La segmentación automática de lesiones de esclerosis múltiple en imágenes de resonancia magnética es una tarea fundamental para el diagnóstico, el monitoreo de la enfermedad y la evaluación de tratamientos. No obstante, la presencia del efecto del volumen parcial, el solapamiento de intensidades entre tejidos y el alto desbalance de clases, dificultan la segmentación de las lesiones de EM. En esta tesis se propone una nueva función de pérdida, denominada Mahalanobis Distance Loss (MDL), basada en el Mapa de Distancias de Mahalanobis (MDM), también propuesto, que integra información espacial y textural mediante características radiómicas extraídas de la modalidad FLAIR. A diferencia de los mapas de distancia construidos con la distancia euclidiana, el MDM incorpora dependencias estadísticas entre características, capturando mejor las variaciones sutiles en regiones ambiguas cercanas a los bordes de las lesiones. La MDL es combinada con la Generalized Dice Loss mediante un parámetro $\epsilon$ que regula el equilibrio entre solapamiento global y precisión en los bordes. La evaluación en los conjuntos de datos públicos ISBI-MS y MSSEG2016, utilizando una U-Net, demuestra que la función de pérdida propuesta supera a las basadas en mapas de distancia euclidiana, como Boundary Loss y Hausdorff Loss, en métricas de solapamiento (Dice, precisión), de borde (HD95, ASSD) y de detección bajo desbalance de clases (AUC-PR), además de reducir los falsos positivos. Los resultados validan que incorporar información de textura en la función de pérdida mediante el MDM mejora la segmentación automática de lesiones de EM y ofrece un marco prometedor para generalizar estas ideas a otros tipos de lesiones, tejidos y órganos.

La nanomedicina ha hecho aportes significativos para la salud de las personas, desarrollando implantes, terapias, medicamentos y reparación de tejidos, entre otros. Para diseñar materiales implantables que imiten las funciones biológicas requeridas, es importante comprender la respuesta del cuerpo humano a estos materiales. Por lo tanto, es fundamental comprender la interacción entre nanomateriales y biomoléculas como los aminoácidos, unidad principal de las proteínas. En este trabajo estudiamos experimentalmente la interacción de nanomateriales a base de carbono: grafeno y óxido de grafeno, con distintos tipos de triptófano. La síntesis de nanomateriales fue llevada a cabo mediante la técnica de PECVD (Plasma Enhanced Chemical Vapor Deposition) y, utilizando una técnica libre de polímeros, transferimos nanomateriales de áreas grandes (~2,8 cm²) a distintos tipos de sustratos. Estudiamos la estructura de los nanomateriales y de los distintos tipos de triptófano mediante espectroscopía Raman y microscopía óptica, y analizamos la naturaleza de su interacción mediante mediciones de absorbancia ultravioleta. Los resultados muestran que, en los casos en que el triptófano se adsorbe en el nanomaterial, el cambio de concentración no depende de la concentración inicial de la solución de triptófano ni del área del nanomaterial. A partir de mediciones AFM (Atomic Force Microscopy) inferimos que el triptófano se adsorbe en la superficie del nanomaterial en aglomeraciones, sugiriendo que las isotermas de adsorción obtenidas en cada experimento podrían ser ajustadas con modelos de adsorción en multicapas como el modelo BET (Brunauer-Emmett-Teller). En esta tesis también presentamos el trabajo realizado durante una pasantía en el Institut National de l'Énergie Solaire (Francia). A través de la técnica de elipsometría espectroscópica, estudiamos los índices ópticos del grafeno, obteniendo valores menores a los reportados en la literatura.

The present study focuses on the modification of primary amine–functionalized drugs, namely pyrimethamine and 4-amino-N-(2,3-dihydrothiazol-2-yl)benzenesulfonamide, through a condensation reaction with 2-hydroxy-1-naphthaldehyde in methanol, yielding two new organic zwitterionic compounds: (E)-1-(((4-(N-(2,3-dihydrothiazol-2-yl)sulfamoyl)phenyl)iminio)methyl)naphthalen-2-olate (DSPIN) and (E)-1-(((4-amino-5-(4-chlorophenyl)-6-ethylpyrimidin-2-yl)iminio)methyl)naphthalen-2-olate (ACPIN). The crystal structures of both compounds were confirmed as imine-based zwitterionic products by single-crystal X-ray diffraction (SC-XRD), revealing stabilization through various noncovalent interactions. Supramolecular assembly was investigated using Hirshfeld surface analysis, while void analysis was employed to predict the mechanical response of the crystals. Density Functional Theory (DFT) calculations showed good agreement with experimental structural parameters. Frontier molecular orbital (FMO) analysis indicated that the HOMO–LUMO gap of DSPIN is 0.15 eV smaller than that of ACPIN, attributed to HOMO destabilization and LUMO stabilization in DSPIN. Charge distribution analysis suggested the presence of intramolecular hydrogen bonding, as well as intermolecular hydrogen bonds, dipole–dipole, and dispersion interactions.

Temporal (race) computing schemes rely on temporal memories, where information is represented with the timing of signal edges. Standard digital circuit techniques can be used to capture the relative timing characteristics of signal edges. However, the properties of emerging device technologies could be particularly exploited for more efficient circuit implementations. Specifically, the collective dynamics of networks of memristive devices could be leveraged to facilitate time-domain computations in emerging memristive memories. To this end, this work studies the star interconnect configuration of bipolar memristive devices. Through circuit simulations using a behavioral model of voltage-controlled bipolar memristive devices, we demonstrated the suitability of such circuits in two different contexts, namely sensing and “rank-order” coding. We particularly analyzed the conditions that the employed memristive devices should meet to guarantee the expected operation of the circuit and the possible effects of device variability in the storage and the reproduction of the information in arriving signal edges. The simulation results in LTSpice validate the correct operation and confirm the promising application prospects of such simple circuit structures, which, we show, natively exist in the crossbar geometry. Therefore, the star interconnect configuration could be considered for temporal computations inside resistive memory (ReRAM) arrays.

En esta tesis se realiza el desarrollo de un modelo de Reinforcement Learning para optimización secuencial de portafolios, incorporando el riesgo mediante el Expected Shortfall. El modelo aprende políticas de decisiones sobre activos de un portafolio (comprar, vender o mantener) usando información de retornos de distintos tipos de activos (acciones, ETFs y criptomonedas) para distintas líneas temporales, junto con un umbral de decisión compuesta que se adapta de forma dinámica según la volatilidad de los retornos. Basándose en prueba y error, obteniendo como recompensa una métrica de retorno ajustado por riesgo, definida por el retorno marginal obtenido de un conjunto de movimientos respecto a una inversión libre de riesgo, ajustada por una métrica de riesgo explícita.El objetivo principal de la tesis es evaluar si un enfoque de Reinforcement Learning logra detectar patrones dinámicos de mercado y lograr generalizarlos para escenarios fuera de muestra. Comparándolo con otros modelos tanto financieros estadísticos, como modelos de machine learning supervisados y no supervisados. Los resultados muestran que, para enfoques más conservadores sobre el umbral, el modelo de Reinforcement Learning tiene un menor gap entre entrenamiento validación y testeo, siendo sus resultados más permanentes entre fases. Los enfoques más de rentabilidad pura, poseen mayor volatilidad entre fases de validación y testeo, teniendo un mayor riesgo que sus competidores y resultados similares.

Piscirickettsia salmonis is the etiological agent of Piscirickettsiosis, one of the most severe and detrimental diseases affecting the Chilean salmon farming industry. Currently, there are 26 vaccines with provisional registration available for prophylactic use in salmonids. However, none have proven effective in fully controlling epidemic outbreaks under field conditions. Several researchers have identified two predominant genogroups along the Chilean coast, designated as LF-89 and EM-90, which exhibit a variable spatiotemporal distribution in southern Chilean salmon farms in recent years. Recent evidence suggests that the biological mechanisms of both genogroups may interact synergistically during infection, indicating the potential for co-infections in contemporary farms. This scenario further complicates vaccine efficacy, as many are designed to target only a predominant genogroup. Thus, the need arises to identify alternative prophylactic methods to ensure and contribute to the sustainability of the Chilean salmon farming industry. A novel prophylactic approach proposed in this thesis involves the use of recombinant nanoproteins, also known as inclusion bodies, with antigenic functions against Piscirickettsiosis. These inclusion bodies are aggregates of soluble and insoluble proteins with amyloid characteristics, eliminating the need for encapsulation. They are scalable, lyophilizable, and highly stable under adverse temperature and pH conditions. This thesis aims to evaluate the ability of certain antigenic protein inclusion bodies from P. salmonis to induce an adaptive immune response in Atlantic salmon (Salmo salar) and their potential use as immunogens in a vaccine prototype. To achieve this objective, three antigenic sequences with chimeric characteristics from the LF-89 and EM-90 genogroups of P. salmonis were designed and produced as recombinant inclusion bodies in E. coli BL21(DE3). The prototypes were designated for commercial protection purposes as SkipZ, PulseJ, and HopQ. These prototypes were quantified and characterized using techniques such as Western blot. Their uptake and immunostimulatory activity were evaluated in salmon macrophages through in vitro assays. Subsequently, juvenile salmon were vaccinated, and the effects of the SkipZ and HopQ prototypes were analyzed in terms of gene expression (qPCR) and immunoglobulin production (ELISA). The three prototypes achieved production efficiencies of 57 mg/L for PulseJ, 40 mg/L for SkipZ, and 26,8 mg/L for HopQ. These prototypes were internalized by salmon macrophages (RTS-11) with uptake efficiencies of 5% for SkipZ, 26% for PulseJ, and 54% for HopQ. In vitro assays showed activation at both transcriptional and translational levels of molecules associated with antigen presentation and pro-inflammatory markers, with notable activation capacities observed for HopQ and SkipZ at 20 µg/mL and 5 µg/mL, respectively. In vivo assays revealed that both vaccine prototypes, administered at doses of 0.5 mg/kg of fish for SkipZ and 2 mg/kg of fish for HopQ, robustly activated the Th1 response, with a marked increase in IFN-γ and IL-12 expression. This strong upregulation might suppress CD8+ lymphocyte differentiation, potentially reducing the effectiveness of the cellular immune response in fully eliminating the pathogen. However, this effect may contribute to maintaining homeostasis in fish during the exacerbated inflammation induced by the vaccines. Moreover, no significant change was observed in the Th2 response, suggesting that the humoral response was not predominantly activated. In conclusion, the vaccine prototypes based on antigenic sequences of Piscirickettsia salmonis and produced as chimeric inclusion bodies demonstrated potent immunogenicity, capable of inducing a robust adaptive immune response in Atlantic salmon. The activation of the Th1 response by HopQ and SkipZ was particularly noteworthy. However, further studies are needed to evaluate their protective effects in challenge trials with the pathogen, including both genogroups, and to determine their long-term effectiveness.

This study introduces a novel measure for evaluating attribute relevance, specifically designed to accurately identify attributes that are intrinsically related to a phenomenon, while being sensitive to the asymmetry of those relationships and noise conditions. Traditional variable selection techniques, such as filter and wrapper methods, often fall short in capturing these complexities. Our methodology, grounded in decision trees but extendable to other machine learning models, was rigorously evaluated across various data scenarios. The results demonstrate that our measure effectively distinguishes relevant from irrelevant attributes and highlights how relevance is influenced by noise, providing a more nuanced understanding compared to established methods such as Pearson, Spearman, Kendall, MIC, MAS, MEV, GMIC, and 𝑃ℎ𝑖𝑘 This research underscores the importance of phenomenon-centric explainability, reproducibility, and robust attribute relevance evaluation in the development of predictive models. By enhancing both the interpretability and contextual accuracy of models, our approach not only supports more informed decision making but also contributes to a deeper understanding of the underlying mechanisms in diverse application domains, such as biomedical research, financial modeling, astronomy, and others.

A critical element in understanding voice production mechanisms is the characterization of vocal fold collision, which is widely considered a primary etiological factor in the development of common phonotraumatic lesions such as nodules and polyps. This paper describes the development of a transoral, dual-sensor intraglottal/subglottal pressure probe for the simultaneous measurement of vocal fold collision and subglottal pressures during phonation using two miniature sensors positioned 7.6 mm apart at the distal end of a rigid cannula. Proof-of-concept testing was performed using excised whole-mount and hemilarynx human tissue aerodynamically driven into self-sustained oscillation, with systematic variation of the superior–inferior positioning of the vocal fold collision sensor. In the hemilarynx experiment, signals from the pressure sensors were synchronized with an acoustic microphone, a tracheal-surface accelerometer, and two high-speed video cameras recording at 4000 frames per second for top–down and en face imaging of the superior and medial vocal fold surfaces, respectively. As expected, the intraglottal pressure signal exhibited an impulse-like peak when vocal fold contact occurred, followed by a broader peak associated with intraglottal pressure build-up during the de-contacting phase. As subglottal pressure was increased, the peak amplitude of the collision pressure increased and typically reached a value below that of the average subglottal pressure. Results provide important baseline vocal fold collision pressure data with which computational models of voice production can be developed and in vivo measurements can be referenced.