Scientific Advances in STEM: From Professor to Students

This book collects the publications of the special Topic Scientific advances in STEM: from Professor to students. The aim is to contribute to the advancement of the Science and Engineering fields and their impact on the industrial sector, which requires a multidisciplinary approach. University generates and transmits knowledge to serve society. Social demands continuously evolve, mainly because of cultural, scientific, and technological development. Researchers must contextualize the subjects they investigate to their application to the local industry and community organizations, frequently using a multidisciplinary point of view, to enhance the progress in a wide variety of fields (aeronautics, automotive, biomedical, electrical and renewable energy, communications, environmental, electronic components, etc.). Most investigations in the fields of science and engineering require the work of multidisciplinary teams, representing a stockpile of research projects in different stages (final year projects, master’s or doctoral studies). In this context, this Topic offers a framework for integrating interdisciplinary research, drawing together experimental and theoretical contributions in a wide variety of fields

Persuasion in the context of a psychic reading

This thesis considers the claim that although there is little reason on the basis of experimental evidence to believe that psychic readers have paranormal access to information about their clients, nevertheless individuals are persuaded that such claimants have demonstrated that they possess psychic abilities. A random sample survey of 1,000 residents of Edinburgh district did find support for the claim that the general population is sympathetic to the claims made by psychics. These findings are reconciled with reference to Pseudopsychics' claimed ability to simulate psychic abilities through the use of a technique known as cold reading. A model is proposed, informed by a review of pseudopsychic literature and a pilot study with a known cold reader, which suggests that cold reading actually consists of a number of discrete but interdependent techniques. Central to the model is that much of the reading is dependent on the Bamum effect for success. Experimental work assessed the previously untested assertion that pseudopsychic statements are capable of inducing Barnum acceptance, and found that such items perform in a similar manner to classical Barnum statements. These statements were used to expand the Barnum pool so that the nature and causes of Barnum acceptance could be studied more systematically. One study explored those properties inherent in Barnum statements which are regarded as contributing to their ready acceptance as true of Ss. It was found that acceptance of items could be predicted on the basis of independent judges' ratings of eight statement properties.Two further studies presented Bamum items as pseudo-feedback from an ostensible psychic reading. These were conducted to explore a proposed model which suggested that Ss accept items because of an artifact of cognitive processing, whereby Barnum statements are not assessed for accuracy in their given form, but rather are interpreted by the client in terms of their own particular circumstances and concerns. Predictions were made on the basis of the artifact model about Ss' recall for the content of the reading, and provided some support for this characterisation of the effect. A final study was conducted to assess the contention that experimental tests of psychic readers misrepresent the function of the reading, and makes the suggestion that with regard to psychic functioning, the client may actually be an active participant. The implications of these results for testing and evaluating psychic readers are discussed

Development of a variant interpretation framework for the SIGEN genomic diagnostic service

"Diagnostic of genetics diseases from high throughput DNA sequencing data is becoming a common practice. The SIGEN diagnostic service aims to offer high quality genetic diagnosis service in Colombia. However, an important concern among practitioners interpreting genetic diagnostic reports is the significant number of disease-related variants classified as Variants of Uncertain Significance (VUS). An additional barrier is the high cost of software and databases required in the the interpretation process. Here, we present a framework for variant interpretation using only open access software tools and databases, tested with real data from patients with suspected genetic disease. To help prioritize VUS with higher probabilities of being pathogenic, we developed different machine-learning methods. We trained and compared a Naive Bayes model, a Random Forest (RF), a Support Vector Machine, and a Five-Layer Perceptron (MLP) using variants from ClinVar classified as pathogenic, likely pathogenic, likely benign and benign on october 2019. A set of conservation scores and 1,000 human genomes global allele frequencies were used as features for model training. The RF and the MLP models showed the highest accuracy, above commonly used tools for variant deleteriousness prediction. Additionally, we developed a database of the variants found in our patient population and a web interface to make it more accessible."--Tomado del Formato de Documento de Grado"El diagnóstico de enfermedades genéticas con secuenciación de ADN de alto rendimiento es una práctica cada vez más común. El servicio de diagnóstico de SIGEN tiene como objetivo ofrecer diagnóstico genético de calidad en Colombia. Sin embargo, el trabajo de los especialistas que interpretan los reportes diagnósticos es el alto número de Variantes de Significado Incierto (VUS). Adicionalmente, el alto costo del software y las bases de datos usadas en el proceso de interpretación son una barrera para su implementación. En el presente trabajo, se presenta un proceso de interpretación de variantes utilizando únicamente software y bases de datos de acceso libre, evaluado en datos reales de pacientes con sospecha de enfermedades genéticas. Para priorizar las VUS con mayor probabilidad de ser patogénicas, se desarrollaron diferentes métodos de Machine Learning. Se entrenaron y compararon modelos basados en Bayes Ingenuo, Bosque Aleatorio (RF), Máquina de Soporte Vectorial y un Perceptron de Cinco Capas (MLP) usando variantes de ClinVar clasificadas como patogénicas, probablemente patogénicas, probablemente benignas y benignas en octubre de 2019. Como atributos para el entrenamiento se utilizó un conjunto de puntajes de conservación y las frecuencias alélicas globales del proyecto de 1000 genomas humanos. Los módelos basados en RF y MLP mostraron la exactitud más alta, sobre herramientas usadas comúnmente en la predicción de variantes. Adicionalmente, se desarrolló una base de datos de las variantes encontradas en nuestra población de pacientes y una interfaz web para facilitar su accesibilidad."--Tomado del Formato de Documento de GradoMagíster en Biología ComputacionalMaestrí