MedVir: 3D visual interface applied to gene profile analysis

The use of data mining techniques for the gene profile discovery of diseases, such as cancer, is becoming usual in many researches. These techniques do not usually analyze the relationships between genes in depth, depending on the different variety of manifestations of the disease (related to patients). This kind of analysis takes a considerable amount of time and is not always the focus of the research. However, it is crucial in order to generate personalized treatments to fight the disease. Thus, this research focuses on finding a mechanism for gene profile analysis to be used by the medical and biologist experts. Results: In this research, the MedVir framework is proposed. It is an intuitive mechanism based on the visualization of medical data such as gene profiles, patients, clinical data, etc. MedVir, which is based on an Evolutionary Optimization technique, is a Dimensionality Reduction (DR) approach that presents the data in a three dimensional space. Furthermore, thanks to Virtual Reality technology, MedVir allows the expert to interact with the data in order to tailor it to the experience and knowledge of the expert

A LINDDUN-based framework for privacy threat analysis on identification and authentication processes

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Identification and authentication (IA) are security procedures that are ubiquitous in our online life, and that constantly require disclosing personal, sensitive information to non-fully trusted service providers, or to fully trusted providers that unintentionally may fail to protect such information. Although user IA processes are extensively supported by heterogeneous software and hardware, the simultaneous protection of user privacy is an open problem. From a legal point of view, the European Union legislation requires protecting the processing of personal data and evaluating its impact on privacy throughout the whole IA procedure. Privacy Threat Analysis (PTA) is one of the pillars for the required Privacy Impact Assessment (PIA). Among the few existing approaches for conducting a PTA, LINDDUN is a very promising framework, although generic, in the sense that it has not been specifically conceived for IA. In this work, we investigate an extension of LINDDUN that allows performing a reliable and systematically-reproducible PTA of user IA processes, thereby contributing to one of the cornerstones of PIA. Specifically, we propose a high-level description of the IA verification process, which we illustrate with an UML use case. Then, we design an identification and authentication modelling framework, propose an extension of two critical steps of the LINDDUN scheme, and adapt and tailor the trust boundary concept applied in the original framework. Finally, we propose a systematic methodology aimed to help auditors apply the proposed improvements to the LINDDUN framework.The authors are thankful for the support through the research project “INRISCO”, ref. TEC2014-54335-C4-1-R, “MAGOS”, TEC2017-84197-C4-3-R, and the project “Sec-MCloud”, ref. TIN2016-80250-R. J. Parra-Arnau is the recipient of a Juan de la Cierva postdoctoral fellowship, IJCI-2016–28239, from the Spanish Ministry of Economy and Competitiveness. J. Parra-Arnau is with the UNESCO Chair in Data Privacy, but the views in this paper are his own and are not necessarily shared by UNESCO.Peer ReviewedPostprint (author's final draft

Privacy-centered authentication: a new framework and analysis

© 2023 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The usage of authentication schemes is increasing in our daily life with the ubiquitous spreading Internet services. The verification of user’s identity is still predominantly password-based, despite of being susceptible to various attacks and openly hated by users. Bonneau et al. presented a framework, based on Usability, Deployability, and Security criteria (UDS), to evaluate authentication schemes and find a replacement for passwords. Although the UDS framework is a mature and comprehensive evaluation framework and has been extended by other authors, it does not analyse privacy aspects in the usage of authentication schemes. In the present work, we extend the UDS framework with a privacy category to allow a more comprehensive evaluation, becoming an UDSP framework. We provide a thorough, rigorous assessment of sample authentication schemes, including analyse novel behavioural biometrics. Our work also discusses implementation aspects regarding the new privacy dimension and sketches the prospect of future authentication schemes.Javier Parra-Arnau is the recipient of a “Ramón y Cajal” fellowship (ref. RYC2021–034256-I) funded by the Spanish Ministry of Science and Innovation and the European Union – “NextGenerationEU”/PRTR (Plan de Recuperación, Transformación y Resiliencia). This work was also supported by the Spanish Government under the project “Enhancing Communication Protocols with Machine Learning while Protecting Sensitive Data (COMPROMISE)” PID2020–113795RB-C31, funded by MCIN/AEI/10.13039/501100011033, and through the project “MOBILYTICS” (TED2021–129782B-I00), funded by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR.Peer ReviewedPostprint (published version

VR BioViewer - A new interactive-visual model to represent medical information

Virtual reality (VR) techniques to understand and obtain conclusions of data in an easy way are being used by the scientific community. However, these techniques are not used frequently for analyzing large amounts of data in life sciences, particularly in genomics, due to the high complexity of data (curse of dimensionality). Nevertheless, new approaches that allow to bring out the real important data characteristics, arise the possibility of constructing VR spaces to visually understand the intrinsic nature of data. It is well known the benefits of representing high dimensional data in tridimensional spaces by means of dimensionality reduction and transformation techniques, complemented with a strong component of interaction methods. Thus, a novel framework, designed for helping to visualize and interact with data about diseases, is presented. In this paper, the framework is applied to the Van't Veer breast cancer dataset is used, while oncologists from La Paz Hospital (Madrid) are interacting with the obtained results. That is to say a first attempt to generate a visually tangible model of breast cancer disease in order to support the experience of oncologists is presented

A methodology to compare dimensionality reduction algorithms in terms of loss of quality

Dimensionality Reduction (DR) is attracting more attention these days as a result of the increasing need to handle huge amounts of data effectively. DR methods allow the number of initial features to be reduced considerably until a set of them is found that allows the original properties of the data to be kept. However, their use entails an inherent loss of quality that is likely to affect the understanding of the data, in terms of data analysis. This loss of quality could be determinant when selecting a DR method, because of the nature of each method. In this paper, we propose a methodology that allows different DR methods to be analyzed and compared as regards the loss of quality produced by them. This methodology makes use of the concept of preservation of geometry (quality assessment criteria) to assess the loss of quality. Experiments have been carried out by using the most well-known DR algorithms and quality assessment criteria, based on the literature. These experiments have been applied on 12 real-world datasets. Results obtained so far show that it is possible to establish a method to select the most appropriate DR method, in terms of minimum loss of quality. Experiments have also highlighted some interesting relationships between the quality assessment criteria. Finally, the methodology allows the appropriate choice of dimensionality for reducing data to be established, whilst giving rise to a minimum loss of quality

Obtención de ácido eicosapentaenoico de alta pureza a partir de la microalga Phaeodactylum tricornutum mediante un proceso de tres etapas

Número de publicación: ES2120898 A1 (01.11.1998) También publicado como: ES2120898 B1 (16.05.1999) Número de Solicitud: Consulta de Expedientes OEPM (C.E.O.) P9602090 (04.10.1996)El ácido eicosapentaenoico (EPA), de alto interés para la salud humana, ha sido obtenido de la microalga Phaeodactylum tricornutum mediante un proceso de tres etapas: extracción de los ácidos grasos por saponificación directa de la biomasa húmeda; concentración de los ácidos grasos poliinsaturados (PUFAs) por el método de la urea y purificación del EPA por cromatografía liquida de alta resolución (HPLC). La extracción de los ácidos grasos se ha llevado a cabo con koh-etanol (96% v/v) (1h, 60 c) extrayéndose el 90,9% del EPA presente en la biomasa. La relación urea/ácidos grasos ha sido 4:1 p/p, la temperatura de cristalización 28 c y se ha utilizado metanol como disolvente de la urea, obteniéndose un rendimiento del 78,6% en EPA. La purificación del EPA se ha realizado en una columna de 4,7 cm d.i. X 30 cm, fase inversa c18 y utilizando metanol-agua (1% ACH) 80:20 p:p como fase móvil; el 97,3% del EPA cargado en la columna se ha recuperado con una pureza del 95,8%. El rendimiento global del proceso es del 69,5%. La producción de EPA de alta pureza es de 2,46 g/día, determinada por la producción de biomasa de phaeodactylum tricornutum de un fotobiorreactor tubular externo (100 g/día). Este mismo proceso puede aplicarse a otras microalgas y a la obtención de otros PUFAsUniversidad de Almerí

Comparative analysis between a STEM-based learning process and traditional teaching

The use of technology in education has modified teaching and learning processes. New concepts such as science, technology, engineering and mathematics (STEM) are changing traditional learning. The purpose of STEM education is to prepare students for university engineering courses and higher technical education. The main aim of the study reported on here was to understand the influence of a STEM-based teaching process in different socio-educational dimensions. This was done by comparing the results achieved with a traditional expository teaching process with different groups of students. A quasiexperimental design was applied. A sample of 231 Spanish students from the first year of secondary education (ESO) was chosen. The results show that the STEM approach was significant in all the dimensions of study and, according to teachers, was more influential for student motivation and grades. The results also show that the STEM teaching approach was significant in all the dimensions of study. These dimensions are motivation; teacher-student, student-content and student-student interactions; autonomy; collaboration; depth of content; resolution of problems; class time, student ratings; and teacher ratings. According to teachers, the strongest influence was on the students' motivation and qualifications

Machine Learning and Big Data in the Impact Literature. A Bibliometric Review with Scientific Mapping in Web of Science

Combined use of machine learning and large data allows us to analyze data and find explanatory models that would not be possible with traditional techniques, which is basic within the principles of symmetry. The present study focuses on the analysis of the scientific production and performance of the Machine Learning and Big Data (MLBD) concepts. A bibliometric methodology of scientific mapping has been used, based on processes of estimation, quantification, analytical tracking, and evaluation of scientific research. A total of 4240 scientific publications from the Web of Science (WoS) have been analyzed. Our results show a constant and ascending evolution of the scientific production on MLBD, 2018 and 2019 being the most productive years. The productions are mainly in English language. The topics are variable in the different periods analyzed, where “machine-learning” is the one that shows the greatest bibliometric indicators, it is found in most of motor topics and is the one that offers the greatest line of continuity between the different periods. It can be concluded that research on MLBD is of interest and relevance to the scientific community, which focuses its studies on the branch of machine-learning

Elaboration and validation of the scale to measure the experience on gamification in education (EGAMEDU)

Nowadays, we talk about the use of gamification in education, an active methodology that consists of the use of mechanics, design or game structures in class. With this type of methodology, the effort is rewarded, which is treated as a motivating tool in class. There is no valid or well-structured instrument to measure gamification properly in education. This research arises from the need to develop and validate an instrument to measure the experience on gamification in educational contexts (EGAMEDU) as a useful tool of diagnosis so that the teaching staff can guide their teaching practice toward the use of this methodology. The sample used for the validation of the questionnaire is composed of 401 participants related to education and gamified experiences. The results show good validity indexes and a factorial structure according to the one proposed in the theoryPeer Reviewe

Spanish Adaptation and Validation of the Teaching and Learning Experiences Questionnaire

Training processes are mainly based on the pedagogical methods applied by teachers. In many cases, these pedagogical methods are adapted to the social, economic, and cultural environment of the students themselves. In this study, we used a psychometric analysis based on the analysis of structural equations to detect the psychometric properties through classical goodness-of-fit indices. The objective of this study was to translate, adapt, and validate the instrument called the Teaching and Learning Experiences Questionnaire (ETLQ) for the population of Spanish adolescents in secondary education. The rrecommendations in the literature were followed for its translation and adaptation into Spanish. The results indicate that, after translation and adaptation, the model remained in 11 factors with acceptable goodness-of-fit indices. We conclude that the process of translation, adaptation, and validation of the ETLQ has produced a valid and reliable tool due to the psychometric findings revealed in the present work