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Genome editing tool for studying <i>Ciona robusta</i> nervous system differentiation
The development of the central nervous system (CNS) depends on complex gene regulatory networks (GRN) that orchestrate the specification, patterning and differentiation of neural cell types. Taking advantage of the unique characteristics of a simple chordate, the ascidian Ciona robusta, I identified a group of genes, expressed in the nervous system of Ciona, to investigate the network that controls specification inside the central and peripheral nervous system. The approach used to study functionally these genes has been the gene editing, performed by CRISPR/cas9 technique, indeed in this work I had the opportunity to evaluate the whole process of this emerging technique, preparing and testing several sgRNAs on the selected genes. Moreover, I focused my attention on a type of sensory neuron, belonging to the peripheral nervous system, Bipolar tail neurons (BTNs), through the investigation of two poorly studied genes, Rimbp and LZTS, both expressed in the BTNs neurons. This thesis amplified the knowledge on their involvement in the gene regulatory network of BTNs during Ciona nervous system development. Here I showed that CRISPR/Cas9-mediated knockout of LZTS in the epidermis results in extra BTNs, suggesting LZTS functions as a repressor during differentiation and specification of BTNs. All these data provide new insight into the development of the Ciona nervous system, encouraging further studies to clarify and confirm LZTS role in the Ciona nervous system development
Pediatric and Adolescent Nephrology Facing the Future: Diagnostic Advances and Prognostic Biomarkers in Everyday Practice
The Special Issue entitled “Pediatric and adolescent nephrology facing the future: diagnostic advances and prognostic biomarkers in everyday practice” contains articles written in the era when COVID-19 had not yet been a major clinical problem in children. Now that we know its multifaceted clinical course, complications concerning the kidneys, and childhood-specific post-COVID pediatric inflammatory multisystem syndrome (PIMS), the value of diagnostic and prognostic biomarkers in the pediatric area should be appreciated, and their importance ought to increase
Developing A Physics-informed Deep Learning Paradigm for Traffic State Estimation
The traffic delay due to congestion cost the U.S. economy $ 81 billion in 2022, and on average, each worker lost 97 hours each year during commute due to longer wait time. Traffic management and control strategies that serve as a potent solution to the congestion problem require accurate information on prevailing traffic conditions. However, due to the cost of sensor installation and maintenance, associated sensor noise, and outages, the key traffic metrics are often observed partially, making the task of estimating traffic states (TSE) critical. The challenge of TSE lies in the sparsity of observed traffic data and the noise present in the measurements. The central research premise of this dissertation is whether and how the fundamental principles of traffic flow theory could be harnessed to augment machine learning in estimating traffic conditions. This dissertation develops a physics-informed deep learning (PIDL) paradigm for traffic state estimation. The developed PIDL framework equips a deep learning neural network with the strength of the governing physical laws of the traffic flow to better estimate traffic conditions based on partial and limited sensing measurements. First, this research develops a PIDL framework for TSE with the continuity equation Lighthill-Whitham-Richards (LWR) conservation law - a partial differential equation (PDE). The developed PIDL framework is illustrated with multiple fundamental diagrams capturing the relationship between traffic state variables. The framework is expanded to incorporate a more practical, discretized traffic flow model - the cell transmission model (CTM). Case studies are performed to validate the proposed PIDL paradigm by reconstructing the velocity and density fields using both synthetic and realistic traffic datasets, such as the next-generation simulation (NGSIM). The case studies mimic a multitude of application scenarios with pragmatic considerations such as sensor placement, coverage area, data loss, and the penetration rate of connected autonomous vehicles (CAVs). The study results indicate that the proposed PIDL approach brings exceedingly superior performance in state estimation tasks with a lower training data requirement compared to the benchmark deep learning (DL) method. Next, the dissertation continues with an investigation of the empirical evidence which points to the limitation of PIDL architectures with certain types of PDEs. It presents the challenges in training PIDL architecture by contrasting PIDL performances in learning the first-order scalar hyperbolic LWR conservation law and its second-order parabolic counterpart. The outcome indicates that PIDL experiences challenges in incorporating the hyperbolic LWR equation due to the non-smoothness of its solution. On the other hand, the PIDL architecture with the parabolic version of the PDE, augmented with the diffusion term, leads to the successful reassembly of the density field even with the shockwaves present. Thereafter, the implication of PIDL limitations for traffic state estimation and prediction is commented upon, and readers\u27 attention is directed to potential mitigation strategies. Lastly, a PIDL framework with nonlocal traffic flow physics, capturing the driver reaction to the downstream traffic conditions, is proposed. In summary, this dissertation showcases the vast capability of the developed physics-informed deep learning paradigm for traffic state estimation in terms of efficiently utilizing meager observation for precise reconstruction of the data field. Moreover, it contemplates the practical ramification of PIDL for TSE with the hyperbolic flow conservation law and explores the remedy with sampling strategies of training instances and adding the diffusion term. Ultimately, it paints the picture of potent PIDL applications in TSE with nonlocal physics and suggests future research directions in PIDL for traffic state predictions
Family Behavior and Children’s Wellbeing: Statistical Modeling and Measurement Issues
In dieser Dissertation gehe ich auf verschiedene statistische Modellierungs- und Messprobleme ein, die eine kausale Interpretation der in der Literatur zu Familiensoziologie und sozialer Ungleichheit gefundenen Zusammenhängen erschweren. Erstens legt die Lebensverlaufsforschung nahe, dass das Problem der Verzerrung durch Selektion in der Literatur über die Abwesenheit von Vätern komplexer sein könnte als angenommen. Durch die Korrektur von dynamischen Verzerrungen wird die Schätzung des kausalen Effektes der Abwesenheit des Vaters auf das Wohlergehen der Kinder reduziert. Zweitens wird angenommen, dass familiäre Instabilität in der Kindheit das Wohlbefinden der Kinder negativ beeinflusst. Allerdings könnten zeitabhängige konfundierende Faktoren, die durch vergangene Episoden familiärer Instabilität beeinflusst werden und sich auf die künftige Stabilität der Familie auswirken, einen Teil der angenommenen negativen Auswirkungen erklären. Ich zeige, dass eine dynamische Version der Selektionshypothese eine wesentliche Rolle bei der Entkräftung der Hypothese der familiären Instabilität spielt. Drittens deuten die Forschungsergebnisse darauf hin, dass die soziale Stratifizierung bei den Sprachkenntnissen von Vorschulkindern durch Eingriffe in den Erziehungsstil von Eltern mit wenig Ressourcen erheblich verringert werden könnten. Mit Hilfe einer kausalen Mediationsanalyse zeige ich, dass die elterliche Erziehung nur etwa ein Drittel des Gesamteffekts des sozioökonomischen Status auf die frühen Sprachfähigkeiten mediieren. Viertens wird die Messung kognitiver Fähigkeiten durch verschiedene Merkmale standardisierter Beurteilungen erschwert. Diese Probleme haben wichtige Auswirkungen auf die Quantifizierung sozialer Ungleichheit bei unbeobachtbaren Variablen und auf die Forschung zu kausalen Effekten. Die Dissertation schließt mit einem Plädoyer zur rigoroseren Anwendung von Methoden der kausalen Inferenz in Familiensoziologie und Forschung zu sozialer Ungleichheit.In this dissertation, I consider various statistical modeling and measurement issues that complicate the causal attributions made about those associations in the literature in family sociology and social inequality. First, life course informed research suggests that the problem of selection bias in the father absence literature may be more complex than currently thought. After adjusting for dynamic biases, estimates of father absence's effect on children's wellbeing are reduced. Second, family instability experienced during childhood is said to negatively affect children's wellbeing. However, time-dependent confounders affected by past episodes of family instability and affecting future family stability might explain away part of the negative impact. I show that a dynamic version of the selection hypothesis counters the family instability hypothesis, and the effects of cumulative family instability are small and not consistent with the family instability hypothesis. Third, research suggest that socioeconomic status gaps in language skills among preschoolers could be substantially reduced by intervening on the parenting styles, practices, and parental investments of low-resource parents. Employing interventional causal mediation analysis, however, I show parenting mediates around one third of the total effect of SES on early language skills. Fourth, the measurement of cognitive abilities is complicated by various features of standardized assessments. Those problems have important implications for the quantification of social inequality in unobservable variables and for causal inference research because test scores capture non-random noise. The dissertation concludes by making a plea for furthering causal inference thinking in family sociology, social inequality, social mobility, and family demography research
Secure Communications in Next Generation Digital Aeronautical Datalinks
As of 2022, Air Traffic Management (ATM) is gradually digitizing to automate and secure data transmission in civil aviation. New digital data links like the L-band Digital Aeronautical Communications System (LDACS) are being introduced for this purpose.
LDACS is a cellular, ground-based digital communications system for flight guidance and safety. Unfortunately, LDACS and many other datalinks in civil aviation lack link layer security measures.
This doctoral thesis proposes a cybersecurity architecture for LDACS, developing various security measures to protect user and control data. These include two new authentication and key establishment protocols, along with a novel approach to secure control data of resource-constrained wireless communication systems.
Evaluations demonstrate a latency increase of 570 to 620 milliseconds when securely attaching an aircraft to an LDACS cell, along with a 5% to 10% security data overhead. Also, flight trials confirm that Ground-based Augmentation System (GBAS) can be securely transmitted via LDACS with over 99% availability.
These security solutions enable future aeronautical applications like 4D-Trajectories, paving the way for a digitized and automated future of civil aviation
Scientific dissemination and professional practices through digital media: The study of pragmatic strategies in the communication of international research projects
La investigación científica hoy en día está ligada a los procesos de globalización y a la búsqueda de la innovación y la excelencia, lo cual favorece una creciente colaboración, internacionalización y multidisciplinariedad. Para llevar a cabo estas iniciativas ambiciosas y de gran escala, los investigadores necesitan la financiación externa que distintas organizaciones, instituciones y programas pueden proporcionar. Esta reconfiguración del trabajo académico va de la mano de la ubiquidad y popularidad de Internet. Un extenso abanico de géneros, plataformas y medios digitales permiten a los científicos y académicos difundir sus investigaciones a una audiencia amplia y heterogénea. La inversión de esfuerzo en la comunicación mediada digitalmente permite a los investigadores contribuir a una diseminación más efectiva del conocimiento generado, así como cumplir con su compromiso social. Por otra parte, este esfuerzo les puede permitir reforzar su reputación como investigadores y conseguir un mayor impacto. Un ejemplo destacado de este escenario académico cambiante donde se maximiza el discurso digital para propósitos investigadores es el de los proyectos de investigación internacionales. Se trata de consorcios compuestos de miembros provenientes de entornos socioculturales y profesionales distintos que hacen uso de sitios web y redes sociales para la diseminación de sus proyectos conjuntos y utilizan las características tecnológicas y comunicativas de estos espacios digitales para ofrecer actualizaciones periódicas de su trabajo e información sobre hallazgos en progreso y resultados de investigación. De este modo, rinden cuentas a los organismos que los financian y aumentan su visibilidad entre los lectores digitales. Las intenciones comunicativas de estos equipos de investigación para cumplir dichos objetivos se codifican y transmiten discursivamente a través de diversas estrategias pragmáticas, que se encuadran en determinados parámetros contextuales y que responden a las especificidades del medio y se ven constreñidas por estas. Estas estrategias revelan cómo los investigadores comparten la información, cómo publicitan sus hallazgos y cómo se dirigen a sus potenciales lectores.Así, esta tesis doctoral tiene como objetivo investigar las estrategias pragmáticas prominentes en lengua inglesa empleadas por grupos de investigación internacionales en sus prácticas digitales discursivas, que normalmente se materializan en sitios webs y redes sociales para sus proyectos. Con este propósito, se compiló y analizó el corpus digital EUROPRO, que contiene 30 sitios web de proyectos de investigación que recibieron financiación en el marco del programa Horizonte2020 (subcorpus EUROPROwebs) y las correspondientes cuentas de Twitter de aquellos proyectos (subcorpus EUROPROtweets). Dichos subcorpus han sido extraídos de la base de datos digital EUROPRO recopilada por el grupo de investigación InterGedi. En mi tesis doctoral propongo una taxonomía derivada de los datos como resultado del análisis del corpus, que comprende 27 estrategias organizadas en torno a tres macrocategorías: informativas, promocionales e interaccionales. Incido teórica y metodológicamente en el proceso de diseñar y revisar esta herramienta analítica para así demonstrar su solidez y viabilidad. Además, analizo el rango de ocurrencia, la frecuencia y el uso específico de estas estrategias en las secciones que aparecen de manera sistemática en los sitios web incluidos en el corpus y en las páginas web donde se aloja la mayor parte de la información sobre el proyecto (Homepage, About, Partners, News & Events), en las cuentas de Twitter y, de forma comparativa, entre las secciones web y los tuits, con el fin de observar tendencias significativas y en cuanto a similitudes y diferencias en su funcionamiento en estos medios digitales. Además, adopto un enfoque etnográfico mediante la inclusión de evidencias contextuales conseguidas a través de entrevistas semi-estructuradas con investigadores de los proyectos Horizonte2020, cuyos resultados ayudan a sustentar los hallazgos procedentes del análisis textual. También tomo una perspectiva multimodal sobre cómo se emplean las estrategias pragmáticas en los sitios web de proyectos de investigación en relación a la sección Homepages. Este análisis, en concreto, permite reconocer el potencial de los recursos verbales y visuales para la construcción de significado desde una perspectiva pragmática. En general, el presente estudio busca ahondar en nuestro entendimiento de prácticas académicas digitales que están evolucionando rápidamente y que tienen gran alcance, en particular adoptadas por grupos de investigación, que pueden beneficiarse de los resultados y las implicaciones de esta investigación para la futura comunicación y diseminación de sus proyectos científicos.<br /
LIPIcs, Volume 274, ESA 2023, Complete Volume
LIPIcs, Volume 274, ESA 2023, Complete Volum
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