar el aprendizaje utilizando herramientas digitales.Depto. de Química AnalíticaFac. de Ciencias QuímicasFALSEsubmitte

Análisis del uso del Cloud Computing en empresas de Ecuador

Existe una innegable tendencia en las empresas ecuatorianas hacia la adopción del Cloud Computing. Sin embargo existen pocos estudios sobre los factores que influyen en esta adopción. Este trabajo incluye un estudio exploratorio que investiga los factores que influyen en la adopción del Cloud Computing por parte de las PYMEs. Se implementaron encuestas con alrededor de 20 preguntas a una muestra probabilística entre empresas de la provincia de El Oro, una de las provincias más importantes de Ecuador. Los datos recopilados a partir de 331 empresas se utilizan para probar las hipótesis. El análisis de los datos indica que el desconocimiento de los beneficios de este tipo de servicios y el poco interés de los directivos sobre este nuevo modelo de negocio, resultaron ser factores determinantes en la migración a servicios Cloud Computing. Estos hallazgos brindan doble ventaja. En primer lugar, proporcionan datos sobre el conocimiento y uso del Cloud Computing, determinantes en el mercado ecuatoriano. En segundo lugar, proporcionan a los planificadores de políticas y tomadores de decisiones de las empresas nuevas perspectivas y orientaciones para el éxito en la adopción de la tecnología de Cloud Computing

Using Artificial Neural Networks for Identifying Patients with Mild Cognitive Impairment Associated with Depression Using Neuropsychological Test Features

Depression and cognitive impairment are intimately associated, especially in elderly people. However, the association between late-life depression (LLD) and mild cognitive impairment (MCI) is complex and currently unclear. In general, it can be said that LLD and cognitive impairment can be due to a common cause, such as a vascular disease, or simply co-exist in time but have different causes. To contribute to the understanding of the evolution and prognosis of these two diseases, this study’s primary intent was to explore the ability of artificial neural networks (ANNs) to identify an MCI subtype associated with depression as an entity by using the scores of an extensive neurological examination. The sample consisted of 96 patients classified into two groups: 42 MCI with depression and 54 MCI without depression. According to our results, ANNs can identify an MCI that is highly associated with depression distinguishable from the non-depressed MCI patients (accuracy = 86%, sensitivity = 82%, specificity = 89%). These results provide data in favor of a cognitive frontal profile of patients with LLD, distinct and distinguishable from other cognitive impairments. Therefore, it should be taken into account in the classification of MCI subtypes for future research, including depression as an essential variable in the classification of a patient with cognitive impairment