Temporal Emotion Dynamics in Social Networks

[ES] El análisis de sentimientos en redes sociales se ha estudiado ampliamente durante la última década. A pesar de ello, las distintas categorías de sentimientos no se consideran adecuadamente en muchos casos, y el estudio de patrones de difusión de las emociones es limitado. Por lo tanto, comprender la importancia de emociones específicas será más beneficioso para diversas actividades de marketing, toma de decisiones empresariales y campañas políticas. Esta tesis doctoral se centra en el diseño de un marco teórico para analizar el amplio espectro de sentimientos y explicar cómo se propagan las emociones utilizando conceptos de redes temporales y multicapa. Particularmente, nuestro objetivo es proporcionar información sobre el modelado de la influencia de las emociones y como esta afecta a los problemas de estimación de las emociones y a la naturaleza dinámica temporal en la conversación social. Para mostrar la eficacia del modelo propuesto, se han recopilado publicaciones relacionadas con diferentes eventos de Twitter y hemos construido una estructura de red temporal sobre la conversación. En primer lugar, realizamos un análisis de sentimientos adoptando un enfoque basado en el léxico y en el modelo circunflejo de emociones de Russell que mejora la efectividad de la caracterización del sentimiento. A partir de este análisis investigamos la dinámica social de las emociones presente en las opiniones de los usuarios analizando diferentes características de influencia social. A continuación, diseñamos un modelo estocástico temporal basado en emociones para investigar el patrón de participación de los usuarios y predecir las emociones significativas. Nuestra contribución final es el desarrollo de un modelo de influencia secuencial basado en emociones mediante la utilización de redes neuronales recurrentes que permiten predecir emociones de una manera más completa. Finalmente, el documento presenta algunas conclusiones y también describe las direcciones de investigación futuras.[CA] L'anàlisi de sentiments en xarxes socials s'ha estudiat àmpliament durant l'última dècada. Malgrat això, les diferents categories de sentiments no es consideren adequadament en molts casos, i l'estudi de patrons de difusió de les emocions és limitat. Per tant, comprendre la importància d'emocions específiques serà més beneficiós per a diverses activitats de màrqueting, presa de decisions empresarials i campanyes polítiques. Aquesta tesi doctoral se centra en el disseny d'un marc teòric per a analitzar l'ampli espectre de sentiments i explicar com es propaguen les emocions utilitzant conceptes de xarxes temporals i multicapa. Particularment, el nostre objectiu és proporcionar informació sobre el modelatge de la influència de les emocions i com aquesta afecta als problemes d'estimació de les emocions i a la naturalesa dinàmica temporal en la conversa social. Per a mostrar l'eficàcia del model proposat, s'han recopilat publicacions relacionades amb diferents esdeveniments de Twitter i hem construït una estructura de xarxa temporal sobre la conversa. En primer lloc, realitzem una anàlisi de sentiments adoptant un enfocament basat en el lèxic i en el model circumflex d'emocions de Russell que millora l'efectivitat de la caracterització del sentiment. A partir d'aquesta anàlisi investiguem la dinàmica social de les emocions present en les opinions dels usuaris analitzant diferents característiques d'influència social. A continuació, dissenyem un model estocàstic temporal basat en emocions per a investigar el patró de participació dels usuaris i predir les emocions significatives. La nostra contribució final és el desenvolupament d'un model d'influència seqüencial basat en emocions mitjançant la utilització de xarxes neuronals recurrents que permeten predir emocions d'una manera més completa. Finalment, el document presenta algunes conclusions i també descriu les direccions d'investigació futures.[EN] Sentiment analysis in social networks has been widely analysed over the last decade. Despite the amount of research done in sentiment analysis in social networks, the distinct categories are not appropriately considered in many cases, and the study of dissemination patterns of emotions is limited. Therefore, understanding the significance of specific emotions will be more beneficial for various marketing activities, policy-making decisions and political campaigns. The current PhD thesis focuses on designing a theoretical framework for analyzing the broad spectrum of sentiments and explain how emotions are propagated using concepts from temporal and multilayer networks. More precisely, our goal is to provide insights into emotion influence modelling that solves emotion estimation problems and its temporal dynamics nature on social conversation. To exhibit the efficacy of the proposed model, we have collected posts related to different events from Twitter and build a temporal network structure over the conversation. Firstly, we perform sentiment analysis with the adaptation of a lexicon-based approach and the circumplex model of affect that enhances the effectiveness of the sentiment characterization. Subsequently, we investigate the social dynamics of emotion present in users' opinions by analyzing different social influential characteristics. Next, we design a temporal emotion-based stochastic model in order to investigate the engagement pattern and predict the significant emotions. Our ultimate contribution is the development of a sequential emotion-based influence model with the advancement of recurrent neural networks. It offers to predict emotions in a more comprehensive manner. Finally, the document presents some conclusions and also outlines future research directions.Naskar, D. (2022). Temporal Emotion Dynamics in Social Networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/180997TESI

Molecular display of synthetic oligonucleotide libraries and their analysis with high throughput DNA sequencing

Thesis (Ph. D. in Biomedical Engineering)--Harvard-MIT Program in Health Sciences and Technology, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 142-151).High throughput methods in molecular biology have changed the landscape of biomedical research. In particular, advances in massively parallel DNA sequencing and synthesis technologies are defining our genomes and the products they encode. In the first part of this thesis, we have constructed a rationally designed antibody library and analysis platform optimized for use with deep sequencing technologies. Libraries of fully defined oligonucleotides encode three complementarity determining regions (CDRs; L3 from the light chain, H2 and H3 from the heavy chain), and were combinatorially cloned into a synthetic single chain variable fragment (scFv) framework for molecular display. Our novel CDR sequence design utilized a hidden Markov model (HMM) that was trained on all antibody-antigen co-crystal complexes present in the Protein Data Bank. The resultant ~10¹² member library has been produced in ribosome display format, and was comprehensively analyzed over four rounds of antigen selections by multiplex paired-end Illumina sequencing. The HMM library generated multiple antibodies against an emerging cancer antigen and is the basis of a next generation antibody production platform. In a second application of these technologies, we have created a synthetic representation of the complete human proteome, which has been engineered for display on bacteriophage. We use this library together with deep DNA sequencing methods to profile the autoantibody repertoires of individuals with autoimmune disease in a procedure called phage immunoprecipitation sequencing (PhIP-Seq). In a proof-of-concept study, this method identified both known and novel autoantibodies contained in the spinal fluid of a control patient with paraneoplastic neurological syndrome. The study was then expanded to include a large scale automated screen of 289 independent antibody repertoires, including those from a large number of healthy donors, multiple sclerosis patients, rheumatoid arthritis patients, and type 1 diabetics. Our data describes each individual's unique "autoantibodyome", and defines a small set of recurrently targeted peptides in health and disease.by Harry Benjamin Larman.Ph.D.in Biomedical Engineerin

The role of PNPLA3 in the development and progression of chronic liver injury

Chronic liver disease is now of great international concern due to rapidly increasing morbidity and mortality associated with the disease. There is significant evidence that carriage of the patatin-like phospholipase domain containing protein 3 (PNPLA3) risk allele, rs738409:G, plays a key role in determining risk for the development of chronic liver disease from a variety of causes. rs738409 is a common single nucleotide polymorphism which results in substitution of an isoleucine residue for methionine at position 148 of PNPLA3 (Ile148Met; I148M). However, the physiological role of PNPLA3 and the functionality of its I148M variant, are currently largely unknown. The central aim of this thesis was to investigate the biological function of PNPLA3 and elucidate the complex role that the I148M variant plays in the development and progression of liver disease. Investigation into the primary sequence of PNPLA3 was undertaken to characterise the protein and inform latter experimental design. Phylogenetic investigation revealed human PNPLA5 to share the highest homology with PNPLA3, and revealed more distant, previously undescribed relationships with the bacterial protein ExoU. A combination of expression trials and subsequent in vitro investigation into the behaviour of PNPLA3 was attempted. Despite attempts with numerous constructs, PNPLA3 remained unstable when expressed using an E. coli expression system and was not able to be produced in sufficient quantity to facilitate structural analysis. In the latter half of the thesis, both variants of PNPLA3 are investigated through in silico structural modelling and subsequent molecular dynamic simulation. The first simulation of full-length PNPLA3 is reported, revealing a more detailed description of the domain architecture of PNPLA3 and the local impact of the I148M variation. A novel disease mechanism is proposed, in which methionine at residue 148 effects the conformational stability of the PNPLA3 active site, resulting in a loss of lipase activity

Bioinformatic analysis of bacterial and eukaryotic amino- terminal signal peptides

Ph.DDOCTOR OF PHILOSOPH

Proceedings of the 36th International Workshop Statistical Modelling July 18-22, 2022 - Trieste, Italy

The 36th International Workshop on Statistical Modelling (IWSM) is the first one held in presence after a two year hiatus due to the COVID-19 pandemic. This edition was quite lively, with 60 oral presentations and 53 posters, covering a vast variety of topics. As usual, the extended abstracts of the papers are collected in the IWSM proceedings, but unlike the previous workshops, this year the proceedings will be not printed on paper, but it is only online. The workshop proudly maintains its almost unique feature of scheduling one plenary session for the whole week. This choice has always contributed to the stimulating atmosphere of the conference, combined with its informal character, encouraging the exchange of ideas and cross-fertilization among different areas as a distinguished tradition of the workshop, student participation has been strongly encouraged. This IWSM edition is particularly successful in this respect, as testified by the large number of students included in the program

Grand Celebration: 10th Anniversary of the Human Genome Project

In 1990, scientists began working together on one of the largest biological research projects ever proposed. The project proposed to sequence the three billion nucleotides in the human genome. The Human Genome Project took 13 years and was completed in April 2003, at a cost of approximately three billion dollars. It was a major scientific achievement that forever changed the understanding of our own nature. The sequencing of the human genome was in many ways a triumph for technology as much as it was for science. From the Human Genome Project, powerful technologies have been developed (e.g., microarrays and next generation sequencing) and new branches of science have emerged (e.g., functional genomics and pharmacogenomics), paving new ways for advancing genomic research and medical applications of genomics in the 21st century. The investigations have provided new tests and drug targets, as well as insights into the basis of human development and diagnosis/treatment of cancer and several mysterious humans diseases. This genomic revolution is prompting a new era in medicine, which brings both challenges and opportunities. Parallel to the promising advances over the last decade, the study of the human genome has also revealed how complicated human biology is, and how much remains to be understood. The legacy of the understanding of our genome has just begun. To celebrate the 10th anniversary of the essential completion of the Human Genome Project, in April 2013 Genes launched this Special Issue, which highlights the recent scientific breakthroughs in human genomics, with a collection of papers written by authors who are leading experts in the field