Development and Applications of Similarity Measures for Spatial-Temporal Event and Setting Sequences

Similarity or distance measures between data objects are applied frequently in many fields or domains such as geography, environmental science, biology, economics, computer science, linguistics, logic, business analytics, and statistics, among others. One area where similarity measures are particularly important is in the analysis of spatiotemporal event sequences and associated environs or settings. This dissertation focuses on developing a framework of modeling, representation, and new similarity measure construction for sequences of spatiotemporal events and corresponding settings, which can be applied to different event data types and used in different areas of data science. The first core part of this dissertation presents a matrix-based spatiotemporal event sequence representation that unifies punctual and interval-based representation of events. This framework supports different event data types and provides support for data mining and sequence classification and clustering. The similarity measure is based on the modified Jaccard index with temporal order constraints and accommodates different event data types. This approach is demonstrated through simulated data examples and the performance of the similarity measures is evaluated with a k-nearest neighbor algorithm (k-NN) classification test on synthetic datasets. These similarity measures are incorporated into a clustering method and successfully demonstrate the usefulness in a case study analysis of event sequences extracted from space time series of a water quality monitoring system. This dissertation further proposes a new similarity measure for event setting sequences, which involve the space and time in which events occur. While similarity measures for spatiotemporal event sequences have been studied, the settings and setting sequences have not yet been considered. While modeling event setting sequences, spatial and temporal scales are considered to define the bounds of the setting and incorporate dynamic variables along with static variables. Using a matrix-based representation and an extended Jaccard index, new similarity measures are developed to allow for the use of all variable data types. With these similarity measures coupled with other multivariate statistical analysis approaches, results from a case study involving setting sequences and pollution event sequences associated with the same monitoring stations, support the hypothesis that more similar spatial-temporal settings or setting sequences may generate more similar events or event sequences. To test the scalability of STES similarity measure in a larger dataset and an extended application in different fields, this dissertation compares and contrasts the prospective space-time scan statistic with the STES similarity approach for identifying COVID-19 hotspots. The COVID-19 pandemic has highlighted the importance of detecting hotspots or clusters of COVID-19 to provide decision makers at various levels with better information for managing distribution of human and technical resources as the outbreak in the USA continues to grow. The prospective space-time scan statistic has been used to help identify emerging disease clusters yet results from this approach can encounter strategic limitations imposed by the spatial constraints of the scanning window. The STES-based approach adapted for this pandemic context computes the similarity of evolving normalized COVID-19 daily cases by county and clusters these to identify counties with similarly evolving COVID-19 case histories. This dissertation analyzes the spread of COVID-19 within the continental US through four periods beginning from late January 2020 using the COVID-19 datasets maintained by John Hopkins University, Center for Systems Science and Engineering (CSSE). Results of the two approaches can complement with each other and taken together can aid in tracking the progression of the pandemic. Overall, the dissertation highlights the importance of developing similarity measures for analyzing spatiotemporal event sequences and associated settings, which can be applied to different event data types and used for data mining, sequence classification, and clustering

Emerging H9N2 Avian Influenza virus research at the human and animal interface

Most emerging diseases are of zoonotic origin. Globalization and intensive animal farming has led to an increased spread of zoonotic infections and it is therefore of crucial importance to be prepared in managing potential outbreaks. Influenza type A virus is cause of a zoonotic disease, which can affect animal and human health. A number of influenza virus subtypes have successfully crossed the species barrier and have established in mammal and human populations, causing yearly seasonal epidemics. The influenza A viruses of the H9N2 subtype are classified as low pathogenic avian influenza (AI) viruses which infect mostly avian species. The H9N2 type has caused infections both in wild birds and in the poultry population around the globe, including several countries in Asia, Europe, North Africa and Americas since the mid-1990s. The wide circulation of H9N2 viruses throughout Eurasia, along with their ability to cause direct infection in mammals and humans, raises public health concern about their potential to become candidates for the next influenza pandemic. This Ph.D. thesis deals with phylogenetic and Bayesian phylogeographic analyses, genetic nomenclature and molecular evolutionary dynamics to elucidate the different aspects of H9 subtypes circulating worldwide. Focusing on the hemagglutinin gene through the application of bioinformatics tools a large amount of H9 subtype strains global data have been analysed and a unified nomenclature system have been designed producing a lot of data that contributed to clarify the evolution dynamics of H9 subtype viruses identified to date. The evolutionary dynamics of H9N2 in an endemic country as Iran have been elaborated in further detail. Phylogenetic and molecular studies have been performed on all the H9N2 Iranian viruses circulating from 1998 to 2015 along with other neighboring countries to identify the phylogenetic relationships grouping the various introductions and the spread to other countries, in order to highlight the evolution dynamics and the extent of its molecular change since the H9N2 first detection in Iran and after the application of a vaccination program. In addition, one of the research topics includes a serological investigation of H9N2 avian influenza virus among the poultry workers in an endemic country as Iran where, despite of the control measures implemented at national level including mass vaccination of poultry, the subtype H9N2 has rapidly spread and can be considered endemic in Iranian poultry. This study has been carried in collaboration with an Iranian research group to reveal the association between professional exposure to poultry and the presence of antibodies to H9N2 viruses in order to develop surveillance and control programs to monitor the biological risk and thus to preserve public health against the spreading of the H9N2 virus

Glycolytic Inhibitors as Leads for Drug Discovery in the Pathogenic Free-Living Amoebae

The free-living amoeba, Naegleria fowleri, can cause a rare yet usually lethal infection of the brain called primary amebic meningoencephalitis. Because of poor diagnostics and limited treatment options, the mortality rate associated with the disease is \u3e97%. Due to our finding that glucose is critical for trophozoite growth in culture, we have been interested in exploiting amoebae glucose metabolism to identify new potential drug targets. We have characterized the first enzyme of the glycolytic pathway, glucokinase (Glck), from N. fowleri and two other pathogenic free-living amoeba, Acanthamoeba castellanii and Balamuthia mandrillaris. We have assessed their biochemical properties and tested potential inhibitors on the recombinant Glcks, which revealed that these enzymes are sufficiently different from one another that developing pan-amoeba inhibitors may be challenging. However, their individual differences from the human host enzyme suggests that species-specific Glck inhibitors could be identified. We have also explored targeting the glucose metabolizing enzyme enolase in N. fowleri using a series of phosphonate human enolase 2 (ENO-2) specific inhibitors that were developed to treat human cancer. These compounds are curative for ENO-1 deleted glioblastoma in a rodent model, can cross the blood-brain barrier, and are of limited toxicity to non-human primates. The phosphonate inhibitors were toxic to N. fowleri in vitro with (1-hydroxy-2-oxopiperidin-3-yl) phosphonic acid (HEX) being the most potent, with an EC50 value of 0.21 ± 0.02 µM, almost 1500-fold lower than the concentration required to impact human cells. Unbiased metabolomics indicates that glycolytic intermediates upstream of NfENO accumulate in HEX treated amoebae. In an effort to genetically validate new targets for therapeutic intervention, we have initiated efforts to develop molecular tools for use in N. fowleri. We have designed a vector for transient transfection of the amoebae that harbors portions of the 5’UTR of actin 1 (NF0111190) upstream of both eYFP and a hygromycin resistance gene, termed pJMJM1. We have tested a variety of approaches used in other parasite systems for plasmid delivery including the transfection reagent SuperFect, Amaxa Nucleofector technologies, and various electroporation settings. Transfection of N. fowleri flagellates with 5 µg pJMJM1 by electroporation (100 V, 500 µF, 400 Ω) yielded a population of fluorescent cells seven days after being treated with 300 µg/mL hygromycin, but this expression of eYFP was lost over time. More recently, we have used CRISPR/Cas9- mediated gene editing to successfully introduce an eYFP repair template into a predicted protein locus. While fluorescent cells were not noted in the culture, editing was confirmed by PCR analysis. Development of these molecular techniques will provide an important tool for uncovering potential target genes and allow for a better understanding of amoeba biology

E-Learning and Digital Education in the Twenty-First Century

E-learning and digital education approaches are evolving and changing the landscape of teaching and learning at all levels of education throughout the world. Innovation of emerging learning technologies is assisting e-learning and digital education to meet the needs of the 21st century. Due to the digital transformation of everyday practice, the process of learning and education has become more self-paced and accessible at any time from anywhere. The new generations of digital natives are growing up with a set of skills through their engagement with the digital world. In this context, this book includes a collection of chapters to facilitate continuous improvements including flexibility and accessibility in e-learning and digital education by exploring the challenges and opportunities of innovative approaches through the lenses of current theories, policies, and practices

The Apprenticeship Structure and the Applied Pedagogical Methods Of the Holy Roman Empire Imperial Trumpeters’ Guild During The 17\u3csup\u3eth\u3c/sup\u3e and 18\u3csup\u3eth\u3c/sup\u3e Centuries

The Imperial Trumpeters’ Guild of the 17th and 18th centuries Holy Roman Empire implemented a remarkable and effective training program for its apprentices: an expedited two-year apprenticeship structure with a seven-year journeyman period when the training was applied. Studying the structure of the apprenticeship and how the expedited system was implemented gives a new insight into an effective and efficient teaching model. This training regimen was made possible because apprentices received the necessary musical foundation and pedagogical methods through attending the Latin school. This is evidenced through analysis and comparison of the Latin school primers (textbooks) and the trumpet treatises, which contextualize the structure and use of the treatises

Enhancing Molecular Docking with Deep Q-Networks

El descubrimiento de fármacos es un proceso largo y costoso que suele durar entre 10 y 15 años, desde la evaluación inicial de candidatos farmacológicos hasta la aprobación final por parte de los organismos reguladores correspondientes. Por este motivo, simulaciones moleculares por computador, conocidas como Virtual Screening (VS) (o Cribado Virtual), se utilizan a menudo para predecir los candidatos a fármacos durante las primeras etapas de su desarrollo. Uno de los métodos más utilizados en el VS es el llamado Docking Molecular, o simplemente abreviado como Docking (en español, Acoplamiento Molecular). El objetivo de este método es resolver el problema de las Interacciones Proteína-Ligando (PLDP) o Docking. Dicho de otro modo, se trata de predecir las conformaciones 3D en las que un candidato farmacológico (también conocido como ligando) se acopla a un receptor determinado (normalmente una proteína) en un punto concreto de su superficie. Los métodos tradicionales de Docking se basan en procedimientos de optimización de funciones de puntuación (o de scoring) siguiendo determinadas heurísticas. Se trata de funciones matemáticas que modelan las interacciones moleculares. Estos métodos se caracterizan por ser computacionalmente costosos. De esta manera, en esta tesis se pretende aprovechar los prometedores algoritmos de Deep RL para mejorar la resolución del problema de Docking. Para ello, el hilo conductor de esta tesis doctoral son las diferentes alternativas de representación de las moléculas de la escena de Docking que serán utilizadas como datos de entrada de dichos algoritmos. En consecuencia, primero se replantea el problema PLDP como uno de Aprendizaje por Refuerzo (RL). Acto seguido, se construye un sistema básico basado en el algoritmo de Deep Q-Network (DQN), originalmente diseñado para enseñar a agentes artificiales a jugar a videojuegos de la consola de Atari 2600. En segundo lugar, se utiliza una implementación, denominada QN-Docking, basada en un vector de características sencillo para la representación molecular. Dicha implementación es testada en un entorno con un receptor relativamente pequeño y un espacio de acciones limitado. Los resultados de la fase de predicción muestran que QN-Docking consigue un aumento de velocidad 8 veces mayor en comparación con métodos estocásticos como METADOCK 2. Dicho programa es un nuevo software de alto rendimiento que incluye diversas metaheurísticas para el Acoplamiento Molecular. Por último, una implementación alternativa basada en imágenes, MVDQN, es testada en el mismo escenario que QN-Docking. Los resultados muestran un rendimiento similar al de la primer implementación durante la fase de entrenamiento. Sin embargo, en la fase de predicción los resultados son mixtos. El agente actúa de forma subóptima en varias de las posiciones de partida establecidas en el experimento. Este escenario final parece prometedor, no obstante, ya que hay mucho margen de mejora para seguir puliendo el algoritmo y mejorar la representación molecular. En resumen, estos resultados suponen un valioso hito en el desarrollo de un método basado en Inteligencia Artificial más rápido y efectivo para resolver el problema PLDP en comparación con métodos más tradicionales.Ingeniería, Industria y Construcció

Educating Semiosis: Exploring ecological meaning through pedagogy

This thesis consists of six essays – framed by introduction and conclusion chapters – that develop possibilities for philosophy of education and pedagogy from the lens of bio-semiotics and edu-semiotics (biological and educational semiotics). These transdisciplinary inquiries have found commonality in the concept of learning-as-semiosis, or meaning-making across nature/culture bifurcations. Here, quite distinct branches of research intersect with the American scientist-philosopher Charles Sanders Peirce’s (1839 - 1914) pragmatic semiotics. I argue in these essays that the research pathway suggested by the convergence of edu- and bio-semiotics, reveals possibilities for developing a (non-reductive) theory of learning (and pedagogy generally) that puts meaning-making processes in a central light. A fully semiotic theory of learning implores us to take an ecological and biological view of educational processes. These processes explore the complementarity of organism-environment relations and the relationship between learning and biological adaptation. They also unravel new implications for education through the basic recognition that meaning is implicitly ecological. Understanding semiotic philosophy as an educational foundation allows us to take a broader and less dichotomized view of educational dynamics, such as: learning and teaching, curriculum design, arts and music education, inter/trans-disciplinary education, literacy (including environmental and digital literacy), as well as exploring the relationships and continuities between indigenous/place-based and formal pedagogical processes and practices. From this meaning-based and ecological perspective, what is important in the educational encounter is not psychologic explanations of learning stages, predetermined competencies, or top-down implemented learning-outcomes, but rather meaning and significance and how this changes through time-space and with others (not only human others) in a dynamic and changing environment. As addressed more directly in the conclusion chapter, these essays unravel the implications of this emerging approach to the philosophy of education, pedagogy and learning theory, specifically by providing conceptual/philosophical possibilities for integrating arts education, science education, and indigenous place-based knowledge into holistic educational approaches and programs

Responsible Sourcing of Materials Required for a Resource Efficient and Low-carbon Society

Understanding future supply and demand of raw materials and the associated environmental and social implications is essential to supporting the transition towards greenhouse gas neutrality by 2050. In this Special Issue, we present a range of research papers with a focus on future outlooks of material supply and use, the consideration of associated environmental and social implications, and issues of raw material criticality and a circular economy. These are complemented by an editorial paper that provides, amongst other aspects, an overview of the corresponding policy and institutional framework. Knowledge of materials availability, their use patterns in modern economies, and associated environmental and social trade-offs is essential for informed decision-making in support of the necessary transition towards more resource-efficient and greenhouse-gas-neutral societies in the coming years