Complex diseases: the relationship between genetic and sociocultural factors int the risk of disease

Les malalties complexes són causades per una combinació de factors genètics, ambientals i socioculturals, que interacciones entre si i amb el factor temps. Són molt comunes en la població i bona part també són cròniques, una combinació que comporta alts costos d'atenció sanitària. Però també són malalties que es poden prevenir, fet que també té moltes implicacions importants per als sistemes sanitaris. Els biomarcadors ens permeten integrar les dades clíniques, bioquímiques i genètiques per a calcular millor el risc d'una malaltia. A més, en molts casos, també sabem com controlar els factors socioculturals que contribueixen a la malaltia, com ara l'adopció d'una dieta i un estil de vida diferents. En aquest sentit, la medicina personalitzada convida els pacients a prendre accions clares per a millorar llur estat de salut, prevenir el desenvolupament d'una malaltia o reduir-ne la gravetat.Complex diseases are caused by a combination of genetic, environmental and sociocultural factors, interacting with one another and with the factor of time. They are very common among the population and most of them are also chronic, a combination that implies high healthcare costs. But they are also preventable, which likewise has many important implications for healthcare systems. Biomarkers allow us to integrate clinical, biochemical and genetic data to better calculate the risk of disease. Furthermore, in many cases we also know how to control the sociocultural factors contributing to the disease, such as adopting different diet and lifestyle choices. In this sense, personalised medicine allows and invites patients to take clear actions to improve their health status, prevent the development or reduce the severity of a disease

A Machine-learning Based Ensemble Method For Anti-patterns Detection

Anti-patterns are poor solutions to recurring design problems. Several empirical studies have highlighted their negative impact on program comprehension, maintainability, as well as fault-proneness. A variety of detection approaches have been proposed to identify their occurrences in source code. However, these approaches can identify only a subset of the occurrences and report large numbers of false positives and misses. Furthermore, a low agreement is generally observed among different approaches. Recent studies have shown the potential of machine-learning models to improve this situation. However, such algorithms require large sets of manually-produced training-data, which often limits their application in practice. In this paper, we present SMAD (SMart Aggregation of Anti-patterns Detectors), a machine-learning based ensemble method to aggregate various anti-patterns detection approaches on the basis of their internal detection rules. Thus, our method uses several detection tools to produce an improved prediction from a reasonable number of training examples. We implemented SMAD for the detection of two well known anti-patterns: God Class and Feature Envy. With the results of our experiments conducted on eight java projects, we show that: (1) our method clearly improves the so aggregated tools; (2) SMAD significantly outperforms other ensemble methods.Comment: Preprint Submitted to Journal of Systems and Software, Elsevie

Portugal: Leapfrogging Digital Transformation

This report is structured as follow: Section 1 presents details about Portugal enabling or inhibiting its digital transformation. Section 2 analyzes the main motivations for the digital transformation strategy; Section 3 summarizes its main challenges, while Section 4 presents the main components of the strategy. Section 5 analyzes the governance model, and Section 6, the legal and regulatory framework. Section 7 discusses critical enablers for the digital transformation of government services. Section 8 introduces 16 key initiatives of the strategy. Section 9 summarizes the lessons learnt, followed by an assessment of the strategy’s impact in Section 10. Section 11 synthesizes lessons for Latin American countries. Finally, Appendix A enumerates main legal and regulatory instruments supporting the digital transformation in Portugal, Appendix B presents a set of 18 sections providing details of the initiatives analyzed in the report1, and Appendix C explains how the digital transformation efforts contributed to face the challenges raised by the COVID-19 pandemics.Fil: Estevez, Elsa Clara. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata; ArgentinaFil: Fillottrani, Pablo. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentina. Universidad Nacional del Sur; ArgentinaFil: Linares, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; ArgentinaFil: Cledou, Maria Guillermina. Universidade do Minho; Portuga

Developing innovation competences in engineering education through project-based and challenge-based learning

There is a gap between industry needs and engineering graduates’ competences that since the past two decades has been under discussion. Engineering graduates are perceived as “too theoretical” by the industry and face difficulties when adapting to the practical working context. This gap is being mostly tackled by project-based courses. Furthermore, the expected competences of the future engineers go beyond the purely technical skills. Competences like creativity, innovativeness, business skills, sense of responsibility, problem-based thinking, collaboration, ability to communicate and effectively dealing with stress and uncertainty, among others, will be increasingly important in the future. Innovation competences in particular are key to tackle current societal challenges, but there is limited understanding about what innovation competences are developed through different types of project-based courses. An education that remains only in the scope of technical skills traditionally expected from engineers will eventually limit the capabilities of the engineers to influence strategy and management decisions, as well as concept definition for new products and services. Institutions like ABET, CDIO and ENAEE – EUR-ACE®, highlight these demands for future engineers’ competences. Ultimately, the more engineers master the innovation process beyond the technical aspects, the more impact they can have in shaping the society of the future, and the greater chances they have to position themselves as decision makers. This study discusses what are the innovation competences needed for engineering students and pedagogical approaches to develop those competences, with the aim of understanding how to better design educational strategies to improve innovation competences in future engineering graduates. A broad literature review was developed on existing innovation competences models and pedagogical approaches to develop innovation competences, going from problem-based to project-based learning to challenge-based education, from New Product Development to Design Thinking, and through different strategies to measure innovation competences. Through a mixed method approach, combining quantitative analysis of surveys and qualitative content analysis of project results, we compared two experiential learning courses developed at the UPC Telecom school: a project-based course and a challenge- based course. We compared self-perception on innovation competences using the INCODE (Innovation Competences Development) Barometer and we developed a qualitative content analysis of project results and self-reflection documents of two groups of engineering students from Telecom Engineering school from UPC going through CBI (Challenge Based Innovation) course versus PDP (Product Development Project) course. CBI is an innovative learning experience carried out by three institutions: Telecom Engineering School of UPC, ESADE Business School and IED Instituto Europeo di Design in collaboration with CERN, where mixed teams of students from the three institutions face open innovation challenges through Design Thinking, with the objective of designing solutions to complex societal problems, considering the use of CERN technologies if suitable. PDP is the “standard” capstone course taken by Telecom engineering students following a classical project management approach based on the CDIO framework. Results shows that experiential learning approaches like project-based and challenge-based education are good educational strategies for developing competences and, explicitly, innovation competences in engineering education, but each strategy emphasizes some competences more than others. Project-based courses demonstrates better results in Planning and Managing Projects. Creativity, Leadership and Entrepreneurship are more developed through a challenge-based approach combined with Design Thinking.Existe una brecha entre las necesidades de la industria y las competencias de los graduados en ingeniería que se ha estado debatiendo desde las últimas dos décadas. Los graduados en ingeniería son percibidos como "demasiado teóricos" por la industria y encuentran dificultades para adaptarse al contexto laboral real. Esta brecha se aborda principalmente mediante cursos basados en proyectos, desarrollando las competencias esperadas de los futuros ingenieros, que van más allá de las habilidades puramente técnicas. Competencias como la creatividad, la innovación, las habilidades empresariales, el sentido de la responsabilidad, el pensamiento basado en problemas, la colaboración, la capacidad para comunicarse y afrontar eficazmente el estrés y la incertidumbre, entre otras, serán cada vez más importantes en el futuro. Las competencias de innovación en particular son clave para abordar los desafíos sociales actuales. Pero hay una comprensión limitada sobre qué competencias de innovación se desarrollan a través de diferentes tipos de cursos basados en proyectos. Instituciones como ABET, CDIO y ENAEE - EUR-ACE®, destacan estas demandas de competencias de los futuros ingenieros. Este estudio analiza cuáles son las competencias de innovación necesarias para los estudiantes de ingeniería y los enfoques pedagógicos para desarrollar estas competencias, con el objetivo de comprender cómo diseñar mejores estrategias educativas para el desarrollo de competencias de innovación en los futuros graduados en ingeniería. Se desarrolló una extensa revisión de la literatura incluyendo modelos de competencias de innovación y enfoques pedagógicos existentes para desarrollar competencias de innovación, pasando del aprendizaje basado en problemas al aprendizaje basado en proyectos y la educación basada en retos. También se estudió el desarrollo de nuevos productos (NPD) y el pensamiento de diseño (Design Thinking), así como diferentes estrategias para medir competencias de innovación. A través de un enfoque de métodos mixto, combinando el análisis cuantitativo de encuestas y el análisis de contenido cualitativo de resultados de proyectos, se compararon dos cursos de aprendizaje experiencial desarrollados en la escuela Telecomunicaciones de la UPC: un curso basado en proyectos PDP (Proyecto de desarrollo de producto) y un curso basado en retos (CBI-Challenge Based Innovation). Se analizó la autopercepción sobre competencias de innovación utilizando el Barómetro INCODE (Innovation Competences Development), y se desarrolló un análisis de contenido cualitativo de los resultados de proyectos y documentos de autorreflexión. CBI es una experiencia de aprendizaje innovadora llevada a cabo por tres instituciones: Escuela de Ingeniería de Telecomunicaciones de la UPC, ESADE Business School e IED Istituto Europeo di Design en colaboración con CERN, donde equipos mixtos de estudiantes de las tres instituciones enfrentan desafíos de innovación abierta a través del Design Thinking, con el objetivo de diseñar soluciones a problemas sociales complejos, considerando el uso de tecnologías CERN (si es apropiado). PDP es el curso final ¿estándar¿ que toman los estudiantes de ingeniería de telecomunicaciones siguiendo un enfoque clásico de gestión de proyectos basado en el marco CDIO. Los resultados muestran que los enfoques de aprendizaje experiencial como la educación basada en proyectos y la educación basada en retos son buenas estrategias educativas para desarrollar competencias y, específicamente, competencias de innovación en la educación en ingeniería. Pero cada estrategia enfatiza algunas competencias más que otras. Los cursos basados en proyectos demuestran mejores resultados en la planificación y gestión de proyectos. La creatividad, el liderazgo y el espíritu empresarial se desarrollan más a través de un enfoque basado en retos combinado con Design Thinking.Postprint (published version

STATISTICAL ASPECTS OF FETAL SCREENING

This thesis discusses the current screening algorithm that is used to detect fetal Down's syndrome. The algorithm combines a model for predicting age related risks and a model for appropriately transformed serum concentrations to produce estimates of risks. A discriminant analysis is used to classify pregnancies as either unaffected or Down's syndrome. The serum concentrations vary with gestational age and the relationship between serum concentrations and gestational age is modelled using regression. These models are discussed and alternative models for these relationships are offered. Concentration values are generally expressed in terms of multiples of the medians for unaffected pregnancies, or MoM values, which involves grouping the concentrations into weekly bins. Transformations of the MoM values are used in the model for predicting risks. The transformed values are equivalent to the residuals of the fitted regression models. This thesis directly models the residuals rather than converting the data to MoM values. This approach avoids the need to group gestational dates into completed weeks. The performance of the algorithm is assessed in terms the detection rates and false positive rates. The performance rates are prone to considerable sampling error. Simulation methods are used to calculate standard errors for reported detection rates. The bias in the rates is also investigated using bootstrapping techniques. The algorithm often fails to recognize abnormalities other than Down's syndrome and frequently associates them with low risks. A solution to the problem is offered that assigns an index of atypicality to each pregnancy, to identify those pregnancies that are atypical of unaffected pregnancies, but are also unlike Down's syndrome pregnancies. Nonparametric techniques for estimating the class conditional densities of transformed serum values are used as an alternative to the conventional parametric techniques of estimation. High quality density estimates are illustrated and these are used to compute nonparametric likelihood ratios that can be used in the probability model to predict risks. The effect of errors in the methods of recording gestational dates on the parameter estimates that are used in the discriminant analysis is also considered.Johnson & Johnson Clinical Diagnostics Lt

A Clinical-Genetic Score for Predicting Weight Loss after Bariatric Surgery: The OBEGEN Study

Genética; Obesidad; Pérdida de pesoGenetics; Obesity; Weight lossGenètica; Obesitat; Pèrdua de pesAround 30% of the patients that undergo bariatric surgery (BS) do not reach an appropriate weight loss. The OBEGEN study aimed to assess the added value of genetic testing to clinical variables in predicting weight loss after BS. A multicenter, retrospective, longitudinal, and observational study including 416 patients who underwent BS was conducted (Clinical.Trials.gov- NCT02405949). 50 single nucleotide polymorphisms (SNPs) from 39 genes were examined. Receiver Operating Characteristic (ROC) curve analysis were used to calculate sensitivity and specificity. Satisfactory response to BS was defined as at nadir excess weight loss >50%. A good predictive model of response [area under ROC of 0.845 (95% CI 0.805–0.880), p < 0.001; sensitivity 90.1%, specificity 65.5%] was obtained by combining three clinical variables (age, type of surgery, presence diabetes) and nine SNPs located in ADIPOQ, MC4R, IL6, PPARG, INSIG2, CNR1, ELOVL6, PLIN1 and BDNF genes. This predictive model showed a significant higher area under ROC than the clinical score (p = 0.0186). The OBEGEN study shows the key role of combining clinical variables with genetic testing to increase the predictability of the weight loss response after BS. This finding will permit us to implement a personalized medicine which will be associated with a more cost-effective clinical practice.This research was supported by grants from the “Pla Estratègic de Recerca i Innovació en Salut” (PERIS) 2016–2020 (SLT002/16/00497), the Instituto de Salud Carlos III (PI PI18/00964), Fondos FEDER “Una manera de hacer Europa”), and Menarini España. CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM) is an initiative of the Instituto Carlos III

An Analytical Study of Code Smells

Software development process involves developing, building and enhancing high-quality software for specific tasks and as a consequence generates considerable amount of data. This data can be managed in a systematic manner creating knowledge repositories that can be used to competitive advantage. Lesson\u27s learned as part of the development process can also be part of the knowledge bank and can be used to advantage in subsequent projects by developers and software practitioners. Code smells are a group of symptoms which reveal that code is not good enough and requires some actions to have a cleansed code. Software metrics help to detect code smells while refactoring methods are used for removing them. Furthermore, various tools are applicable for detecting of code smells. A Code smell repository organizes all the available knowledge in the literature about code smells and related concepts. An analytical study of code smells is presented in this paper which extracts useful, actionable and indicative knowledge

Artificial Intelligence Research and Its Contributions to the European Union’s Political Governance: Comparative Study between Member States

In the last six decades, many advances have been made in the field of artificial intelligence (AI). Bearing in mind that AI technologies are influencing societies and political systems di erently, it can be useful to understand what are the common issues between similar states in the European Union and how these political systems can collaborate with each other, seeking synergies, finding opportunities and saving costs. Therefore, we carried out an exploratory research among similar states of the European Union, in terms of scientific research in areas of AI technologies, namely: Portugal, Greece, Austria, Belgium and Sweden. A key finding of this research is that intelligent decision support systems (IDSS) are essential for the political decision-making process, since politics normally deals with complex and multifaceted decisions, which involve trade-o s between di erent stakeholders. As public health is becoming increasingly relevant in the field of the European Union, the IDSSs can provide relevant contributions, as it may allow sharing critical information and assist in the political decision-making process, especially in response to crisis situations.info:eu-repo/semantics/publishedVersio

Code Smells and Refactoring: A Tertiary Systematic Review of Challenges and Observations

In this paper, we present a tertiary systematic literature review of previous surveys, secondary systematic literature reviews, and systematic mappings. We identify the main observations (what we know) and challenges (what we do not know) on code smells and refactoring. We show that code smells and refactoring have a strong relationship with quality attributes, i.e., with understandability, maintainability, testability, complexity, functionality, and reusability. We argue that code smells and refactoring could be considered as the two faces of a same coin. Besides, we identify how refactoring affects quality attributes, more than code smells. We also discuss the implications of this work for practitioners, researchers, and instructors. We identify 13 open issues that could guide future research work. Thus, we want to highlight the gap between code smells and refactoring in the current state of software-engineering research. We wish that this work could help the software-engineering research community in collaborating on future work on code smells and refactoring